Name: How to Attract and Retain the Next Generation of Machine Learning Talent
Start: 2021-11-17T15:05+0:00
End: 2021-11-17T15:35+0:00

Join our 5th Annual TMLS Event

november, 2021

17nov3:05 PM3:35 PMHow to Attract and Retain the Next Generation of Machine Learning TalentTammy Kim-Newman - Business Developer, Co-Operative and Experiential Education3:05 PM - 3:35 PM

Event Details

Abstract:
Are you struggling to find or retain the best talent? As the world’s largest co-op program, we’ve analyzed over 37,000 postings and want to share our tips and insights with you. You’ll walk away knowing more about the next generation of workforce talent, what skills they have, the checklist you need to make sure you’re attracting top candidates and how to retain them in your organization.

What You’ll Learn:
Attendees will walk away knowing more about the next generation of talent, how to attract them and how to retain them in your organization.

Tammy Kim-Newman is Business Developer and Tech Talent Specialist at the University of Waterloo, Canada’s #1 university for computer science, engineering and mathematics (Macleans 2021 University Rankings). She has 10+ years supporting industry partners with their early talent strategies, is an advocate for work-integrated learning, and is currently working towards a diploma in gender and social justice.

Time

(Wednesday) 3:05 PM - 3:35 PM

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Ben Goertzel

CEO, SingularityNET

Dr. Ben Goertzel is the CEO of the decentralized AI network SingularityNET, a blockchain-based AI platform company, and the Chief Science Advisor of Hanson Robotics, where for several years he led the team developing the AI software for the Sophia robot. Dr. Goertzel also serves as Chairman of the Artificial General Intelligence Society, the OpenCog Foundation, the Decentralized AI Alliance and the futurist nonprofit Humanity+. Dr. Goertzel is one of the world’s foremost experts in Artificial General Intelligence, a subfield of AI oriented toward creating thinking machines with general cognitive capability at the human level and beyond. He also has decades of expertise applying AI to practical problems in areas ranging from natural language processing and data mining to robotics, video gaming, national security and bioinformatics. He has published 20 scientific books and 140+ scientific research papers, and is the main architect and designer of the OpenCog system and associated design for human-level general intelligence. He obtained his PHD in mathematics from Temple University in 1989.

Talk: Neural-Symbolic AI for Creativity, Generalization and Transfer Learning

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Jaakko Lempinen

Head of Customer Experience, Yle

Jaakko Lempinen works as a Head of Customer Experience at Yle – Finnish public broadcaster. For the last 14 years Jaakko has been developing data and advanced analytics (ie.ML/AI) - solutions for the media industry for both commercial and public media. Jaakko is a well-known speaker on this topic and is always eager to learn more about the future of artificial intelligence & customer experience in media sector.

Talk: How Finnish Public Broadcaster Yle is the Only Streaming Service Beating Out Netflix

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Chip Huyen

Snorkel AI - Machine Learning Engineer & Open Source Lead

Chip Huyen works to bring the best practices to machine learning production. She’s built AI applications at Snorkel AI, Netflix, NVIDIA, and Primer. She graduated from Stanford, where she taught TensorFlow for Deep Learning Research. She’s also the author of four bestselling Vietnamese books.

Talk: Design, Data, Development, Deployment: Breaking Down the Machine Learning Production Pipeline

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Melanie Mitchell

Melanie Mitchell is the Davis Professor of Complexity at the Santa Fe Institute, and Professor of Computer Science (currently on leave) at Portland State University. Her current research focuses on conceptual abstraction, analogy-making, and visual recognition in artificial intelligence systems.

Melanie is the author or editor of six books and numerous scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. Her book Complexity: A Guided Tour (Oxford University Press) won the 2010 Phi Beta Kappa Science Book Award and was named by Amazon.com as one of the ten best science books of 2009. Her latest book is Artificial Intelligence: A Guide for Thinking Humans (Farrar, Straus, and Giroux).

Talk: Abstraction and Analogy in Natural and Artificial Intelligence

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Ashish Bansal

Director, Recommendations Systems, Twitch

Ashish is a Director of Recommendations at Twitch where he works on building scalable recommendation systems across a variety of product surfaces, connecting content to people. He has worked on recommendations systems at multiple organizations, most notably Twitter where he led Trends and Events recommendations and at Capital One. Ashish was also a co-founder of GALE Partners, a full-service digital agency in Toronto. He has over 20 years of experience with over a decade in building ML systems. Ashish is a guest lecturer at IIT BHU teaching Deep Learning and also writing a book on advanced NLP techniques.

Talk: Key Design Patterns for Building Recommendation Systems At Scale

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Benedikt Koller

Co-Founder / CTO – maiot

I'm a seasoned SRE/Opsguy with 11+ years experience in data-heavy companies (ecommerce, SaaS, advertising). For over two years now I'm one of two CTOs of maiot, a Munich-based AI startup. Originally focused on predictive maintenance / asset optimization of industrial assets and commercial vehicles, we're now making our internal tech stack available to a broad audience.

Talk: A Tale of A Thousand Pipelines

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Jose Antonio Murillo Garza

Chief Analytics Officer, Banorte

Jose Murillo established and leads the Analytics Business Unit at Banorte –the second largest financial group in Mexico. His group has been recognized as a success story within the data and analytics industry. The value created with analytics on 2019, the fifth year of operations of his group, was equivalent to 1 billion USD –during his tenure the value created exceeds 3 billion USD. This case was published at Harvard Business Review as an example of a company which made its analytics investments pay-off (2018) and by Forbes (2020) as an example of a firm that has developed high ROI artificial intelligence applications. In addition, the Harvard Business School has written and taught a case study on Jose’s analytics and digital transformation leadership. He obtained the Lafferty Global Award on Credit Card Excellence for the impact of analytics on customer equity (2016). Jose has been named repeatedly among the top global analytics and data professionals by Corinium. He has been published in several industry and refereed journals, he is an international speaker at data science and artificial intelligence forums, and he is a member of Queen’s University Advisory Board for the Master’s in Management Analytics.

Prior to Banorte, Jose was a top ranking official at Mexico’s Central Bank and participated for more than a decade at the Monetary Policy Committee holding the staff’s view on inflation –the key variable for policy decision. Also, he was an advisor to the International Monetary Fund and led missions to several Latin American countries. At the academia he taught economics at Rice University, ITAM and El Colegio de Mexico. He holds a PhD in economics from Rice University and a BA from ITAM (Cum Laude and national prize winner from the National Chamber of Commerce and Tlacalel).

Talk: Banorte’s AI Transformation Journey: How the Analytics Team of this Bank Yielded 3 Billion USD Revenue During the Past Five Years

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Nadia Fawaz

Applied Research Scientist, Pinterest

Nadia Fawaz is an applied research scientist at Pinterest and the tech lead for Inclusive AI. Her research and engineering interests include machine learning for personalization, AI fairness and data privacy. Her work leverages techniques from AI including deep learning, information theory, fairness and privacy theory, and aims at bridging theory and practice. She was a winner of the ACM RecSyS challenge on Context-Aware Movie Recommendations CAMRa2011, her 2012 UAI paper "Guess Who Rated This Movie: Identifying Users Through Subspace Clustering" was featured in an MIT TechReview article as “The Ultimate Challenge For Recommendation Engines”, and her work on inclusive AI was featured many press outlets, including FastCompany and Vogue Business. Earlier, she was a Staff Software Engineer in Machine Learning and the tech lead for the job recommendations team at LinkedIn, a principal research scientist at Technicolor Research lab, Palo Alto, and a postdoctoral researcher at the Massachusetts Institute of Technology, Research Laboratory of Electronics. She received her Ph.D. in EECS in 2008 and her Diplome d'ingenieur (M.Sc.) in EECS in 2005 both from Ecole Nationale Superieure des Telecommunications de Paris and EURECOM, France. She is a Member of the IEEE and of the ACM.

Talk: Inclusive Search and Recommendation

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Brandy Freitas

Senior Data Scientist, Precisely

Brandy Freitas is a senior data scientist at Precisely (formerly Pitney Bowes Software and Data), where she works with clients in a wide variety of industries to develop analytical solutions for their business needs. Brandy is a research physicist-turned-data scientist based in Boston, MA. Her academic research focused primarily on protein structure determination, applying machine learning techniques to single-particle cryoelectron microscopy data. Brandy is a National Science Foundation Graduate Research Fellow and a James Mills Pierce Fellow. She holds an undergraduate degree in physics and chemistry from the Rochester Institute of Technology and did her graduate work in biophysics at Harvard University.

Talk: Harnessing Geospatial Data for Machine Learning

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Roxana Barbu

Cognitive Data Specialist, Macadamian Technologies

Having traversed academia, healthcare and industry, and trained as a cognitive scientist, Roxana tackles research questions from various lenses striving to provide comprehensive, unique, and data-driven insights. Sought-after public speaker and facilitator, she’s counting over 30 conference presentations and speaking invitations, and 4 undergraduate courses on topics centered around human cognition and research approaches. When not in front of the computer, she enjoys the solitude of her garden, trying to recreate a bit of the magic from “the old world” where she spent summers walking across vineyards with a whistle to send away starlings, and falls picking hectares of grapes to make magic.

Talk: The Algorithm is Not Enough: UX Meets Data Science

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Scott Plewes

Chief Strategy Officer, Akendi

Over the past twenty-five years, Scott has worked in the areas of business strategy, product design and development in the high tech sector. He brings with him cross-sector expertise and experience working with clients in industries such as aviation, telecom, and finance. His primary area of focus over the last several years has been in healthcare. Scott has a master’s degree in Physics from Queen’s University. Prior to joining Macadamian in 2006, Scott co-founded Maskery and Associates, a UX design consultancy. His focus the past several years has been the improved integration of multi-discipline teams that include clinical, business, technical, design, and data expertise; and the enhancement of the tools and practices these teams apply.

Talk: The algorithm is not enough: UX meets Data Science

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Patrick Cullen

Director of Data Science, The Washington Post

Patrick is the Director of Data Science at the Washington Post. His team of data scientists and software engineers builds personalization algorithms, advertisement targeting platforms, and products at the intersection of journalism and machine learning. He also lead the development of Zeus Technology, a publisher focused suite of advertising technologies that utilize NLP technology to drive contextual ad targeting. Before joining the Washington Post, he lead engineering teams at Amazon Web Services building cloud networking solutions used by some of the largest companies in the world.

Talk: Raising the Quality of Online Conversations with Machine Learning

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Valerii Podymov

Senior Data Scientist, Cineplex

Valerii joined Cineplex last year to drive the best practice in development and serving of ML models. Previously he was a Senior Data Scientist at Real Tech, an IoT company in the Water Industry. Prior to that he has contributed to the development of innovative solutions for a variety of brands such as LG Electronics, Panasonic, SAMSUNG, Toyota, Scotiabank. Author of 20 patented inventions in Signal Processing, Electronics and Computing. He has a University Degree in Telecom Engineering and PhD in Automated Control Systems. Lifelong learner and researcher by nature.

Talk: Movie Attendance Forecasting: Machine Learning in Post-COVID Market

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Muhammad Mamdani, PharmD, MA, MPH

Vice President, Data Science and Advanced Analytics

Dr. Mamdani is Vice President of Data Science and Advanced Analytics at Unity Health Toronto. Dr. Mamdani’s team bridges advanced analytics including machine learning with clinical and management decision making to improve patient outcomes and hospital efficiency. Dr. Mamdani is also Professor in the Department of Medicine of the Faculty of Medicine, the Leslie Dan Faculty of Pharmacy, and the Institute of Health Policy, Management and Evaluation of the Dalla Lana Faculty of Public Health. Dr. Mamdani is the Director of the University of Toronto Faculty of Medicine Centre for Machine Learning in Medicine. He is also adjunct Senior Scientist at the Institute for Clinical Evaluative Sciences (ICES) and a Faculty Affiliate of the Vector Institute, which is a leading institution for artificial intelligence research in Canada. Further, Dr. Mamdani is a member of the Human Drug Advisory Panel of the Patented Medicine Prices Review Board (PMPRB). Previously, Dr. Mamdani founded the Ontario Drug Policy Research Network (ODPRN), which is among the world’s most impactful collaborations between researchers and drug policy decision-makers. He was also the Founding Director of the Li Ka Shing Centre for Healthcare Analytics Research and Training (LKS-CHART) of Unity Health Toronto and the Founding Director of the Applied Health Research Centre (AHRC) of the Li Ka Shing Knowledge Institute of Unity Health Toronto, which is Toronto’s leading academic research organization focused on the design and implementation of multicentre clinical research initiatives. In 2010, Dr. Mamdani was named among Canada’s Top 40 under 40. Prior to joining the Li Ka Shing Knowledge Institute and Unity Health Toronto, Dr. Mamdani was a Director of Outcomes Research at Pfizer Global Pharmaceuticals in New York. Dr. Mamdani’s research interests include pharmacoepidemiology, pharmacoeconomics, drug policy, and the application of advanced analytics approaches to clinical problems and health policy decision-making. He has published approximately 500 research studies in peer-reviewed medical journals, including leading journals such as the New England Journal of Medicine, the Lancet, the Journal of the American Medical Association, the British Medical Journal, and Annals of Internal Medicine. His research has been cited over 34,000 times and has an h-index of 90. Dr. Mamdani obtained a Doctor of Pharmacy degree (PharmD) from the University of Michigan (Ann Arbor) in 1995 and subsequently completed a fellowship in pharmacoeconomics and outcomes research at the Detroit Medical Center in 1997. During his fellowship, Dr. Mamdani obtained a Master of Arts degree in Economics from Wayne State University in Detroit, Michigan with a concentration in econometric theory. He then completed a Master of Public Health degree from Harvard University in 1998 with a concentration in quantitative methods, focusing on biostatistics and epidemiological principles.

