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Conference Workshops

Nov 15-16th

Add Ticket holders can join all workshops listed (Some interactive sessions may have limited seating. All attendee will receive videos and material post-event.)

november

15nov10:00 AM11:00 AMAI Inference Workloads: Solving MLOps Challenges in ProductionGuy Salton - Solutions Engineering Lead, Run:AI10:00 AM - 11:00 AM

15nov10:00 AM11:30 AMNLP in EcommerceMathangi Sri - Vice President Data Science10:00 AM - 11:30 AM

15nov10:00 AM12:00 PMNLP Without A Ready-Made Labeled DatasetSowmya Vajjala - Researcher, National Research Council, Canada10:00 AM - 12:00 PM

15nov12:00 PM1:30 PMMLOps without Much OpsJacopo Tagliabue - Lead AI Scientist, Coveo12:00 PM - 1:30 PM

15nov12:00 PM3:00 PMBeginning your Journey with AI Art!Dr. Kirell Benzi - Data Artist | Researcher, EPFL12:00 PM - 3:00 PM

15nov12:00 PM3:00 PMDevelop and Deploy ML Projects with Metaflow and SeldonVille Tuulos - Co-Founder, CEO & Oleg Avdeev - Co-Founder, Outerbounds | Clive Cox - CTO & Alejandro Saucedo - Engineering Director & Adrián González Martín - Machine Learning Engineer, Seldon12:00 PM - 3:00 PM

15nov2:00 PM3:30 PMDynaTask, A New Open Source Approach for AI BenchmarkingSam Lightstone - Software Engineer2:00 PM - 3:30 PM

15nov2:00 PM4:30 PMMoving Beyond Average: Adopting Inclusive Design into Business Practices Policies, and SystemsDr. Deirdre Kelly - Leadership Specialist, NAV CANADA2:00 PM - 4:30 PM

15nov2:00 PM6:00 PMFrom Concept to Production: Template for the Entire ML JourneyChanchal Chatterjee - Cloud AI Leader & Elvin Zhu - AI Engineer, Google2:00 PM - 6:00 PM

15nov4:00 PM5:00 PMTrain your first NLP Transformer Model with Amazon SageMaker and Hugging FaceMark McQuade - ML Success & Business Development Lead & Philipp Schmid - Machine Learning Engineer and Tech Lead, Hugging Face 🤗4:00 PM - 5:00 PM

15nov4:00 PM5:30 PMTowards Observability for Machine Learning PipelinesShreya Shankar - Ph.D. Student, UC Berkeley4:00 PM - 5:30 PM

16nov10:00 AM12:00 PMUnlocking the Potential of Unstructured Data in Finance Through Document IntelligenceRahul Ghosh - Vice President & Himanshu Sharad Bhatt - Research Director10:00 AM - 12:00 PM

16nov10:00 AM1:00 PMIntroduction to Data Analysis Using PandasStefanie Molin - Data Scientist / Software Engineer | Author of Hands-On Data Analysis with Pandas, Bloomberg10:00 AM - 1:00 PM

16nov10:00 AM1:00 PMModern NLP: Learning to Apply Real Use CasesLeonardo De Marchi - Head of Data Science and Analytics, Financial Start-up Stealth10:00 AM - 1:00 PM

16nov2:00 PM4:00 PMManaging AI/ML Model RiskJonathan Ouimet - Sales Engineer2:00 PM - 4:00 PM

16nov4:00 PM5:30 PMDeep AutoViML For Tensorflow Models and MLOps WorkflowsRam Seshadri - Machine Learning Program Manager, Google4:00 PM - 5:30 PM

Conference Presentations

Nov 17-18th

All sessions are live and interactive and will be available for all ticket holders. Videos will be sent to attendees to re-watch.

november

17nov10:05 AM10:50 AMResearchers Gone WildAdam Harvey - Independent Researcher10:05 AM - 10:50 AM

17nov10:55 AM11:40 AMTrade-Off between Optimality and ExplainabilityNima Safaei - Senior Data Scientist & Taha Jaffer - Lead Data Scientist10:55 AM - 11:40 AM

17nov10:55 AM11:40 AMTowards Machine Intelligence Capable of Nobel-Caliber ScienceAlexander Lavin - Founder & Chief Technologist10:55 AM - 11:40 AM

17nov10:55 AM11:40 AMLeadership of Responsible AI – The Case for Inclusive TechElizabeth Adams - Chief AI Ethics & Culture Advisor/Affiliate Fellow Lead Venture Partner10:55 AM - 11:40 AM

17nov12:10 PM12:40 PMUnsolved Problems in Human-in-the-Loop Machine LearningRobert Monarch - Author, Human-in-the-Loop Machine Learning12:10 PM - 12:40 PM

17nov12:10 PM12:40 PMHow HelloFresh Leverages Feature Engineering and Modelling Techniques to Inform Menu DesignDelina Ivanova - Senior Manager, Data, Analytics & Insights12:10 PM - 12:40 PM