Talk: Applied Machine Learning in Healthcare - Practical and Legal Considerations

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Talk: A Cookbook for Deep Continuous-Time Predictive Models

Abstract:

Do you want to make predictions based on irregularly-sampled, sparse time series? This tutorial will outline a series of approaches to this task based on neural networks. We'll start with simple feedforward approaches, and gradually build towards latent-variable stochastic differential equation models. This talk will also highlight some recent research on regularizing differential equation-based models to be more computationally efficient to solve.

What You'll Learn

You'll learn about the main existing approaches for building flexible time series models, and their strengths and weaknesses.

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Talk: Neural-Symbolic AI for Creativity, Generalization and Transfer Learning

Abstract:

Neural-Symbolic methods, combining neural ML tools with symbolic logical reasoning methods, have greater capability than current purely subsymbolic ML methods for transfer learning, generalization beyond the training space, and creative hypothesis and invention. This talk will give examples of neural-symbolic AI implemented using the OpenCog AI framework, including semantics-preserving hypergraph embeddings and probabilistic logic based explanations of ML-identified data patterns. Practical applications will be discussed, including personalized medicine, humanoid robotics and grammar learning.

What You'll Learn:

How to think about, and build, neural-symbolic AI systems that generalize, transfer knowledge and perform creative hypothesis generation.

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Talk: A Tale of A Thousand Pipelines

Abstract:

This is an Ops-guy's story about we had to build scalable deep learning pipelines for hundreds of model trainings on giant timeseries datasets. Ah, and how we saved 80% of our cost along the way.

What You'll Learn:

Real-world learnings from putting deep learning models rapidly from research to production through solid Ops and orchestration.

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Talk: Abstraction and Analogy in Natural and Artificial Intelligence

Abstract:

In 1955, John McCarthy and colleagues proposed an AI summer research project with the following aim: “An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.” More than six decades later, all of these research topics remain open and actively investigated in the AI community. While AI has made dramatic progress over the last decade in areas such as vision, natural language processing, and robotics, current AI systems still almost entirely lack the ability to form humanlike concepts and abstractions.

Some cognitive scientists have proposed that analogy-making is a central mechanism for conceptual abstraction and understanding in humans. Douglas Hofstadter called analogy-making “the core of cognition”, and Hofstadter and co-author Emmanuel Sander noted, “Without concepts there can be no thought, and without analogies there can be no concepts.” In this talk I will reflect on the role played by analogy-making at all levels of intelligence, and on how analogy-making abilities will be central in developing AI systems with humanlike intelligence.

What You'll Learn:

You'll learn how modern AI and ML are approaching the problem of conceptual abstraction and analogy-making, and how these approaches compare with human abilities in these areas.

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Talk: Key Design Patterns for Building Recommendation Systems At Scale

Abstract:

Description of key design patterns useful for building scalable recommendation systems, based on learnings from deploying several such systems in the field.

What You'll Learn:

Practical considerations in building real life recommendation systems

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Talk: How Finnish Public Broadcaster Yle is the Only Streaming Service Beating Out Netflix

Abstract:

I’ll talk about how AI will shape the future of media experience and how Yle is shaping it’s operations around this change. I’ll give few examples of how ideas are scaled into products across the whole organisation. I’ll also talk about how the culture changes within organisations as they start to benefit more from progressive data solutions – what are the future skills that every organisation should have and how to get started with the change.

What You'll Learn:

Case studies from media sector, How to drive the change in your organisation and what do you actually need to make the change.

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Talk: Design, Data, Development, Deployment: Breaking Down the Machine Learning Production Pipeline

Abstract:

Machine learning has found increasing use in the real world, and yet a framework for productionizing machine learning systems is not well understood.

This talk outlines the challenges and approaches to designing, developing, and deploying ML systems. It starts with the gap between ML in research and ML in production. It examines how ML applications differ from traditional software engineering applications, the scaling challenge, and the rise of MLOps.

The next part covers the four main stages in the iterative process of ML systems design. For each stage, it breaks down the steps needed, the tradeoffs of different solutions at each step.

The talk ends with a survey of the MLOps landscape by analyzing over 200 available tools, where they fit into the ecosystem, and what’s missing in the ecosystem.

What You'll Learn:

Attendees will gain an understanding of principles of knowledge translation in applied machine learning in healthcare and understand issues related to privacy and ethics as well as legal considerations.

Talk: The Algorithm is not Enough: UX Meets Data Science

Abstract:

What You'll Learn:

Cross-disciplinary artefacts and processes generally not applied in digital product development Human centered data science

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Ling Jiang

Senior Data Scientist,The Washington Post

Ling Jiang is a data scientist at the Washington Post. She enjoys working on data mining and knowledge discovery from large volume of data. She is skilled in various machine learning and data mining techniques, and using them to tackle business problems. At the Post, she has successfully built several data-powered products using machine learning and NLP techniques. She graduated from Drexel University with a PhD degree in Information Science before joining The Washington Post.

Talk: Raising the Quality of Online Conversations with Machine Learning

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Talk: Raising the Quality of Online Conversations with Machine Learning

Abstract:

What You'll Learn:

How to build a system that utilizes both human and machine learning moderation to efficiently scale to millions of reader comments.

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Winston Arima

Founder, Arima

Winston is the founder of Arima, a synthetic database that captures individual consumer-level behavioural and demographic attributes across Canada. Arima aims to be a full-stack solution for data scientists to easily acquire individual-level consumer intelligence, connecting those who want better data to build more robust ML models and those who have data, without compromising data privacy. Prior to founding Arima, Winston was the Director of Data Science at PwC and Omnicom Mediacom. Winston is also a part time faculty member at Northeastern University Toronto, and sits on the advisory board of the Master of Analytics program.

Talk: A Machine Learning based Privacy-Preserving Framework for Generating Synthetic Data from Aggregated Sources

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Talk: A Machine Learning based Privacy-Preserving Framework for Generating Synthetic Data from Aggregated Sources

Abstract:

A synthetic dataset is a data object that is generated programmatically, and it is often necessary for situations where data privacy is a concern, or when collecting data is difficult or costly. Although it is a fundamental step for many data science tasks, an efficient and standard framework is absent. In this presentation, we study a specific synthetic data generation task called downscaling, a procedure to infer high-resolution information (e.g., individual level records) from low-resolution variables (e.g., average of many individual records), and propose a multi-stage framework. Specifically, we discuss 1) how synthetic data is generated from aggregated sources like census, 2) why is this important from a application perspectives, and 3) two real world use cases demonstrating why using synthetic data generation can significantly improve model performances.

What You'll Learn:

I will present a novel method for generating synthetic datasets (which has not yet been published) as well as 2 real world case studies of Arima's partners on how synthetic data has improved their model performances.

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Ari Kalfayan

Senior Business Development Manager - AI/ML & VC, Amazon Web Services

Ari Kalfayan is a Senior Business Development Manager at AWS in charge of AI/ML startups. Ari began his career in the AI/ML space in 2009, where he led sales at Figure Eight (sold for $300M to Appen) which pioneered data labeling technology. Ari then joined the founding team of Weights & Biases (W&B) in 2018 which is pioneering machine learning tools for deep learning. Ari focuses on helping early-stage co-founders, who are building machine learning startups, accelerate growth and achieve product market fit. He specializes in helping early stage startups accelerate their growth by helping them connect with internal resources at AWS/Amazon, with go-to-market strategy, introductions to enterprise accounts, and connecting startups with investors.

Talk: Winning Your First 50 Enterprise Customers: Practical Strategies to Successfully Launch a Machine Learning Startup

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Talk: Winning Your First 50 Enterprise Customers: Practical Strategies to Successfully Launch a Machine Learning Startup

Abstract:

This talk is designed to help you land your first 50 enterprise machine learning customers. Whether you are developing your first machine learning application, creating an enterprise ML infrastructure startup, or creating new Machine/Deep Learning tools, this hands-on session is designed to share practical strategies, growth hacks, and specific techniques to use that will win you your first customers and scale.

This presentation will be broken up into three parts:

- Landing your first customer (0-1 customer)

- Validating your business model (1-10 customers)

- Scaling your business (10-50 customers)

This presentation is designed to leave you with practical tips to help you acquire new customers⁠, no matter your funding stage.

What You'll Learn:

Practical advise and mistakes from having launched two top tier ML tools companies

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Sujith Ravi

Director, Amazon Alexa AI

Dr. Sujith Ravi is a Director at Amazon Alexa AI where he is leading efforts to build the future of multimodal conversational AI experiences at scale. Prior to that, he was leading and managing multiple ML and NLP teams and efforts in Google AI. He founded and headed Google’s large-scale graph-based semi-supervised learning platform, deep learning platform for structured and unstructured data as well as on-device machine learning efforts for products used by billions of people in Search, Ads, Assistant, Gmail, Photos, Android, Cloud and YouTube. These technologies power conversational AI (e.g., Smart Reply), Web and Image Search; On-Device predictions in Android and Assistant; and ML platforms like Neural Structured Learning in TensorFlow, Learn2Compress as Google Cloud service, TensorFlow Lite for edge devices.

Dr. Ravi has authored over 100 scientific publications and patents in top-tier machine learning and natural language processing conferences. His work has been featured in press: Wired, Forbes, Forrester, New York Times, TechCrunch, VentureBeat, Engadget, New Scientist, among others, and also won the SIGDIAL Best Paper Award in 2019 and ACM SIGKDD Best Research Paper Award in 2014. For multiple years, he was a mentor for Google Launchpad startups. Dr. Ravi was the Co-Chair (AI and deep learning) for the 2019 National Academy of Engineering (NAE) Frontiers of Engineering symposium. He was also the Co-Chair for ICML 2019, NAACL 2019, and NeurIPS 2018 ML workshops and regularly serves as Senior/Area Chair and PC of top-tier machine learning and natural language processing conferences like NeurIPS, ICML, ACL, NAACL, AAAI, EMNLP, COLING, KDD, and WSDM.

Website: www.sravi.org

Twitter: @ravisujith

LinkedIn: https://www.linkedin.com/in/sujithravi

Talk: Efficient AI: Building Efficient Neural Computing Machines on the Edge & Cloud

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Talk: Efficient AI: Building Efficient Neural Computing Machines on the Edge & Cloud

Abstract:

Deep learning has changed the computing paradigm. Today, AI researchers & practitioners increasingly use deep neural networks for many applications across different modalities and areas such as NLP, Vision, Speech, Conversational and Multimodal AI. However, much of the Deep Learning revolution has been limited to the Cloud and highly specialized hardware. Recently the AI community has witnessed an increasing trend for training larger and larger neural models (e.g., GPT-3, T5, BERT) that achieve state-of-the-art results but require enormous computation, memory and energy resources on the Cloud. In order to enable AI experiences in real-time across all users and devices, ML models have to run efficiently on the Cloud and personal devices on the Edge (e.g., mobile phones, wearables, IoT) which have limited computing capabilities.

This talk will introduce our work on Neural Projection computing, an efficient AI paradigm, and a family of efficient Projection Neural Network architectures that yield fast (e.g., quadratic speedup for transformer networks) and tiny models that shrink memory requirements by upto 10000x while achieving near state-of-the-art performance powering vision and NLP applications on billions of mobile devices. Widespread increase in availability of connected “smart” appliances (e.g., conversational assistants) means that there is an ever-expanding surface area for mobile intelligence and ambient devices in homes. Our approach enables efficient ML to solve complex prediction tasks for such applications both on-device and on Cloud, keeping model size, compute and power usage low while simultaneously optimizing for accuracy.

What You'll Learn:

Cutting-edge technology & practical applications for efficient Deep Learning on the Edge & Cloud

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Gonzalo Corrales

Sr. Director - Robotics and Machine Learning, Rogers Communications

Gonzalo is the Sr. Director of Intelligent Automation at Rogers Communications. Gonzalo’s work focuses in identifying business problems that can be solved through new technologies like Machine Learning, Intelligent Automation and others. As well, he understands the challenges in operationalizing those solutions, having deployed and implemented several of them in ways that deliver measurable financial results for organization. Gonzalo hold a BSc in Electrical Engineering and an MBA from the Richard Ivey School of Business in Canada.

Talk: Predicting Which Customers Will Experience a Technical Issue Tomorrow and Will Call as A Result

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Dillon Erb

CEO and Co-Founder, Paperspace

Dillon is the CEO and Cofounder of Paperspace, a 5-year-old cloud and machine learning infrastructure company backed by YCombinator, Battery Ventures, Sinewave Capital, Intel Capital, and more.

Talk: Productionizing Deep Learning Models at Scale

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Jacopo Tagliabue

Lead A.I. Scientist, Coveo

Educated in several acronyms across the globe (UNISR, SFI, MIT), Jacopo was founder and CTO of Tooso, an A.I. company based in San Francisco: Tooso was named "Gartner Cool Vendor" in 2019 and acquired by Canadian unicorn Coveo in the same year. Jacopo is currently the Lead A.I. Scientist at Coveo, building A.I. products for a network of more than 400 customers, including several Fortune 500 companies. Together with his team, he combines product thinking with technical innovation on a variety of topics, presenting original findings at major conferences (KDD, HCOMP, ACL, ECAI, RecSys, etc.). In previous lives, he managed to get a Ph.D., do scienc-y things for a pro basketball team and simulate a pre-Columbian civilization.

Talk: AI in the Multiverse: Measuring ROI when A/B Tests are Not Possible

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Joe Greenwood

Vice President Data Strategy - North America, Mastercard

Joe’s career has been focused on building and scaling data-driven products, services, and businesses. Joe is Vice President of Data Strategy for North America at Mastercard with responsibility for realizing Mastercard’s global data strategy and Data Responsibility Imperative across all business lines in North America and enabling the growth of new services through region and product specific data strategies. Previously Joe was Lead Executive for Data at MaRS Discovery District in Toronto, North America’s largest urban innovation hub dedicated to growing Canada’s tech sector. At MaRS Joe founded and led the data practice, building strategic partnerships to scale Canadian data and AI businesses in sectors including retail, finance, energy, and healthcare. Joe has also held leadership roles in product development for location-based data-products and web services at Ordnance Survey in the UK and for secure real-time mobile data services at Blackberry. Joe holds a Masters degree in Geographical Information Science from the University of Edinburgh and an MBA from Manchester Business School and is a Certified Analytics Professional and a Privacy and Access by Design Ambassador.

Talk: Scaling Global Models with Regional Data Strategies and Model Governance

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Shirin Akbarinasaji

Senior Data Scientist, Scotiabank

Shirin is a senior data scientist at Artificial intelligent and machine learning team at Scotiabank. She has an engineering PhD from data science lab, Ryerson University, Canada. Her PhD was on applying Reinforcement leaning to prioritize software bugs in issue tracking system. Shirin's expertise includes but not limited to applying reinforcement learning, deep learning, classification, and clustering to real world problems. She also has experience working in an agile environment and been able to build machine learning solutions praised by internal clients and senior executives.

Talk: Customer Segmentation, Pricing, and Profit Optimization for international Banking

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Talk: Predicting Which Customers Will Experience a Technical Issue Tomorrow and Will Call as A Result

Abstract:

Large telecom providers (and many other industries) spend tens of millions of dollars each year reacting to customer issues. This generally takes the form of large call center and repair technician workforces that are waiting for an issue to happen, in order to help solve it.

Utilizing machine learning and the power of robotic process automation (RPA), we have set out to determine a way to predict which customers are going to reach out with an issue, before they actually do – empowering us to take immediate action, to correct the issue, before a customer notices and before they have to spend their valuable time contact us.

This talk will focus on our journey to build this model, and how we are able to operationalize the findings quickly using RPA.

Finding a way to predict which customers will experience those issues, and taking and action BEFORE the customer calls has been a long sought after objective in the business. At Rogers, we have used Machine Learning to develop such a model with precision rates of over 90%. We have also combined this prediction with an action layer driven Robotics Automation, which takes the actions required to correct the technical issue "before the Customer notices it".

I would like to share how this ecosystem (ML, Robotics and process engineering) will result in significant benefits for the organization.

What You'll Learn:

Real world applications of ML & How we operationalize model findings quickly in an

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Talk: Productionizing Deep Learning Models at Scale

Abstract:

The ecosystem for deploying SaaS applications includes countless tools for delivering an app to production, monitoring its performance, and deploying in real-time. By contrast, while we’ve seen explosive growth in the adoption of machine and deep learning (ML/DL) across industries, putting ML/DL models into production isn’t as well supported. During this talk, we’ll discuss the emerging patterns, state-of-the-art methods, and best practices leading companies are using to productionize ML/DL models.

What You'll Learn:

ML infrastructure and toolstacks are endlessly interesting and convoluted. Dillon has great clarity on macro trends within the infrastructure space while maintaining pragmatism about incorporating the latest open source tools.

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Talk: AI in the Multiverse: Measuring ROI when A/B Tests are Not Possible

Abstract:

A.I. services are deployed to produce improvements to important business metrics, e.g. customer engagement, number of transactions, total profits. When evaluating the contribution of a new service, it is crucial to be able to answer the attribution question: how much of my target outcome would have been achieved even in the absence of the A.I. model? Causal assessments are usually done though A/B tests, which however are not always feasible: who would switch off Amazon recommendations entirely to do such an assessment? In this talk, we show how to use A.I. to assess A.I. contributions to revenues in eCommerce: in particular, we will show how deep learning models can be used to assess how much revenues in a digital shop comes from interactions with search and recommendation APIs. Our findings can be generalized to many other settings, to assess and monitor the performance of existing ML pipelines even in the absence of A/B testing.

What You'll Learn:

Attribution models for site search engines are stuck at "last-action" and Google Analytics-style reporting: since A/B testing the search bar is impossible, it is really hard to make informed business decisions involving the search experience. What would you do if you knew causation, not correlation, in the search behavior of your shoppers?

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Talk: Scaling Global Models with Regional Data Strategies and Model Governance

Abstract:

AI-driven, including ML, models provide the capability to process a greater volume and variety of data to power new global platforms and products and to optimize global business operations. Given that the world and its data are ever more varied and dynamic, to take advantage of this power models need to be highly adaptable to represent the local diversity of events, people, markets, and operations. Models developed only with a global perspective can result in missing valuable insights, and potential harms from models that are biased in their results, or inadvertently exclude groups in society. This talk will outline the business imperative for robust and ethical model design and Mastercard's approach to leveraging a global data-strategy that sets the highest standards for the responsible use of data and AI though human-centered data-design while ensuring local compatibility and functionality through a regional approach to data sourcing and quality, model testing and governance, and internal data literacy. The benefits of scaling global models through regional data strategies will be illustrated with examples from fraud detection, credit decisioning, economic modeling, and understanding consumer preferences.

What You'll Learn:

How to set out an enterprise approach to responsible use of data and AI, how to translate that into global data strategy elements and frameworks and then how to use regional or country specific data and model building strategies

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Talk: Customer Segmentation, Pricing, and Profit Optimization for international Banking

Abstract:

Background: Pricing is a famous business issue in many companies and organizations. The approach behind pricing analytics can be formulate as customer segmentation and constrained optimization problem in order to increasing sales and/or revenue.

Aim: Our main objectives is to design a pricing product that can help to:
1) Identify groups of elastic and inelastic customers,
2) Determine the optimal rate for each group of customers,
3) Be agonistic pipeline and can be reusable for other pricing use cases.

Methodology: We propose to use model based recursive partitioning (MOB) which use product characteristics and customer attributes as input and customer willingness to pay as output to segment customers. For each customer segmentation, we found the demand curve function and formulate the nonlinear optimization problem that maximize the sale or revenue using PYOMO and IPOPT.

Results: This pricing product has been used in three different countries, Peru, Coloumbia and Mexico in various products such as mortgage, SPL, and term deposit with great feedback. It helped Scotiabank to capture international banking customer behaviour and their price sensitivity more promptly .

What You'll Learn:

This is about applying cutting edge machine learning domain in the banking domain. As pricing is very critical, mainly companies do not reveal their methodology so google search will not help that much.

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Mary Jane Dykeman

Partner & Co-Founder, INQ Data Law

Talk: Applied Machine Learning in Healthcare - Practical and Legal Considerations

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Talk: Applied Machine Learning In Healthcare - Practical And Legal Considerations

Abstract:

What You'll Learn:

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Francisco Martha Gonzales

Payments, Digital Banking and IT Managing Director, Banorte

In 2019 Francisco was appointed as Payments, Digital Banking and IT Managing Director. He has strong interest in new technology, innovation and delivery in the financial services sector with the main challenge of the digital transformation of the organization, products and services.

Previously, Francisco worked as the CIO focused on the development of an advanced organizational model that will allow management and operation of infrastructure and solutions, increase the capacity of project implementation and encourage innovation processes in the organization.

With close to thirty years of experience in the financial sector, he has developed his career in the Technology, Products, Payments, Digital Banking and Operations departments where he has successfully participated in numerous consulting projects, operational and organizational transformations, apps development, systems integration and infrastructure

Francisco has a degree of Chemical Engineering from Universidad Iberoamericana (Mexico) and has completed degrees in different Universities in Mexico, USA and Canada.

Francisco is married and has two children with whom he shares as much time as possible practicing sports.

Talk: Banorte’s AI Transformation Journey: How the Analytics Team of this Bank Yielded 3 Billion USD Revenue During the Past Five Years

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Talk: Banorte's AI Transformation Journey: How the Analytics Team of this Bank Yielded 3 Billion USD Revenue During the Past Five Years.

Abstract:

What You'll Learn:

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Jaya Kawale

Director of Machine Learning, Tubi

Jaya Kawale is the Director of Machine Learning at Tubi leading all of the machine learning efforts at Tubi encompassing homepage recommendations, content understanding and ads. Prior to Tubi, she has worked on different aspects of recommender systems at Netflix and Adobe research labs. She did her PhD from the University of Minnesota, Twin cities and her thesis won several awards including the Explorations in Science using computation award. She has published many top tier conference and journal papers.

Talk: Understanding Content Using Deep Learning for Natural Language Processing

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Matt Sheehan

Fellow The Paulson Institute

Matt Sheehan is a Fellow at the Paulson Institute’s think tank, MacroPolo, where he leads work on U.S.-China technology issues, with a specialization in artificial intelligence. His research maps and quantifies the key inputs to AI ecosystems globally. Matt is the author of the book The Transpacific Experiment: How China and California Collaborate and Compete for our Future.

From 2010-2016 Matt lived and worked in China, including as the first China correspondent for The World Post. In 2016 he returned to the Bay Area, working as an analyst and consultant on topics connecting China and California. In 2018, he was selected as a finalist for the Young China Watcher of the Year award.

Matt's research has been cited and he has been quoted in numerous media outlets, including The New York Times, The Wall Street Journal, The Financial Times, Reuters, The San Francisco Chronicle, and elsewhere.

Talk: Assessing China’s AI Capabilities

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Rich Caruana

Principal Researcher, Microsoft Research

Rich Caruana is a Senior Principal Researcher at Microsoft. His focus is on intelligible/transparent modeling, machine learning for medical decision making, deep learning, and computational ecology. Before joining Microsoft, Rich was on the faculty in Computer Science at Cornell, at UCLA's Medical School, and at CMU's Center for Learning and Discovery. Rich's Ph.D. is from CMU. His work on Multitask Learning helped create interest in a subfield of machine learning called Transfer Learning. Rich received an NSF CAREER Award in 2004 (for Meta Clustering), best paper awards in 2005 (with Alex Niculescu-Mizil), 2007 (with Daria Sorokina), and 2014 (with Todd Kulesza, Saleema Amershi, Danyel Fisher, and Denis Charles), and co-chaired KDD in 2007 with Xindong Wu.

Talk: Friends Don’t Let Friends Deploy Black-Box Models: The Importance of Intelligibility in Machine Learning for Healthcare

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Mai B Phan

Race Data Collection Expert, Toronto Police Service

Dr. Mai Phan is a data expert consultant currently supporting Toronto Police Service's ground-breaking anti-racism initiative. Mai works collaboratively with colleagues and partners to promote an evidence-based approach to anti-racism, human rights, and inclusion in public service organizations.

Mai was senior research/policy advisor at the Anti-Racism Directorate. She led the development and establishment of the Anti-Racism Data Standards and provided strategic advice and support to public sector organizations regulated to collect race-based data under the Anti-Racism Act. She contributed to the development of the Systemic Racial Barriers Identification and Removal Program to support advancement of workplace racial equity and inclusion within the Ontario Public Service.

Prior to that, as a Human Rights Advisor in the Ministry of Community Safety and Correctional Services, Mai supported initiatives to address systemic discrimination and remove barriers in employment and service delivery in correctional services.

Talk: Race Data As An Anti-Racism Tool

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Kan Deng, PhD

Founder & CEO, Beijing Rxthinking Inc.

Kan Deng PhD, graduated from Shanghai Jiaotong University with Bachelor and Master degree, then graduated from Carnegie Mellon University with Ph.D degree for Robotics and Machine Learning.

After graduation, Dr Deng worked with Oracle Inc as a Principal Architect for 6 years, worked with Telenav as the General Manager of its Beijing lab for 5 years, then worked with Baidu as a senior director in charge of its core business, web search engine.

In 2015, Dr Deng founded Beijing RxThinking Inc, applying deep reinforcement learning cutting-edge technology to solve healthcare problems.

RxThinking has been collecting more than 800 million of Electronic Health Records from top hospitals in China. We translate those 800 million EHRs into structured clinical routes, one by one, so that we have 800 million structure clinical routes. After then we compress them together to assemble a medical map.

With the medical map, we develop AI doctor assistant. Our AI doctor assistant can answer patients’ queries, recommend what-to-do-next to the community doctors, supervise the quality and cost for the hospitals, and improve the doctor's productivity hundreds of times especially for online remote diagnosis.

During the pandemic, our product is widely used in China serving over 200 million people, including the citizens in Wuhan. And we are awarded as the top 10 of the 100 best practices of AI medical solutions, by China Academy of Information and Communications Technology (CAICT) in April 2020.

Talk: AI. MD in the Time of Corona Virus in China

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Abstract:

With widespread use of machine learning, there have been serious societal consequences from using black box models for high-stakes decisions, including flawed bail and parole decisions in criminal justice. Explanations for black box models are not reliable, and can be misleading. If we use interpretable machine learning models, they come with their own explanations, which are faithful to what the model actually computes.

What You'll Learn:

You will learn that there is a chasm between explaining black box models and using inherently interpretable models. You may also find my experience helpful, which is that we have never needed a black box model for a high stakes decision, because we have always been able to construct an interpretable model that is at the same level of predictive performance as the best black box we could find.

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Shreyansh Daftry

AI Research Scientist, NASA Jet Propulsion Laboratory

Shreyansh Daftry is a Research Scientist at NASA Jet Propulsion Laboratory (JPL) in Pasadena, California, working at the intersection of Artificial Intelligence and Space Technology to help develop the next generation of robots for Earth, Mars and beyond. Shreyansh received his M.S. degree in Robotics from the Robotics Institute, Carnegie Mellon University, USA, and his B.S. degree in Electronics and Communications Engineering from Manipal University, India. His research interests spans computer vision, machine learning and autonomous robotics, with a focus on real-time computation, safety and adaptability. Shreyansh has received numerous awards and honors, including the NASA Space Act Award and the JPL Software of the Year award, for his contributions to the field of space and aeronautics.