17nov12:45 PM1:30 PMLeveraging Novel Computer Vision and Machine Learning Solutions for Visual Inspection at Canadian National Railway (CN)Ashley Varghese - Data Scientist12:45 PM - 1:30 PM

17nov12:45 PM1:30 PMGraph Neural Networks with Almost No FeaturesEmanuele Rossi - Machine Learning Researcher12:45 PM - 1:30 PM

17nov2:15 PM3:00 PMSemantic Scholar, NLP, and the Fight Against COVID-19Oren Etzioni - CEO2:15 PM - 3:00 PM

17nov4:05 PM4:50 PMObtaining Answers from Social Media DataAlon Halevy - Director, Facebook AI4:05 PM - 4:50 PM

17nov4:55 PM5:40 PMHow Can You Trust Machine Learning?Carlos Guestrin - Professor4:55 PM - 5:40 PM

18nov9:15 AM10:00 AMExploring Opportunities and Challenges for AI in Digital GovernmentsAlex Benay - Global Lead, Government Azure Strategy & Former Government CIO, Canada & Siim Sikkut - Government CIO of Estonia9:15 AM - 10:00 AM

18nov10:05 AM10:50 AMRecommendation Systems for Digital Out of Home AdvertisingNataliya Portman - Senior Data Scientist10:05 AM - 10:50 AM

18nov10:55 AM11:40 AMCost Reduction Methods for Machine Learning in ProductionGeorge Seif - Machine Learning Engineer10:55 AM - 11:40 AM

18nov10:55 AM11:40 AMModeling Individuals without Data via a Secondary Task Transfer Learning MethodMatthew Guzdial - Assistant Professor and CIFAR AI Chair & Anmol Mahajan10:55 AM - 11:40 AM

18nov12:45 PM1:30 PMThe Technologists are Not in Control: What the Internet Experience Can Teach Us About AI Ethics and ResponsibilityNatalie Klym - VP Market Development & David Clark, Senior Research Scientist12:45 PM - 1:30 PM

18nov3:05 PM3:35 PMPhysics-Informed Machine Learning Methods for Materials DevelopmentMaryam Emami - CEO3:05 PM - 3:35 PM

18nov4:05 PM4:50 PMConstructing a Knowledge Graph for the World's Largest Professional NetworkQi He - Senior Director of Engineering at LinkedIn, ACM Distinguished Member4:05 PM - 4:50 PM

18nov4:55 PM5:40 PMSharing L'Oréal Canada's Bespoke Data Science Strategy FrameworkLudovic Begue - CRM & Data Science Director & Mohamed Sabri - Consultant in MLOps4:55 PM - 5:40 PM

18nov4:55 PM5:40 PMDeploying Transformers at Scale: Addressing Challenges and Increasing PerformancePieter Luitjens - Co-founder & CTO4:55 PM - 5:40 PM

Taken from the real-life experiences of our global ML audience and with over 300+ submissions, the Steering Committee has selected the top applications, achievements and knowledge-areas to highlight.

Come expand your network with machine learning experts and further your own personal & professional development in this exciting and rewarding field.

Each ticket includes:

  • Access 80+ hours of live-streamed content (incl. recordings)
  • Talks for beginners/intermediate & advanced
  • Network and connect through our event app
  • Q+A with speakers
  • Channels to share your work with community
  • Run your chat groups and virtual gatherings!

Hands-on Workshops: Bonus workshops take place November 16th-17th and require separate signup once registered
(Limited Availability for Some Workshops)

  • Put Deep Learning to Work: Accelerate Deep Learning Through AWS SageMaker and ML Services
  • Managing Data Science in the Enterprise
  • Knowledge Graph Recommendation Systems For COVID-19
  • Leveraging Pretrained Language Models For Natural Language Understanding
  • Reaching Lightspeed Data Science: ETL, ML, And Graph with NVIDIA RAPIDS
  • Intrusion Detection Systems – An Overview
  • Building A MovieLens Recommender System
  • Build, Train, And Deploy Models with Amazon SageMaker
  • Machine Learning Simplified: From Ideation To Deployment in Minutes with Automated Machine Learning
  • Computer Vision In Practice – Building An End-to-End Pipeline for Object Detection And Segmentation
  • How To Automate Machine Learning With GitHub Actions
  • Docker-Based Workflow for Deploying a Machine Learning Model
  • Introduction to Tensorflow (Hands-on Workshop)
  • Deploying Deep Learning Models to the Edge
  • MLOps & Automation Workshop: Bringing ML to Production in a Few Easy Steps
Please note conference talks will be held on Hopin.to conference platform. Those who have registered, will receive that link to join.

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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

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

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

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 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

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 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

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

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

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

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 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 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

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.