Talk: Machine Learning for Space Exploration

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Gaurav Nemade

Product Manager, Google AI

Gaurav is a Product Manager at Google AI. He focuses on bringing cutting-edge Conversation AI technologies from research to production. He is also a product advisor for AI startups in Google's internal startup incubator, Area120. Prior to that, he led Payments Trust & Safety efforts at Google for the APAC region and co-founded a FinTech startup Novus Minds in India. He holds a degree in Computer Science from IIT Roorkee in India.

Talk: Fine-Grained Emotion Detection for Products & Research

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Talk: Machine Learning for Space Exploration

Abstract:

Artificial Intelligence is playing an increasing role in the space industry, where AI related technologies such as machine learning have the potential to revolutionize almost every aspect of space exploration. In this talk, we will discuss the evolution of autonomous robots for space exploration and planetary science. Next we will look at examples of machine learning technologies we are developing for autonomous robotic applications on Earth, Mars and beyond, and describe some of the grand challenges in AI for such safety-critical systems. Finally, we describe lessons learnt from space industry that can be applied to industrial applications here on Earth.

What You'll Learn:

- How AI/ML is being used by NASA to enable the next frontier in robotics space exploration

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Dana received a Ph.D. in Computer Science from Carnegie Mellon University in 2015 and a M.S. and B.S. in Computer Science and Computational Biology from the Hebrew University of Jerusalem, Israel in 2010 and 2007.

Talk: Fine-Grained Emotion Detection for Products & Research

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Talk: Fine-Grained Emotion Detection for Products & Research

Abstract:

What You'll Learn:

1. How emotions can be detected from textual content for business use cases & research purposes
2. Details about data for training own models

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Azin Asgarian

Applied Research Scientist, Georgian

Azin Asgarian is currently an Applied Research Scientist on Georgian’s R&D team where she works with companies to help adopt applied research techniques to overcome business challenges. Azin holds a Master of Science in Computer Science from University of Toronto and a Bachelor of Computer Science from University of Tehran. Prior to joining Georgian, Azin was a research assistant at the University of Toronto and part of the Computer Vision Group where she was working on the intersection of Machine Learning, Transfer Learning, and Computer Vision. Due to her interest in HealthCare, she has worked on various healthcare projects as a research assistant at University Health Network (UHN).

Talk: Overcoming the Cold Start Problem: How to Make New Tasks Tractable

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Talk: Overcoming the Cold Start Problem: How to Make New Tasks Tractable

Abstract:

In recent years, fuelled by the advances in supervised machine learning, we have seen astonishing leaps in the application of deep neural networks. Despite the remarkable results, these models are data-hungry and their performance relies heavily on the quality and size of the training data. In real-world scenarios, this can increase the time to value add significantly for businesses as collecting huge amounts of labeled data is usually very time and cost consuming. This phenomenon—known as the cold start problem—is a pain point for almost any AI company that wants to scale. In this talk, we demonstrate how this problem can be addressed by aggregating data across sources and leveraging previously trained models.

What You'll Learn:

In this talk, you will see real examples of the cold start problem and how it can prevent businesses from effectively and efficiently growing. You will learn about various machine learning methods that can be used to address this problem.

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Franziska Kirschner

Research Lead, Tractable

Fran Kirschner is a Research Lead at Tractable. She develops Tractable’s deep learning algorithms, and focuses on diversifying and scaling the core AI across domains. Franziska started life as a physicist, and completed her PhD in condensed matter physics at the University of Oxford.

Talk: Overcoming the Cold Start Problem: How to Make New Tasks Tractable

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Talk: Overcoming the Cold Start Problem: How to Make New Tasks Tractable

Abstract:

What You'll Learn:

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Danit Gal

Technology Advisor, The United Nations

Danit Gal is Technology Advisor at the United Nations, leading work on AI in the implementation of the United Nations Secretary-General's Roadmap for Digital Cooperation. She is interested in technology ethics, geopolitics, governance, safety, and security. Previously, she was Project Assistant Professor at the Cyber Civilizations Research Center at Keio University in Tokyo, Japan. Danit serves as the former chair and vice chair of the P7009 IEEE standard on the Fail-Safe Design of Autonomous and Semi-Autonomous Systems, and the executive committee of The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems. She is an Associate Fellow at the Leverhulme Centre for the Future of Intelligence at the University of Cambridge, and Visiting Research Fellow at the S. Rajaratnam School of International Studies at the Nanyang Technological University.

Talk: The State of AI/ML at the United Nations

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Talk: The State of AI/ML at the United Nations

Abstract:

This talk introduces relevant work on AI/ML at the United Nations to provide an overview of key developments and use cases and highlight opportunities for collaboration.

What You'll Learn:

You'll learn about exciting applications of AI/ML at the United Nations and learn about opportunities to collaborate.

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Xunyu Zhou

Professor, Department of IEOR, Columbia University

Xunyu Zhou is the Liu Family Professor of Industrial Engineering and Operations Research at Columbia University in New York. He was the Nomura Professor of Mathematical Finance, the Director of Nomura Center for Mathematical Finance, and the Director of Oxford-Nie Financial Big Data Lab at the University of Oxford during 2007-2016 before joining Columbia.

His research includes reinforcement learning in continuous time and spaces, quantitative behavioral finance models that incorporate human emotions and psychology into financial decision makings, and intelligent wealth management solutions using stochastic control and machine learning techniques.

Professor Zhou is known for his work in indefinite stochastic LQ control theory and application to dynamic mean-variance portfolio selection, in asset allocation and pricing under cumulative prospect theory, and in general time-inconsistent problems. He directs the Nie Center for Intelligent Asset Management, a research center funded by a FinTech company, at Columbia. He has addressed the 2010 International Congress of Mathematicians, and has been awarded the Wolfson Research Award from The Royal Society (UK), the Outstanding Paper Prize from the Society for Industrial and Applied Mathematics, the Humboldt Distinguished Lecturer, the Alexander von Humboldt Research Fellowship, and the Archimedes Lecturer at Columbia. He is both an IEEE Fellow and a SIAM Fellow.

Professor Zhou received his Ph.D. in Operations Research and Control Theory from Fudan University in China in 1989.

Talk: Reinforcement Learning via Stochastic Control

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Nathan Killoran

Head of Software & Algorithms, Xanadu Quantum Technologies

Nathan Killoran is the Head of Software & Algorithms at Xanadu, and one of the founding developers of PennyLane, the world’s leading quantum machine learning software library. Nathan steers Xanadu’s open-source quantum software products and leads algorithm research in photonics and quantum machine learning. Nathan holds a PhD in Physics from the University of Waterloo, with expertise in quantum computing, deep learning, and quantum optics.

Talk: Software for Quantum Machine Learning

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Talk: Reinforcement Learning via Stochastic Control

Abstract:

While most existing reinforcement learning (RL) research is in the framework of Markov Decision Processes (MDPs), it is important and indeed necessary, both theoretically and practically, to consider RL in continuous time with continuous feature and action spaces, for which stochastic control theory offers a natural underpinning. The related research is still in its infancy, and this talk reports some of the latest developments and suggests several directions for investigation.

What You'll Learn:

Theoretical foundation and interpretation of some of the commonly used heuristics in reinforcement learning such as entropy regularization and Gibbs/Boltzmann/Gaussian exploration.

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Talk: Software for Quantum Machine Learning

Abstract:

One of the fundamental goals in the emerging field of quantum machine learning is to build trainable quantum computing algorithms. It turns out that we can, with very minimal changes, port many existing ideas, algorithms, and training strategies from deep learning over to the quantum domain. This allows us to train quantum computers in largely the same way as we do neural networks, even using familiar software tools like TensorFlow and PyTorch. In this talk, I will give a high-level overview of the key ideas that make this possible.

What You'll Learn:

You'll learn how to use software tools like PennyLane, TensorFlow, and PyTorch to train quantum computers!

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Sedef Kocak

Project Manager at Applied AI Project, Vector Institute

Sedef Akinli Kocak is the Project Manager at Vector Institute for Artificial Intelligence engaging Vector sponsors on AI projects. Currently, she leads several multi-industrial participant projects. She holds a Ph.D. degree from the Data Science Lab at Ryerson University, Canada, and earned her master’s degrees in both Chemical Engineering and Business of Administration. She worked in data-intensive R&D project development and academic-industry partnerships in the area of AI/ML at SOSCIP, the Southern Ontario Smart Computing for Innovation Platform. She is also an experienced and accomplished researcher in the area of ICT for sustainability and sustainability design in software-intensive systems and a part-time Data Science and Analytics lecturer and supervisor at Ryerson University since 2014. She served as a member of the Compute Ontario Board Advisory Committee and AI program development advisor at the Continuing Education, University of Toronto.

Talk: Harnessing the Power of NLP: A Vector Institute Industry Collaborative Project

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Talk: Harnessing the Power of NLP: A Vector Institute Industry Collaborative Project

Abstract:

The Vector Institute’s project, Recreation of Large Scale Pre-Trained Language Models (the NLP Project), is an industry-academia collaboration that explores how state-of-the-art natural language processing (NLP) models could be applied in business and industry settings at scale. Developing and employing NLP models in industry has become progressively more challenging as model complexity increases, data sets grow in size, and computational requirements rise. These hurdles limit the accessibility many organizations have to NLP capabilities, putting the significant benefits advanced NLP can provide out of reach. The NLP Project addressed these challenges by familiarizing industry participants with advanced NLP techniques and the workflows for developing new methods that could achieve high performance while using relatively small data sets and widely accessible computing resources. The project involved 60 participants: 23 Vector researchers and staff with expertise in machine learning and NLP along with 37 industry technical professionals from 16 Vector sponsor companies. The participants established 11 working groups, each of which developed and performed experiments relevant to existing industry needs.

In this talk, I will provide an overview of the NLP project and share how industry participants gained practical knowledge through pre-training large scale language models, learned theoretical concepts from leading NLP practitioners, and broadened their professional network through collaborations with participating sponsors. I will share some of the technical challenges that we encountered throughout the project and how we overcome them. Finally, I will offer best practices to guide future industry collaborative projects.

What You'll Learn:

How Vector Institute's industry collaborations help sponsors in adoption of AI advances, specifically in the NLP domain.

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Rebecca Knowles

Research Associate, National Research Council of Canada

Rebecca Knowles is a Research Associate at the National Research Council of Canada. Her research focuses on machine translation and computer-aided translation. She holds a Ph.D. in computer science from Johns Hopkins University, where she was a National Science Foundation Graduate Research Fellow.

Talk: Indigenous Language Technologies: Neural Machine Translation for Inuktitut

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Mutale Nkonde

CEO, AI For the People

Talk:

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Alba Cervera Lierta

Postdoctoral Researcher, University of Toronto

Alba Cervera Lierta did her doctoral studies in entanglement applications in quantum information at the University of Barcelona. She also worked on quantum algorithms for near-term applications at the Barcelona Supercomputing Center. Her background includes particle physics phenomenology, multipartitie entanglement and quantum information. She is currently a postdoctoral fellow at the Alán Aspuru-Guzik group at the University of Toronto. She is working on variational quantum algorithms and computational tools for quantum simulation.

Talk: The Quest for the Final Quantum Computer

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Paige Bailey

Product Manager, Google Research

Talk: Everything You Need to Know About New Libraries in the Keras Ecosystem

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Alan Aspuru-Guzik

Professor of Chemistry and Computer Science, University of Toronto

Alán Aspuru-Guzik’s research lies at the interface of computer science with chemistry and physics. He works in the integration of robotics, machine learning and high-throughput quantum chemistry for the development of materials acceleration platforms. These “self-driving laboratories¨ promise to accelerate the rate of scientific discovery, with applications to clean energy and optoelectronic materials. Alán also develops quantum computer algorithms for quantum machine learning and has pioneered quantum algorithms for the simulation of matter. He is jointly appointed as a Professor of Chemistry and Computer Science at the University of Toronto. Alán is a faculty member of the Vector Institute for Artificial Intelligence. Previously, Alán was a full professor at Harvard University where he started his career in 2006. Alán is currently the Canada 150 Research Chair in Quantum Chemistry as well as a CIFAR AI Chair at the Vector Institute. Amongst other awards, Alán is a recipient of the Google Focused Award in Quantum Computing, the MIT Technology Review 35 under 35, and the Sloan and Camille and Henry Dreyfus Fellowships. Alán is a fellow of the American Association of the Advancement of Science and the American Physical Society. He is a co-founder of Zapata Computing and Kebotix, two early-stage ventures in quantum computing and self-driving laboratories respectively.

Talk: Artificial Intelligence for Molecular Design and Self Driving Labs

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Alejandro Perdomo Ortiz

Lead Quantum Applications, Zapata Computing Inc.

Alejandro did his graduate studies, M.A and Ph.D. in Chemical Physics, at Harvard University. Over the past 12+ years, he has explored the computational limits and opportunities of quantum computing for real-world applications. Before joining Zapata Computing as a Senior Quantum Scientist and Quantum Applications Lead, Alejandro was the lead scientist of the Quantum Machine Learning effort at NASA's Quantum Artificial Intelligence Laboratory (NASA QuAIL) where he worked for 5+ years. He was also the Co-Founder of Qubitera LLC, a consulting company acquired by Rigetti Computing where he worked after NASA and before his current appointment with Zapata Computing. His latest research involves the design of hybrid quantum-classical algorithms to solve hard optimization problems and intractable machine learning subroutines.

Talk: Quantum - Assisted Machine Learning with Near-Term Quantum Devices

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Marco Túlio Ribeiro

Researcher, Microsoft Research

Marco Tulio Ribeiro is a Senior Researcher at Microsoft Research. His work is on facilitating the communication between humans and machine learning models, which includes interpretability, trust, debugging, feedback, robustness, testing, etc. He received his PhD from the University of Washington.

Talk: Productionizing Deep Learning Models at Scale

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Richard Zuroff

Advisor, Element AI

Richard is an advisor to companies, start-ups, and policy-makers on AI strategy and governance. He has applied this on-the-ground knowledge of how AI is transforming organizations and the economy as an expert participant in many forums investigating the broader social impact of the technology, including the Brookfield Institute, the Federal Economic Strategy Table for Digital Industries, and the Partnership on AI. He holds an M.B.A., two Law degrees, and a BSc in Cognitive Science.

Talk: Algorithmic Decision Making: Exploring Practical Approaches to Liability, Fairness, and Explainability

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Talieh Tabatabaei

Data Scientist, TD Bank

Talieh Tabatabaei holds B.Eng and MASc degrees in Electrical and Computer Engineering. She has more than 8 years of working experience in the field of Machine Learning and Artificial Intelligence in high-level academic research, teaching, and industry, with several publications in this field.

Talieh is currently working as a data scientist at TD Bank.

Talk: Algorithmic Decision Making: Exploring Practical Approaches to Liability, Fairness, and Explainability

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Patrick Hall

Principal Scientist, bnh.ai

Patrick Hall is the principal scientist at bnh.ai, a boutique law firm focused on AI and data analytics. He also serves as a visiting assistant professor of decision sciences at the George Washington University School of Business and as an advisor to select machine learning startups. Before co-founding bnh.ai, Patrick led responsible AI efforts at H2O.ai. His work at H2O resulted in one of the world's first commercial solutions for explainable and fair machine learning. Among other academic and technology media writing, Patrick is the primary author of popular e-books on explainable and responsible machine learning.

Patrick has also held global customer-facing and R&D roles at SAS Institute, where he authored multiple patents in automated market segmentation using novel clustering methods and deep learning. During these years, he became the 11th person worldwide to become a Cloudera certified data scientist. Patrick studied computational chemistry at the University of Illinois before graduating from the Institute for Advanced Analytics at North Carolina State University.

Talk: Algorithmic Decision Making: Exploring Practical Approaches to Liability, Fairness, and Explainability

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Sasha Luccioni

Researcher, MILA

Sasha Luccioni is a researcher working with Yoshua Bengio at the Mila AI institute to fight climate change using Artificial Intelligence. She leads projects at the nexus of AI and climate change, using generative networks to visualize the consequences of climate change and tracking the carbon footprint of AI. In the past, she worked in the finance sector, but decided to follow her heart and leave a top Wall Street company to use her skills in AI to make the world a better place. She is highly involved in community initiatives, serving on the Advisory board of Kids Code Jeunesse and as a chair of the Climate Change AI initiative.

Talk: The Role of ML in Climate Change

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Arthur Berrill

Head of Location Intelligence, RBC

Arthur Berrill is the Head of Data Science at the Royal Bank of Canada. His charter is to research, guide and deliver data science capabilities including location intelligence, new data content, artificial intelligence, ontology and climate change studies across all departments of the bank.

Arthur is an RBC Distinguished Technologist.

Arthur has more than 40 years of experience in the architecture, design and development of enterprise spatial systems including WILDMAP (a GIS before the term existed), SYSTEM 9, SpatialWare®, MapInfo products and Location Hub®. He holds numerous key and valuable patents in the location intelligence and spatial systems domain.

Fascinated by the use of new technology to solve business challenges, Arthur is working on future data strategies, inventing new or improved algorithms and methods and helping businesses see technology opportunities as or before they emerge.

Prior to RBC, Arthur led the location intelligence initiative at Scotiabank and before then was president of DMTI Spatial. Arthur was Inventor of the Year for 2008 at Pitney Bowes (MapInfo) and won the TechAmerica 50th Anniversary Innovation Award in 2009.

Arthur is a graduate with Honours from the University of Queensland and did his postgraduate work at the International Institute of Aerial Survey and Earth Sciences in the Netherlands.

Talk: The Role of ML in Climate Change

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Jules Andrew

Senior Vice President, Enterprise Operations and Payments, RBC

Jules Andrew has held leadership financial and operational positions across the globe with IBM and RBC for over 20 years

Jules was appointed Senior Vice President, Strategy, Transformation and Enterprise Operations, Technology and Operations, RBC, in February 2020. In this role, she is responsible for developing and executing T&O’s strategy in support of RBC’s goal to be a digitally-enabled, relationship bank and transforming shared services, including finance, to enhance RBC’s industry-leading position. Jules’ team facilitates money movement for clients, suppliers and employees and ensures branches and ATMs have cash.

Jules is the current chair of the T&O Diversity Leadership Council where she supports T&O employees to be champions, advocates and examples of diversity, inclusion & belonging at RBC. Working with organizations such as Women in Communications and Technology, Canadian Women in Business and Girls Who Code, Jules actively promotes the importance of encouraging women to pursue STEM careers and leadership roles. She is also responsible for several hackathons and innovation challenges encouraging diversity and community involvement for students globally. She holds a Master’s of Science in Management from Purdue University. Jules lives in Toronto and loves to travel – having lived in 5 countries and visited 58.

Talk: The Role of ML in Climate Change

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Inmar Givoni

Director Of Engineering, Uber

Talk: Autonomous Vehicles - The Next Step Forward

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Nima Ashtari

Founder, X-Matik Inc.

Talk: Autonomous Vehicles - The Next Step Forward

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Jianxiong Xiao (Professor X)

Founder & CEO, AutoX, Inc.

Talk: Autonomous Vehicles - The Next Step Forward

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Jennifer Nguyen

Lead Data Scientist, Sun Life

Jennifer is the Lead Data Scientist at Sun Life Financial’s Analytics Centre of Excellence, helping the company to build intelligent data solutions to better serve their clients. Her past experience in the field includes the Globe and Mail, Scribd and Slyce. She holds a Master’s in Machine Learning from University College London and a B. Math from the University of Waterloo. Jennifer is a strong proponent of gender diversity in her field and partners with the University of Waterloo to support young females pursuing careers in STEM.

Talk: Forget ROC scores, What Metrics Do your Stakeholders Care About?

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Ali Madani

Leader of Machine Learning Team, Cyclica

Ali leads the machine learning team at Cyclica Inc focusing on improving the company's technology for predicting interaction between drugs and target proteins. As a computational biologist and machine learning specialist, Ali has worked on a series of scientific articles in high impact scientific journals and international conferences covering such fields as transfer learning and unsupervised clustering. He earned his Ph.D from the University of Toronto, and master of mathematics degree from the University of Waterloo.

Talk: Deep Learning Across Label Confidence Distribution via Transfer Learning

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Talk: Beyond Accuracy: Behavioral Testing of NLP Models with CheckList

Abstract:

What You'll Learn:

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Talk: Quantum - Assisted Machine Learning with Near-Term Quantum Devices

Abstract:

With quantum computing technologies nearing the era of commercialization and quantum advantage, machine learning (ML) has been proposed as one of the promising killer applications. Despite significant effort, there has been a disconnect between most quantum ML proposals, the needs of ML practitioners, and the capabilities of near-term quantum devices towards a conclusive demonstration of a meaningful quantum advantage in the near future. In this talk, we provide concrete examples of intractable ML tasks that could be enhanced with near-term devices. We argue that to reach this target, the focus should be on areas where ML researchers are struggling, such as generative models in unsupervised and semi-supervised learning, instead of the popular and more tractable supervised learning tasks. We will discuss recent experimental implementations of these quantum generative models, in both, superconducting-qubit and ion-trap quantum computers.

What You'll Learn:

Talk: Forget ROC scores, What Metrics Do your Stakeholders Care About?

Abstract:

How do you get buy-in from leadership to sponsor your ML project? How do you convince your stakeholders to put your ML models into production? And finally, how do you communicate the ROI of the project once it’s been deployed?

While these are questions universal to any industry, they are particularly challenging to answer in the insurance industry because of its highly regulated and risk-averse nature. As such, more creative thinking is needed to convince stakeholders that your ML solutions can be trusted and bring value.

What You'll Learn:

In this talk, we will share lessons we learned in answering three questions and the metrics stakeholders care about. The talk is designed so that those managing projects (e.g., data science directors/managers) and those executing the work (e.g., data scientists/analysts) can walk away with tips to help their ML projects start and close off successfully.

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Talk: Deep Learning Across Label Confidence Distribution via Transfer Learning

Abstract:

Performance of neural network models relies on the availability of large datasets with minimal levels of uncertainty. However, in aggregated data environments, confidence in the individual data points vary in a quantifiable manner by primary data source or measurement type. Differences in label confidence make model building challenging, as the optimization cannot be done while amalgamating all the data points in the training process. In this work, we apply a transfer learning approach to improve predictive power in noisy data systems with large variable confidence datasets. We propose a deep neural network approach called Filtered Transfer Learning (FTL) that defines multiple tiers of data confidence as separate tasks in a transfer learning setting. The deep neural network is fine-tuned in a hierarchical process by iteratively removing (filtering) data points with lower label confidence, and retraining. In this report we use FTL for predicting the interaction of drugs and proteins. We demonstrate that using FTL to learn stepwise, across the label confidence distribution, results in higher performance compared to deep neural network models trained on a single confidence range.

The challenge of mixed confidence training data is not restricted to the domain of protein and drug interaction; in practice, data labeling is done based on either computational algorithms or human experts (or even non-experts), and neither approach is perfect. Other examples with differences in data point label confidence include: radiological or histopathological images or image segment labels, and measured resistance to cancer drugs. We anticipate that FTL will enable the machine learning community to benefit from large datasets with uncertain labels in fields such as biology and medicine.

What You'll Learn:

How to deal with data points with different levels of confidence in deep learning setting

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Workshop: Knowledge Graph Recommendation Systems for COVID-19

Core Takeaways:

CORD-19, or COVID-19 Open Research Dataset, was launched in March 2020, in response to a request from the White House’s office of Science and Technology Policy. The joint collaboration from AI2, Microsoft, the NLM at the NIH, and other prestigious research institutes aims at empowering the world’s AI researchers with a text and data mining tools to help accelerate COVID-19 related research.

The objective of this tutorial is to give the audience hands-on experience to work through the basics about knowledge graph and recommender technology, and how to use them for building an article recommender for COVID-19 research.

All the code is available and open sourced.

Prequestite Knowledge:

- Knowledge of python

- Knowledge of recommendation systems

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Pooja Bhojwani

Senior Data Scientist, Manager, Scotiabank

Pooja is a Senior Manager, Data Scientist at Scotiabank, Pooja has given several talks as part of analytics community at Scotiaban and was also a TA at last year's workshop at TMLS

Workshop: Leveraging Pretrained Language Models for Natural Language Understanding

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Workshop: Leveraging Pretrained Language Models for Natural Language Understanding

Core Takeaways:

Data Scientist/Machine Learning Specialist & Senior ML Specialist, AWS

Stefan Natu is a Sr. Machine Learning (ML) Specialist at Amazon Web Services with a focus on financial services. He spends his time working with enterprise financial services customers from investment banking, asset management and investment research on building secure environments, best practices on model development, model governance and operationalizing ML workflows. He did his PhD in Atomic and Condensed Matter Physics from Cornell, and worked as a research physicist at ExxonMobil building machine learning models for oil and gas exploration. Prior to joining AWS, he worked at Publicis Sapient as a data scientist focused on ML in the retail vertical.

Saeed Aghabozorgi Ph.D. is senior ML Specialist in AWS, with a track record of developing enterprise level solutions that substantially increase customers’ ability to turn their data into actionable knowledge. He is also a researcher in the artificial intelligence and machine learning field.

Workshop: Build, Train, and Deploy Models with Amazon SageMaker

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Workshop: Build, Train, and Deploy Models with Amazon SageMaker

Core Takeaways:

Hands-on experience with SageMaker to train and deploy a ML model

Prequestite Knowledge:

Basics of machine learning

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Saeed Aghabozorgi

ML Specialist Solutions Architect, AWS

Workshop: Build, Train, and Deploy Models with Amazon SageMaker

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Workshop: Docker Based Workflow for Deploying a Machine Learning Model

Core Takeaways:

After attending this workshop, students will learn how Docker and containers fit into the ML development lifecycle. We will start with the basics of containers and work our way up to a full example. We will cover topics such as: reproducibility, portability, and ease of deployment.

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Aniththa Umamahesan

Program Manager, Microsoft

Aniththa Umamahesan is a Program Manager on Microsoft Azure Machine Learning team. Her latest mission is accelerating and democratizing Artificial Intelligence via Automated Machine Learning.

Workshop: Machine Learning Simplified: From Ideation to Deployment in Minutes with Automated Machine Learning

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Workshop: Machine Learning Simplified: From Ideation to Deployment in Minutes with Automated Machine Learning

Core Takeaways:

Artificial intelligence (AI) has become the hottest topic in tech. Executives, analysts, engineers, and developers all want to leverage the power of AI to gain better insights and make better predictions. But machine learning requires advanced data science skills that are hard to come by. Automated ML is an emerging field that helps developers and new data scientists build ML models without understanding the complexity of algorithm selection and hyper parameter tuning. This session shows you how to train a high quality model with Azure Machine Learning automated ML by supplying only a dataset and a few configuration parameters.

Prequestite Knowledge:

Tutorial will be for all levels.

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Elle O'Brien

Data Scientist, Iterative.ai

Elle is a data scientist at Iterative, a startup building open source software tools for machine learning. She completed her PhD at the University of Washington where she conducted research on speech and hearing using mathematical models. Elle is broadly interested in developing methods, standards, and educational resources for anyone who works with data.