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

Abstract:

Applied machine learning has the potential to transform healthcare, particularly in the areas of automation, prediction, and optimization. However, numerous challenges to the acquisition, storage, and utilization of data as well as the development of practical machine learning algorithms and change management principles need to be considered. This talk will provide an overview of the process of applying ML into healthcare and the legal and ethical considerations needed for data access and application.

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.

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Talk: Raising the Quality of Online Conversations with Machine Learning

Abstract:

The quality of online comments is critical to the Washington Post. Learn how they built a machine learning system for automatically moderating comments from millions of readers. We will share the technical challenges with building the comment moderation platform and how we raised the quality of online conversations with machine learning.

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

Abstract:

There are high expectatios about AI initiatives across different industries in North America. However, too often results have been disappointing producing some backlash against digital transformation efforts. The capacity to implment and demonstrate high ROI AI projects changes this dynamic. This talk will delve into Banorte's transformation journey into an AI enhanced organization with data science projects yielding a net revenue that exceeds 3 billion USD during the past five years and avoiding the transformational fatigue.

What You'll Learn:

1. How to measure AI contribution to the bottom line
2. What are key prerequisites to focus yield high ROI on AI projects
3. Where to focus AI inititiatives to have a large organizational impact: revenue or cost?

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Talk: Inclusive Search and Recommendations

Abstract:

Machine learning powers many advanced search and recommendation systems, and user experience strongly depends on how well ML systems perform across all data segments. This performance can be impacted by biases, which can lead to a subpar experience for subsets of users, content providers, applications or use cases. Biases may arise at different stages in machine learning systems, from existing societal biases in the data, to biases introduced by the data collection or modeling processes. These biases may impact the performance of various components of ML systems, from offline training, to evaluation and online serving in production systems. Specific techniques have been developed to help reduce bias at each stage of an ML system. We will describe sources of bias in ML technology, why addressing bias matters, and techniques to mitigate bias, with examples from our work on inclusive AI at Pinterest. Mitigating bias in machine learning systems is crucial to successfully achieve our mission to "bring everyone the inspiration to create a life they love".

What You'll Learn:

We will describe sources of bias in ML technology, why addressing bias matters, and techniques to mitigate bias, with examples from our work on inclusive AI at Pinterest.

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Talk: Harnessing Geospatial Data for Machine Learning

Abstract:

Working with and analyzing geospatial data requires a different and often nuanced approach from most data types, especially to derive spatial predictions and detect patterns using machine learning applications. Many data scientists and analysts are not used to fully leveraging the power of geospatial data, and often don't know what business questions to ask, aren't aware of which algorithms are available to them to enrich their models, or resort to eliminating spatial variables entirely in order to use the data with common machine learning algorithms.

What You'll Learn:

How to maximize the value of geospatial data using machine learning and artificial intelligence techniques, business problems that can be tackled in a variety of industries using this type of data, and how to utilize algorithms specific to spatial data.

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

Abstract:

We live in an age of data; so much data that it’s overwhelming. We also live in the age of UX where user centricity is no longer the exception or a market differentiator - it is now the norm. Many product companies have an established team of data science experts; many have an established team of UX experts. However, it’s not very common to come across companies that have both. And if they do, each team often works in a vacuum, siloed from each other.

What You'll Learn:

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

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

Abstract:

We live in an age of data; so much data that it’s overwhelming. We also live in the age of UX where user centricity is no longer the exception or a market differentiator - it is now the norm. Many product companies have an established team of data science experts; many have an established team of UX experts. However, it’s not very common to come across companies that have both. And if they do, each team often works in a vacuum, siloed from each other.

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

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:

The quality of online comments is critical to the Washington Post. Learn how they built a machine learning system for automatically moderating comments from millions of readers. We will share the technical challenges with building the comment moderation platform and how we raised the quality of online conversations with machine learning.

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 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

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

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 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 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

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 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 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

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:

Applied machine learning has the potential to transform healthcare, particularly in the areas of automation, prediction, and optimization. However, numerous challenges to the acquisition, storage, and utilization of data as well as the development of practical machine learning algorithms and change management principles need to be considered. This talk will provide an overview of the process of applying ML into healthcare and the legal and ethical considerations needed for data access and application.

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.

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Francisco Martha Gonzales
Payments, Digital Banking and IT Managing Director, Banorte

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:

There are high expectatios about AI initiatives across different industries in North America. However, too often results have been disappointing producing some backlash against digital transformation efforts. The capacity to implment and demonstrate high ROI AI projects changes this dynamic. This talk will delve into Banorte's transformation journey into an AI enhanced organization with data science projects yielding a net revenue that exceeds 3 billion USD during the past five years and avoiding the transformational fatigue.

What You'll Learn:

1. How to measure AI contribution to the bottom line
2. What are key prerequisites to focus yield high ROI on AI projects
3. Where to focus AI inititiatives to have a large organizational impact: revenue or cost?

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Jaya Kawale
Director of Machine Learning, Tubi

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

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

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

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

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.