Workshop: How to Automate Machine Learning with GitHub Actions

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Workshop: How to Automate Machine Learning with GitHub Actions

Core Takeaways:

- How to write your own GitHub Actions to automatically train, test, and report machine learning models

- Why automation is key to speeding up development cycles and getting models into production faster

Prequestite Knowledge:

- Basic familiarity with machine learning (framework agnostic)

- Some exposure to Git/GitHub is helpful, but not necessary. Git beginners are welcome.

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Rhys Williams

AI Instructor, WeCloudData

Rhys Williams is a senior data scientist at Beam Data. He’s an avid lover of machine learning and AI. In his spare time, he builds his own robots using Raspberry Pi and trains deep neural nets to teach the bots to recognize objects and understand human language. Rhys is also a part-time AI instructor at WeCloudData.

Workshop: Deploying Deep Learning Models to The Edge

Workshop: Introduction to Tensorflow (Hands-on Workshop)

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Workshop: Deploying Deep Learning Models to The Edge

Core Takeaways:

Learn how Tensorflow models can be deployed to mini-robots

Prequestite Knowledge:

Python, Deep Learning, Computer Vision basics, IoT basics

Workshop: Introduction to Tensorflow (Hands-on Workshop)

Core Takeaways:

Complete an end to end Tensorflow tutorial

Prequestite Knowledge:

Python, ML basics

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Yaron Haviv

CTO and founder, Iguazio

Yaron Haviv is a serial entrepreneur who has been applying his deep technological experience in data, cloud, AI and networking to leading startups and enterprise companies since the late 1990s. As the co-founder and CTO of Iguazio, Yaron drives the strategy for the company’s data science platform and leads the shift towards real-time AI. He also initiated and built Nuclio, a leading open source serverless platform with over 3,400 Github stars and MLRun, Iguazio’s open source MLOps orchestration framework.

Prior to co-founding Iguazio in 2014, Yaron was the Vice President of Datacenter Solutions at Mellanox (now NVIDIA), where he led technology innovation, software development and solution integrations. He was also the CTO and Vice President of R&D at Voltaire, a high-performance computing, IO and networking company which floated on the NYSE in 2007. Yaron is an active contributor to the CNCF Working Group and was one of the foundation’s first members. He presents at major industry events and writes tech content for leading publications including TheNewStack, Hackernoon, DZone, Towards Data Science and more.

Workshop: MLOps & Automation Workshop: Bringing ML to Production in a Few Easy Steps

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Workshop: MLOps & Automation Workshop: Bringing ML to Production in a Few Easy Steps

Core Takeaways:

How to deploy ML/AI faster to production using various MLOps technologies

Prequestite Knowledge:

Familiarity with Jupyter Notebooks, Pandas, and common ML tools

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Vlad Voroninski

Partner & Co-Founder, INQ Data Law

Talk: Autonomous Vehicles - The Next Step Forward

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Talk: Autonomous Vehicles - The Next Step Forward

Abstract:

What You'll Learn:

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Randi Ludwig

Sr. Manager, Applied Data Science, Dell Technologies

Randi manages a team of data scientists at Dell Technologies within Support and Deployment Services who deliver data science solutions using telemetry data to proactively prevent customer issues and resolve them more quickly when they do happen. As a data scientist, she brought data science solutions to business problems involving tech support, warranties, and repairs on Dell products. She continues to focus on raising visibility for data science at the executive level and connecting global Dell data scientists into a networked community that can collaborate and learn from one another. Additionally, she is a co-organizer of Women in Data Science ATX and promotes diversity and fostering a welcoming space for newcomers to the field. Before venturing into industry, Randi completed a PhD in Astrophysics at UT Austin, including research on both active galactic nuclei and how students learn astronomy, which gave her experience with varied statistical data-mining techniques and many kinds of data sets.

Workshop: Managing Data Science in the Enterprise

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Workshop: Managing Data Science in the Enterprise

Core Takeaways:

Prequestite Knowledge:

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Ron Bodkin

VP of AI Engineering and CIO, Vector

Ron is the VP of AI Engineering and CIO at Vector Institute and is the Engineering Lead at the Schwartz Reisman Institute for Technology and Society. Ron is responsible for leading engineering teams that apply Vector’s leading AI research to industry and health care problems for Canada, establishing and supporting world class scientific computing infrastructure to scale the adoption of beneficial AI, and ensuring that all Vector users, sponsor participants and partners are upskilled to use it effectively.

Talk: Responsible AI

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Emeli Dral

CTO and Co-founder, Evidently AI

Emeli Dral is a Co-founder and Chief Technology Officer at Evidently AI, a startup developing tools to analyze and monitor the performance of machine learning models. Prior to that, she co-founded a startup focused on the application of machine learning in the industrial sector and served as the Chief Data Scientist at Yandex Data Factory. She led a team of accomplished data scientists and oversaw the development of machine learning solutions for various industries - from banking to manufacturing. Emeli is a lecturer at the Graduate School of Management of St. Petersburg State University and Harbour.Space University, where she teaches courses on machine learning and data analysis tools. In addition, she is a co-author of the Machine Learning and Data Analysis curriculum at Coursera with over 100,000 students. In 2017, she also co-founded Data Mining in Action, the largest open data science course in Russia.

Talk: How Your ML Model Will Fail - And How to Prepare for It?

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Talk: Responsible AI

Abstract:

Advances in AI promise tremendous benefits to society but also pose significant challenges. The Vector Institute is at the forefront of AI research and operationalizing AI, and collaborates closely with the Schwartz Reisman Institute for Technology and Society to advance the state of Responsible AI. As the field continues to advance, responsibility is becoming increasingly important to meet expectations of all stakeholders. Learn about challenges such as unintended user and societal harm, unfair bias, surveillance, adversarial attacks.

In this talk we look at challenges and emerging research and practices to address:

• Unintended consequences

• Model interpretability

• Fairness

• Engineering objectives (loss functions)

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Talk: How Your ML Model Will Fail - And How to Prepare for It?

Abstract:

The data scientist’s job does not finish when the model is shipped. Models degrade and break in production. The failure modes of machine learning systems are also different from those of traditional software applications. They require purpose-built monitoring and debugging. However, this aspect is often overlooked in practice. In this talk, we will explore:

- How and why the machine learning models break;

- How to analyze production model performance, data drift and monitor data quality;

- How to set up your monitoring strategy in a pragmatic way.

What You'll Learn:

How to set up your model monitoring from scratch, and how to prioritise different metrics

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Doug Creighton

Data Science Lead, Statflo Inc.

Doug Creighton is the Data Science Lead at Statflo. Statflo’s TextKit is a 1:1 business text messaging platform that works seamlessly with your existing systems.

Before working and understanding retail data, Doug worked as an analyst in the energy industry finding low-cost ways to get energy from A to B. Doug has spent his career solving innovative data problems to impact decision making internally or for his customers. He often spends a lot of his free time solving personal data problems or collaborating with others. His latest AI project at hopupon.com.

Talk: A Machine Learning based Privacy-Preserving Framework for Generating Synthetic Data from Aggregated Sources

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Talk: A Machine Learning based Privacy-Preserving Framework for Generating Synthetic Data from Aggregated Sources

Abstract:

What You'll Learn:

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Selika Josiah Talbott

Professorial Lecturer, American University

Selika is an innovative strategist, transportation executive, and motivational speaker. Currently researching and educating on electric and autonomous vehicle policy, impacts on governments, OEMs and regulatory stakeholder communities at American University in Washington, DC.

Selika formerly served as the co-chair for the FMCSA (Federal Motor Carrier Safety Administration) Automated Working Group and served on the AAMVA (American Association of Motor Vehicle Administrators) Automated Vehicle Work Group. From electric/autonomous vehicles to politics she has written and spoken about public policy regarding transportation, innovation and technology as well as provided strategic assistance for transportation associations and companies.

Selika was formerly the Appointed Deputy Administrator for the State of New Jersey Motor Vehicle Commission in charge of all Operations for state services, the Director of Field Operations for the FMCSA (Federal Motor Carrier Safety Administration) and has over 18 years’ experience as an attorney litigator and was most recently the Senior Advisor to the Administrator of the FMCSA.

She has spoken nationally and internationally providing unique strategies for companies and governments in a variety of policy areas. Selika currently serves on the PAVE (Partners for Autonomous Vehicle Education) Academic Advisory Counsel and has authored several articles for Forbes.com. Selika is a sought after subject matter expert including having been recently interviewed by Axios and Automotive News.

Talk: Political Economy of Future Transportation and Equity

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Talk: Political Economy of Future Transportation and Equity

Abstract:

The concept of the innovation and transportation and real world issues of public engagement, political policy and equity

What You'll Learn:

Policy considerations when designing for future mobility

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Lan Yao

Data Scientist, Loblaw Digital

Lan is a data scientist in the growth and marketing team at Loblaw digital. She holds Master's degrees from the University of Toronto and Imperial College London. Her work at Loblaw Digital includes developing a feature hub to serve multiple models, building customer models for campaign targeting, and implementing an auto model selection platform.

Talk: From Data Warehouse To Feature Warehouse: How DBT & AirFlow DAGs Orchestrate Building A Reliable & Reusable Feature Repository To Accelerate Model Development

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Talk: From Data Warehouse To Feature Warehouse: How DBT & AirFlow DAGs Orchestrate Building A Reliable & Reusable Feature Repository To Accelerate Model Development

Abstract:

At Loblaw Digital, we have abundant data resources. Yet, it takes a lot of processing before we can build predictive models or perform analysis on them. Data engineers, data analysts, and data scientists have to conduct time-consuming and repetitive tasks to understand the business logic within and across data components to get the desired features and datasets. Data discovery and data generation became the most challenging piece before putting ML solutions in production. To conquer these difficulties, we enrich millions of transactions from a variety of sources using data build tool (DBT) while ensuring quality checks. The pipelines are scheduled using AirFlow DAGs and they output in a single, scalable, consolidated repository. These features enable our teams to have a quicker turnaround time on our solutions' development.

What You'll Learn:

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Phoenix Unnayan Majumder

Senior Technology Leader & Platform Head-Data Engineer, Data Science & BI, Rogers Communications

Phoenix Majumder is an accomplished Data and Analytics leader, who has delivered cutting-edge analytics solutions that have provided a sound basis for informed business decisions. Adept in the execution of a wide spectrum of analytics capabilities that spreads across Analytics Strategy formulation to production-grade implementations. Phoenix thrives in delivering dependable data solutions and explaining complex analytical outcomes to a diverse set of audiences. He has lead implementation of Data and Analytics enablement technologies across a wide array of technology platforms. Excellent communicator, proven history of delivering impactful projects that have transformed analytical outcomes to enterprise strategies. Data Analytics Product Management, Machine Learning/ AI Operations, and Digital Transformation are few key areas of his recent focus. An Electrical Engineer by technical training, Phoenix holds graduate degrees in Business Administration and Analytics.

Talk: Lessons Learned Transitioning to the Cloud

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Talk: Lessons Learned Transitioning to the Cloud

Abstract:

The discussion will cover a broad-spectrum of considerations on moving Analytics journey to cloud. The design aspects of the technology and how the technology can accelerate the Data Science and Machine Learning use community.

What You'll Learn:

Technology Strategy

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Sheldon Fernandez

Darwin AI

Workshop: Founder's Circle

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Workshop: Founder's Circle

Core Takeaways:

Prequestite Knowledge:

What are the most imp lessons that AI practitioners and founders can learn from your experiences

What is the biggest blocker that’s blocking ML research and the adoption of AI/ML in the business world?

Is the technology side or business side harder in building an AI company?

Working with orgs who are new to AI and how to manage their expectations

What is the state of the art in ML and how can our audience benefit from it?

Role of the cloud provider and how it is evolving?

How does AI for good and sustainability play into your work?

How have VCs, accelerators and incubators helped you grow your startup?

Are folks doing ML the right way? What are ways we will see major improvement over the next 5-10 years?

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Rich Caruana

Principal Researcher, Microsoft Research

Workshop: Black-Box and Glass-Box Explanation in Machine

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Workshop: Black-Box and Glass-Box Explanation in Machine

Abstract:

The overall importance of intelligibility and explanation in machine learning.

New methods for providing explanations.

Glass-Box vs. Black-Box ML and explanation methods.

What You'll Learn:

Participants should be familiar with Supervised Machine Learning

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Sam Cooper

Phenomic AI

Workshop: Founder's Circle

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Workshop: Founder's Circle

Core Takeaways:

Prequestite Knowledge:

What are the most imp lessons that AI practitioners and founders can learn from your experiences

What is the biggest blocker that’s blocking ML research and the adoption of AI/ML in the business world?

Is the technology side or business side harder in building an AI company?

Working with orgs who are new to AI and how to manage their expectations

What is the state of the art in ML and how can our audience benefit from it?

Role of the cloud provider and how it is evolving?

How does AI for good and sustainability play into your work?

How have VCs, accelerators and incubators helped you grow your startup?

Are folks doing ML the right way? What are ways we will see major improvement over the next 5-10 years?

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Mikhail Klassen

Paladin AI

Workshop: Founder's Circle

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Workshop: Founder's Circle

Core Takeaways:

Prequestite Knowledge:

What are the most imp lessons that AI practitioners and founders can learn from your experiences

What is the biggest blocker that’s blocking ML research and the adoption of AI/ML in the business world?

Is the technology side or business side harder in building an AI company?

Working with orgs who are new to AI and how to manage their expectations

What is the state of the art in ML and how can our audience benefit from it?

Role of the cloud provider and how it is evolving?

How does AI for good and sustainability play into your work?

How have VCs, accelerators and incubators helped you grow your startup?

Are folks doing ML the right way? What are ways we will see major improvement over the next 5-10 years?

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Jennifer Prendki

Alectio

Workshop: Founder's Circle

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Workshop: Founder's Circle

Core Takeaways:

Prequestite Knowledge:

What are the most imp lessons that AI practitioners and founders can learn from your experiences

What is the biggest blocker that’s blocking ML research and the adoption of AI/ML in the business world?

Is the technology side or business side harder in building an AI company?

Working with orgs who are new to AI and how to manage their expectations

What is the state of the art in ML and how can our audience benefit from it?

Role of the cloud provider and how it is evolving?

How does AI for good and sustainability play into your work?

How have VCs, accelerators and incubators helped you grow your startup?

Are folks doing ML the right way? What are ways we will see major improvement over the next 5-10 years?

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Ari Kalfayan

AWS

Workshop: Founder's Circle

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Workshop: Founder's Circle

Core Takeaways:

Prequestite Knowledge:

What are the most imp lessons that AI practitioners and founders can learn from your experiences

What is the biggest blocker that’s blocking ML research and the adoption of AI/ML in the business world?

Is the technology side or business side harder in building an AI company?

Working with orgs who are new to AI and how to manage their expectations

What is the state of the art in ML and how can our audience benefit from it?

Role of the cloud provider and how it is evolving?

How does AI for good and sustainability play into your work?

How have VCs, accelerators and incubators helped you grow your startup?

Are folks doing ML the right way? What are ways we will see major improvement over the next 5-10 years?

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Talk: Artificial Intelligence for Molecular Design and Self Driving Labs

Abstract:

In this talk, I will review our group’s recent work in AI and automation for designing functional molecular materials.

What You'll Learn:

ML for chemistry and materials

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Jesika Briones

Senior Manager, Connected and Autonomous Vehicles, MaRS Discovery District

Jesika is responsible for the business and partnership development and program management activities of the Autonomous Vehicle Innovation Network's (AVIN) Toronto Regional Technology Development Site (RTDS). Located at MaRS, the Toronto RTDS provides business and technical resources and co-working space to artificial intelligence and machine learning startups who apply their solutions to the connected and autonomous vehicles space.

In her spare time, Jesika works as a strategic partnership consultant and advisor in the auto-tech sector and volunteers as an Advisory Council Member and Chair for Girls in Tech - Toronto Chapter. A global non-profit focused on the engagement, education, and empowerment of girls and women passionate about technology.

Jesika holds a bachelor's degree in manufacturing engineering with a specialization in Total Quality Management (TQM) and a Master of Engineering Entrepreneurship and Innovation (MEEI) from McMaster University. She is also a proud Action Canada Fellow alumni.

Talk: Political Economy of Future Transportation and Equity

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Talk: Political Economy of Future Transportation and Equity

Abstract:

The concept of the innovation and transportation and real world issues of public engagement, political policy and equity

What You'll Learn:

Policy considerations when designing for future mobility

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Ayeh Bandeh-Ahmadi

Advisor, Ex-U.S. Treasury

Ayeh Bandeh-Ahmadi has over two decades’ experience advising policy development at the Departments of Treasury, Defense, State, and Health and Human Services, and at the White House. Most recently, she served on a 10-person team responsible for managing U.S. Treasury’s $20 trillion of marketable debt throughout the COVID-19 outbreak and deployment of the CARES Act.

Prior to that, she represented Treasury as its expert on financial applications of artificial intelligence and machine learning to the international Financial Stability Board in Switzerland and briefly served as Program Manager for OFR’s grant programs on the study of financial computation.

Before coming to Treasury she studied backer behavior and what makes projects successful at Kickstarter. Her work designing methods to map out technology landscapes of startups and their investors at Quid was featured in BusinessWeek, the Harvard Business Review, and The Telegraph.

Abstract:

This talk will highlight some roles machine learning is playing in finance and financial markets today -- from the aftermath of COVID-19 to lending applications, and how policy makers and practitioners might use machine learning to promote financial stability and build more sustainable business models.

What You'll Learn:

(1) some measurements showing the extent to which financial markets and traders dependent on machine learning algorithms were affected in the aftermath of COVID-19, (2) examples of how machine learning tools can be used to address macro shifts in the financial system, and (3) considerations for building financial algorithms that are more robust to changing regulatory policies

Talk: A Former Government Economist Discusses Opportunities at The Intersection of Financial Policy and Machine Learning

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Dhagash Mehta

Senior Manager, Machine Learning and Asset Allocation, Vanguard

Dr Dhagash Mehta is a Senior Investment Strategies Manager (Machine Learning and Asset Allocation) at Investment Strategies Group at Vanguard, and prior to that was a Principal Research Data Scientist at Vanguard. Dr Mehta is an Editorial Board Member at the Journal of Financial Data Science (https://jfds.pm-research.com/). Dr. Mehta pursued his undergraduate studies in Physics in India, followed by Part III of Mathematical Tripos at the University of Cambridge, and Ph.D. in theoretical particle physics from the University of Adelaide (Australia) as well as Imperial College London (UK).

Before joining Vanguard, he was a Senior Research Scientist at United Technologies Research Center (now called Raytheon Technology Research Center), and prior to that a Research Professor at Department of Applied and Computational Mathematics and Statistics at University of Notre Dame. He has held multiple research positions at various research institutes such as Fields Institute in Toronto, Simons Institute for Theory of Computing at Berkeley, the University of Cambridge (UK), Imperial College London (UK), the University of Adelaide (Australia), North Carolina State University (USA), Syracuse University (USA) and National University of Ireland Maynooth (Ireland).

Dr. Mehta's areas of expertise are theory of machine/deep learning, and applications of machine learning in finance.. In particular, he has published 75+ research papers in reputed journals on optimization (convex and nonconvex), computational algebraic geometry, numerical analysis, network science and machine learning to solve various problems arising in financial services and wealth/asset management (and in the past, power systems and control theory; theoretical physics, jet-engines, and smart building systems).

Abstract:

Identifying similar mutual funds (including exchange-traded funds) with respect to the underlying portfolios has found many applications in fund recommender systems, competitors analysis, marketing and sales of the products.

The traditional methods are either qualitative, and hence prune to biases and often not reproducible,or, are known not to capture all the nuances (non-linearities) among the portfolios from the raw data. We propose a radically new approach based on the weighted bipartite network representation of funds and their underlying assets data using a sophisticated machine learning method called Node2Vec to learn an embedded low-dimensional representation of the network. We use this network embedding to identify similar portfolios by computing node similarities in the representation space, which we callFund2Vec.

Our approach provides novel insights to theportfolio similarity problem as well as a data-driven method to remove bias from qualitative categorizations available in the market. Ours is also the first ever study of the weighted bipartite network representation of the funds-assets network.

What You'll Learn:

Machine learning for comparative analysis of mutual funds using machine learning.

Talk: Fund2Vec: Mutual Funds Similarity Using Graph Learning

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Diego Klabjan

Professor, Northwestern University

Diego Klabjan is a professor at Northwestern University, Department of Industrial Engineering and Management Sciences. He is also Founding Director, Master of Science in Analytics and Director, Center for Deep Learning.

After obtaining his doctorate from the School of Industrial and Systems Engineering of the Georgia Institute of Technology in 1999 in Algorithms, Combinatorics, and Optimization, in the same year he joined the University of Illinois at Urbana-Champaign. In 2007 he became an associate professor at Northwestern and in 2012 he was promoted to a full professor.

His research is focused on machine learning, deep learning and analytics (modeling, methodologies, theoretical results) with concentration in finance, manufacturing, insurance, sports, and bioinformatics. Professor Klabjan has led projects with large companies such as Intel, Baxter, Allstate, AbbVie and many others, and he is also assisting numerous start-ups with their analytics needs. He is also a co-founder of Opex Analytics LLC, a Coupa company and is now involved with his next start-up.

Abstract:

Recurrent neural networks and transformers are well suited for temporal data and sequences however their performance can be improved by using novel concepts. We take a deeper dive into how to output only confident predictions in a dynamic fashion. Another family of models discussed are adaptive computational time that remedy some of the challenges related to time series data.

These models dynamically allocate the number of layers in each time and thus the hardness of computation in each time. We will present them in context of sequence-to-sequence with attention. Data also usually has sparse features with respect to time with tailored models accounting for them. The results are discussed on proprietary and public data sets related to financial instruments.

What You'll Learn:

Textbook deep learning models of course work but their performance can be improved with tailored approaches for data and problem in question. Prediction of financial instruments is such an example. You will learn advanced modeling and algorithmic techniques for financial data and the gains obtained by using tailored approaches.

Talk: Beyond Standard Deep Learning Models for Time Series and Sequences

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Simona Gandrabur

AI & Innovation Strategy Lead, National Bank of Canada

Simona Gandrabur has been working in the general field of AI for close to 20 years, most notably in areas related to processing of human languages – such as automatic speech recognition, natural language understanding, machine translation and conversational reasoning. Her experience ranges from many years in research, in the development of smart assistant applications, to defining strategy of AI-based offers. She is currently the head of AI strategy within the Wealth Management division of the National Bank of Canada.

Abstract:

A pandemic, a world economy put on stand-by, radically changed work environments : how AI application in banking stepped up or had to be adapted to suit a world that radically changed onver night.

What You'll Learn:

Innovation Strategy. Business adaptation. Applied AI solutions.

Talk: Covid-19 Impact on AI in Banking

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Mehrnaz Shokrollahi

Senior Data Scientist, PureFacts Financial Solutions

Mehrnaz Shokrollahi is currently a Senior Data Scientist at PureFacts Financial Solutions where she transforms financial data into reliable Artificial Intelligence. She has developed multiple algorithms and use-cases for the financial institutes like boutique firms. Alongside her work on PureFacts she is a member of the advisory board on Queens University’s InQUbate Program, first student-run AI startup incubator.

She also holds a position in IEEE Canada with the title of Chair of the IEEE Signal Processing Section Toronto Chapter. Since completing her PhD from Ryerson University in 2015, Mehrnaz has had various positions in different industries including healthcare, manufacturing and mobile app and focused solely on developing AI solutions. She has also authored numerous papers and been awarded the prestigious scholarships including Mitacs Postdoctoral Award.

Abstract:

We are trying to solve one of biggest problem of the wealth management firms, client retention, using computer vision. In our design we assume that using historical data enables us to understand the past and allows us to predict the future which is detection of client’s churn. What we are looking for is finding what caused the client to leave the firm and what prior information led him/her to make that decision. To do this we proposed to collect all the information about the client over all time (or a specific period).

In order to achieve our objective, we proposed to convert our data which includes all the history about each client over time into images, and then apply deep neural network to predict client churn. This method allows us to include all our historical data to identify whether a client is going to leave the firm or not based on periodical information we have for every client.

We have seen that the performance of the model is comparable with other methods like Lightgbm and this performance can be boosted as we increase our dataset.

What You'll Learn:

In this talk we will demonstrate our novel method of detecting client churn based on image processing. We also talk about the challenges that we are facing in the financial world and how we overcome problems like historical data, time information, imbalance dataset and not having access to many data points.

Talk: Financial Client Churn Detection Using Computer Vision

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Mark Weber

MIT-IBM Watson AI Lab, Strategy & Operations Lead, IBM

Mark Weber is (@markrweber) is the IBM Research Strategy & Operations Lead for the MIT-IBM Watson AI Lab, a community of over 200 scientists dedicated to pushing the frontiers of artificial intelligence. Through the lab’s corporate membership program, which he leads, Mark works closely with global leaders across multiple industries to harness the power of AI science for real-world impact. A former fellow at the MIT Legatum Center for Entrepreneurship & Development, Mark serves as an advisor to the International Monetary Fund’s Innovation Lab, the Harambe Alliance of African Entrepreneurs, and the Traffik Analysis Hub using data and AI to fight human trafficking.

As a researcher, Mark has published works on blockchain technology for supply chain finance, graph deep learning for anti-money laundering, and algorithmic fairness for anti-discrimination in lending. Prior to IBM Research, Mark earned his MBA in finance from MIT Sloan and worked as a graduate researcher at the MIT Media Lab’s Digital Currency Initiative, where he led the development of the open-source blockchain protocol b_verify for public registries with with the Mexican government and Inter-American Development Bank, and oversaw working groups with member companies.

In a previous life, Mark produced documentary films on poverty and development, most notably the award-winning documentary Poverty, Inc., which received international acclaim and distribution on Netflix, Amazon, and television stations around the world. In his personal life, Mark enjoys reading, chess, ultramarathon mountain running, and dual sport motorcycling.

Abstract:

Black Lives Matter. These three words have loomed large in the sociopolitical psyche of the United States since the founding of the movement in 2013.

The #BLM movement, distinct from any specific organization, is principally focused on combatting police brutality, mass incarceration, and other forms of violence resulting from structural and cultural racism. Freedom from violence is among the most basic human needs articulated in Maslow’s Hierarchy. Another is financial security.

Today in the United States, African Americans continue to suffer from financial exclusion and predatory lending practices. Meanwhile the advent of machine learning in financial services offers both promise and peril as we strive to insulate artificial intelligence from our own biases baked into the historical data we need to train our algorithms. In our paper, Black Loans Matter: Distributionally Robust Fairness for Fighting Subgroup Discrimination, published in the Fair AI in Finance workshop of the 2020 NeurIPS conference, we evaluate this challenge for AI fairness in lending.

We focus on a critical vulnerability in the group fairness approach enforced in banking today. Insofar as we want similar individuals to be treated similarly, irrespective of the color of their skin, today’s group-level statistical parity measures fail because algorithms can learn to discriminate against subgroups. We recommend an alternative approach drawing from the literature on individual fairness, including state-of-the-art methods published in 2020 in top AI conferences.

Specifically, we propose Distributionally Robust Fairness (DRF) and an efficient training algorithm called SenSR to enforce it, where SenSR also relies on an algorithm called EXPLORE to learn a fair metric from the data. These methods have been validated in the AI peer community; now they need to be validated in the real world. We hope this paper will serve as but the first step in the right direction.

What You'll Learn:

Why today's group fairness algorithms can result in blatantly unfair outcomes (and what we can do about it)

Talk: Black Loans Matter: Algorithms for Racial Justice

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Jenny Ni Zhan

PhD student, Carnegie Mellon University

Jenny Ni Zhan is a fourth year PhD student at Carnegie Mellon University. Her PhD research is about atomic simulations of liquid alloys, and machine learning methods to aid the simulations and their analysis. She has published research on machine learning for finance topics including graphical models for portfolio selection and modeling bank deposits using bank financial data and macroeconomic variables. She worked at JPMorgan Chase as a quantitative analyst intern and at Eastman Chemical Company as a chemical engineer.

Abstract:

We examine a variety of graphical models to construct optimal portfolios. Graphical models such as PCA-KMeans, autoencoders, dynamic clustering, and structural learning can capture the time varying patterns in the covariance matrix and allow the creation of an optimal and robust portfolio. We compared the resulting portfolios from the different models with baseline methods. In many cases our graphical strategies generated steadily increasing returns with low risk and outgrew the S&P 500 index. This work suggests that graphical models can effectively learn the temporal dependencies in time series data and are proved useful in asset management.

What You'll Learn:

A very natural motivation in finance is selecting portfolios with high returns and low risk. The risk of a portfolio is determined by the covariance between assets in the portfolio, and the covariances also change over time. We show how graphical methods can be used to determine structure correlation between assets, and create and test portfolio selection strategies in simulation backtest. This talk is based on paper “Graphical Models for Financial Time Series and Portfolio Selection” by Ni Zhan, Yijia Sun, et. al.

Talk: Graphical Models For Financial Time Series and Portfolio Selection

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Agus Sudjianto

EVP, Head of Corporate Model Risk, Wells Fargo

Agus Sudjianto is an executive vice president, head of Model Risk and a member of Management Committee at Wells Fargo, where he is responsible for enterprise model risk management.

Prior to his current position, Agus was the modeling and analytics director and chief model risk officer at Lloyds Banking Group in the United Kingdom. Before joining Lloyds, he was an executive and head of Quantitative Risk at Bank of America. Prior to his career in banking, he was a product design manager in the Powertrain Division of Ford Motor Company.

Agus holds several U.S. patents in both finance and engineering. He has published numerous technical papers and is a co-author of Design and Modeling for Computer Experiments. His technical expertise and interests include quantitative risk, particularly credit risk modeling, machine learning and computational statistics.

He holds masters and doctorate degrees in engineering and management from Wayne State University and the Massachusetts Institute of Technology.

Abstract:

All models are wrong and when they are wrong they create financial or non-financial harm. Understanding, testing and managing potential model failures and their unintended consequences are the key focus of model risk management, particularly for mission critical or regulated applications.

This is a challenging task for complex machine learning models and having an explainable model is a key enabler. Machine learning explainability has become an active area of academic research and an industry in its own right.

Despite all the progress that has been made, machine learning explainers are still fraught with weakness and complexity. In this talk, I will argue that what we need is an interpretable machine learning model, one that is self-explanatory and inherently interpretable. I will discuss how to make sophisticated machine learning models such as Neural networks (Deep Learning) as self-explanatory models.

What You'll Learn:

How to make sophisticated machine learning models such as Neural networks (Deep Learning) as self-explanatory models.

Talk: What We Need is Interpretable Self-Explanatory Machine Learning Models

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Susan Shu Chang

Principal Data Scientist, Clearbanc

Susan is currently a data scientist in fintech. She builds and ships machine learning at scale. On the side, she founded a profitable video game studio. She is a frequent speaker at machine learning as well as game development conferences, and writes career guides at susanshu.com.

Abstract:

What we learned from deploying inference on-demand, and best practices for integrating machine learning into a core business functionality.

What You'll Learn:

What we learned from deploying inference on-demand, and best practices for integrating machine learning into a core business functionality.

Talk: Machine Learning Deployment and Product Integration

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Kyle Caverly

Machine Learning Researcher, RiskLab Toronto

Kyle Caverly is a Machine Learning Researcher at RiskLab Toronto, and an Investment Data Scientist with OMERS Capital Markets. His work focuses on applied Machine Learning in Portfolio Construction and Risk Management, with a particular interest in AI systems and Natural Language.

Abstract:

Investment firms are now more than ever cognizant of the public's reaction and expectation surrounding the Environmental, Social and ethical implications of their investments. As such, it is becoming more important for Financial firms to be able to incorporate dynamic ESG metrics into their investment processes.

This talk will focus on how Natural Language models can be leveraged to automatically adjust and influence portfolio construction methods away from sensitive ESG topics. I will touch on two primary conversation pieces:

1. Why we believe NLP tools will be important for automatic portfolio construction methods inside the ESG space?""

2. How can we incorporate publicly available news sources to generate a portfolio adjusted for ESG topics?

This will include a discussion of recent research from Risklab - Toronto, ""Weak Supervision and Black-Litterman for Automated ESG Portfolio Construction."" In which, we propose an approach that combines modern machine learning techniques in Natural Language combined with portfolio optimization to incorporate views of company ESG performance, curated automatically using large scale news data, into portfolio management decisions. Technically - this will include discussion of the application of Transformer based classification algorithms, trained under weak supervision into a Black-Litterman Portfolio Optimization process.

What You'll Learn:

Why we should explore Natural Language applications in Finance, and how we can incorporate news data in automated Portfolio Construction.

Talk: Natural Language Processing for Automated ESG Portfolio Construction

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Andrew Green

Managing Director and Lead XVA Quant, Scotiabank

Andrew Green is a Managing Director and lead XVA Quant at Scotiabank in London. He is the author of XVA: Credit, Funding and Capital Valuation Adjustments which is published by Wiley, co-editor of Landmarks in XVA which is published by Risk Books and co-author of a number of technical articles on XVA in recent years.

Abstract:

XVAs models are amongst the most computationally intensive in finance and require the use of acceleration techniques such as GPU computation and Adjoint Algorithmic Differentiation (AAD). Deep learning provides a computationally efficient and implementation friendly way to approximate derivative valuation function, a critical component of XVA models. This presentations shows how deep learning dovetails with other traditional quantitative finance models to deliver an effective XVA calculation platform.

What You'll Learn:

The application of Deep Learning to Derivative Valuation Adjustments.

Talk: Deep XVA

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Avi Schwarzschild

PhD Student, University of Maryland,

Avi is a PhD student in the Applied Math and Scientific Computation program at the University of Maryland. He is advised by Dr. Tom Goldstein on his work in AI security, relating to data security and model vulnerability.

Abstract:

Algorithmic trading systems are often completely automated, and deep learning is increasingly receiving attention in this domain. Nonetheless, little is known about the robustness properties of these models to small but deliberate orders that break them.

We study valuation models for algorithmic trading from the perspective of adversarial machine learning. We introduce new adversarial attacks specific to this domain with size constraints that minimize costs.

We further discuss how these attacks can be used as an analysis tool to study and evaluate the robustness properties of financial models. Finally, we investigate the feasibility of realistic adversarial attacks in which an adversarial trader fools automated trading systems into making inaccurate predictions.

What You'll Learn:

In this talk, automated stock price prediction models are looked at through the lens of adversarial machine learning.

You'll learn how neural network based programs may be vulnerable to malicious activity. I will also describe ways to study valuation models using similar attacks.

Talk: Analyzing the Security of Machine Learning for Algorithmic Trading

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Peng Cheng

Head of Machine Learning Strategies, JP Morgan

As a member of JPMorgan Global Research, Peng Cheng leads the Big Data and AI Strategies research effort based in New York, and is responsible for developing cross asset investment strategies leveraging alternative data and advanced statistical techniques.

Charles Elkan

University of California, San Diego

Charles Elkan is the founder and CEO of a fintech company that has not yet been announced publicly. He is also an adjunct professor of computer science at the University of California, San Diego, where he was previously a tenured full professor.

Until 2020 he was a managing director and the global head of machine learning at Goldman Sachs in New York, while from 2014 to 2018 he was the first Amazon Fellow, leading a team of over 30 scientists and engineers in Seattle, Palo Alto, and New York doing research and development in machine learning for both e-commerce and cloud computing. Professor Elkan earned his doctorate at Cornell University and his undergraduate degree at Cambridge University.

His students have gone on to faculty positions at universities that include Columbia and Stanford, and to leading roles in industry that include managing the largest app store in the world.

Abstract:

Machine learning (ML) as an academic research field is over 60 years old. So why is there so much excitement about it nowadays in the business world? What can the technology really do now that was impossible ten years ago? In what ways are humans still fundamentally superior? If we want to apply ML in trading or in banking, where are the best opportunities? What are ten different traps to avoid falling intro? How should an applied ML project be directed and organized? Should we use deep learning? This talk will provide answers, hopefully reasoned, to these questions.

What You'll Learn:

The audience will gain insights about which applications of machine learning in finance are likely to be successful.

Talk: Machine Learning in Finance: Lessons Learned

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Nassim Tayari

Head, Personal and Commercial Banking, BorealisAI

Abstract:

In recent years most companies are being forced to innovate and many are particularly excited about Machine learning applications. However many companies find themselves struggling to advance their ML strategy. The focus of this talk is on ML product strategy and we can build meaningful and impactful ML product roadmaps. This presentation will address certain important questions including

What a product strategy is?

Why an ML product strategy is important?

And how to create a product strategy?

What You'll Learn:

What is Product strategy? Why ML product strategy matters?

How to craft Impactful and Actionable ML product strategy

Frameworks and tips on measuring what matters most while developing a strategy

Talk: Craft, Communicate and Deliver an Impactful ML Product Strategy- Observations and Learnings

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Dean Van Asseldonk

Data Science Manager, Borrowell

Hello everyone, my name is Dean Van Asseldonk. I've been working at FinTech startup Borrowell as a Data Science Manager for the past 4 years, focused on Recommender Systems and Personalization for financial products and content. I'm a Bachelor of Mathematics graduate from the University of Waterloo. In my spare time I enjoy downhill skiing and the tribulations of sourdough bread making. Looking forward to presenting about ML Feature Stores on March 23rd.

Abstract:

The speaker will present on a Machine Learning Feature Store at Borrowell including;

Importance of Feature Store

Versioning of feature store

Automatic updates to data

Visualizing feature store in Looker

Efficiency improvements for future projects

Demo

Machine Learning and Data Mining Researcher and Practitioner, Datatron

Rohan Khade is a machine learning and data mining researcher and practitioner with 10 years of experience working in academia, large organizations, and startups. His research led to the development of novel machine learning algorithms for streaming data. At Datatron, Rohan worked on building the governance platform and designed algorithms and systems for detecting bias, drift and anomalies in near real time for hundreds of models in production.

Abstract:

The key factors driving model risk are increased complexity, regulatory compliance, and adverse business impact. In this session, Datatron will address how model risk management mitigates these risks and ensures that models are operating as designed.

Senior Director of Data Science, Comcast

Venkata Gunnu is a senior director of data science at Comcast, where he manages data science and data engineering teams and architects data science projects that process and analyze billions of messages a day and petabytes of data. Venkata is a leader in data science democratization with 15+ years of data science modeling, design, architect, consultant, entrepreneur, and development experience, and 10+ years of that in data science modeling, big data and the cloud. He earned a master’s in information systems management in project planning and management from Central Queensland University, Australia. He has experience with product evangelization and speaking at conferences, user groups.

Abstract:

As artificial intelligence (AI) and machine learning (ML) evolve and more organizations seek to become insight-driven, it is increasingly clear that solutions powered by AI/ML models are becoming critical

to improving business decisions. Attend this session to hear from AI/ML thought leaders on:

Challenges facing AI/ML leaders.

The need for MLOps in demonstrating quick wins.

The Future of AI/ML Initiatives.

What You'll Learn:

Talk: MLOps: Realizing the Full Potential of AI/ML

Talk: Unleashing the Value of Customer Intent with Intelligent Interventions

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Maia Brenner

AI Specialist, Tryolabs

Maia Brenner is a passionate data scientist and economist with strong programming skills, a mathematical and statistical background, and work experience in consulting and the public sector. As an AI Specialist at Tryolabs she helps clients to utilize the full potential of data science and machine learning to solve their business problems. Maia's experience in the consulting industry covers several projects related to demand forecasting, price optimization, customer segmentation, and natural language processing, among others. She is also a professor and enjoys working on initiatives of AI4SocialGood. She has helped in the application of Machine Learning to improve the Public Education sector and is involved in Gender Inequality research groups.

Abstract:

Setting the right price for a good or service is an old problem in economic theory, marketing, and business practices. However, as companies have more data than ever about their business and their customers, the world is moving towards new data-driven pricing strategies.

Machine learning tools allow us to incorporate structured and unstructured data in order to get accurate demand forecasts. But most importantly, with Machine Learning tools we can learn, for example, the price elasticity of demand for each SKU on each location and day, and therefore understand the willingness to pay of each customer. With these demand curves estimation and optimization algorithms we can learn which is the right price to set at each moment in time. In addition, with explainable AI tools and with randomized control trial experiments we can get new business insights, and deliver high impact price recommendations.

In this presentation, we will focus on how Machine learning is reshaping price optimization and will show outstanding results obtained by leading retailers around the globe.

In particular, we will walk you through our Machine Learning approach, which helped a Luxury Retail Company boost its gross margin by 28%.

We will share the algorithms, techniques, and data sources applied, which led our client to gain over half a million dollars during a 10-weeks price optimization experiment.

What You'll Learn:

State-of-the-art ML pricing solutions currently applied in the industry.

Algorithms, technics and data sources used.

Talk: Boosting Retail Margins: Price Optimization with ML

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