TMLS Annual Conference & Expo

November 20th – 22nd

A 3-day exploration of Machine Learning Research and Business with practical use-cases.

Tomi Poutanen

Co-founder Layer6 AI, Chief AI Officer, TD

November 20 - 22nd

LIMITED SPOTS AVAILABLE

JOIN US TODAY!

David Duvenaud
David Duvenaud

Assistant Professor, UofT

Marzyeh Ghassemi

Researcher, Vector Institute

Kathryn Hume
Kathryn Hume

Director of Product & BD, Borealis

Jian Chan

Staff Algorithm Expert, Alibaba Group

Rupinder Dhillon

Chief Data Officer, SVP Data & AI, Hudson's Bay Company

Jaya Kawale

 Machine Learning Research Engineering, Netflix

Raj Verma

Senior Staff Engineer, Uber

Attendees

Speakers

Companies

Job-seekers

Conference Includes

4X STREAMS:  Business Steam | Real Case Studies & ML in Production |  Research & Advanced Technical Stream | Workshops

Additional:

Research Poster Displays

November 21/22, 2019
 

Women In AI Celebration

November 21, 2019

 

AI Expo & Career Fair

 November 22st, 2019

 

Workshop Day (Most now full)

 November 20th, 2019

 

Platinum Sponsors

Official Media Partner and Video Host

SCHEDULE

Day 1 - November 21

Start Time

BUSINESS

Applied Case Studies
& ML in Production

Advanced Technical

}

8:45am

Attendee Registration and Sponsor Booths Open including Poster Sessions.

}

9:45am

Opening Welcome: David Scharbach & Land Acknowledgement Presentation

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9:55am

Keynote Address: Kathryn Hume, Director of Product & Business Development,  Borealis AI

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10:25am

Morning Coffee Break (Sponsored by H2O.ai) + Poster Sessions

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10:55am

Fail to Scale: 5 Challenges to Implementing AI and How to Solve Them

Ian Scott, Partner, Chief Data Scientist, Deloitte 

Building an AI Engine for Time Series Data Analytics - Alibaba’s TSDB AI Engine

Jian Chang
Senior Algorithm Expert.
Alibaba Group

Lookahead Optimizer: k steps forward, 1 step back

Michael Zhang Researcher, University of Toronto & Vector Institute

}

11:30am

In Data We Trust: Data Governance Strategies for Data Projects

Laila Paszti, Of Counsel
Norton Rose Fulbright

AI in Clinical Decision Support: Roadblocks & Opportunities

Niki Athanasiadou, Data Scientist
H2O.ai

 

Neural Stochastic Differential Equations for Irregularly-Sampled Time Series

David Duvenaud, Assistant Professor University of Toronto

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12:05pm

Networking Lunch, Exhibitions +  Poster Sessions

Professional Brain-dates:
Networking for Media, Retail, E-commerce, Advert

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1:15pm

Panel: Determining Which ML Opportunities You Should Prioritize

Tomi Poutanen, Chief AI Officer, TD, Founder, Layer 6 AI

Simona Gandrabur, Sr. Director, AI Lead at the National Bank of Canada, Wealth Division

Ofer Shai Chief AI Officer Deloitte, Omnia AI

Trishala Pillai, Applied AI Partner, Myplanet

Rupinder Dhillon
Chief Data Officer, SVP Data & AI, Hudson's Day Company 

An Explanation of What, Why, and how of Explainable AI (XAI)

Bahador Khaleghi
Customer Data Scientist and Solution Engineer
H2O.ai

HoloClean: A Scalable Prediction Engine for Automating Structured Data Prep

Ihab Ilyas Founder, Tamr, Professor, University of Waterloo 

}

1:50pm

Lessons From Google’s Journey to AI-First

Chanchal Chatterjee
Leader in Artificial Intelligence Solutions, Google

Image Augmentations for Semantic Segmentation and Object Detection

Vladimir Iglovikov,  Senior Computer Vision Engineer, Lyft

}

2:20pm

Afternoon Networking Break, Exhibitions + Poster Sessions

Professional Brain Dates:
Networking for Banking, Financial Industries

}

2:55pm

Debate: "Sidewalk Labs' proposed development in Toronto is a net gain for the city." 

Debating For:
- Sunil Sharma, Managing Director, Techstars Toronto
- Brian Kelcey, VP Policy & Public Affairs at Toronto Region Board of Trade

Debating Against: 
- Mike Cook, President - Identos Inc
- Ann Cavoukian -Executive Directo, Global Privacy & Security by Design Centre

Multi-Arm Bandit Approaches for Recommendation at Netflix

Jaya Kawale, ML Research Engineering, Netflix

Certifiable Robustness to Adversarial attacks; What is the Point?

Nick Frosst
rSWE, GoogleBrain

}

3:30pm

Building Private Machine Learning Models with TensorFlow

Chang Liu
Applied Research Scientist at Georgian Partners

Temporal Concept Localization on YouTube-8M Dataset

Satya Krishna Gorti Machine Learning Scientist, Layer 6 AI

 

}

6pm to 8pm

Women in Data Science Ceremony Sponsored By RBC
- Curated Networking and Mentor Meetings

Day 2 – November 22

Start Time

BUSINESS

Applied Case Studies
& ML in Production

Advanced Technical

}

8:45am

Attendee Registration and Sponsor Booths Open including Poster Sessions.

}

9:40am

Opening Welcome & Sponsor Greetings

}

9:50am

Keynote Address: Darin Graham, Director Toronto AI Lab, LG Electronics
}

10:05am

Keynote Address: Jordan Jacobs, Radical Ventures

}

10:20am

Morning Coffee Break (Sponsored by H2O.ai) + Poster Sessions

Professional Brain-dates:
Autonomous Vehicles

}

10:50am

Productize AI – Transformation From Research Lab to Product

Daniel Weimer Head of AI, Volkswagen of America, Inc

Using Machine Learning in Revenue Forecasting and Planning

Raj Verma, Senior Staff Engineer, Uber

Machine Learning for Systems

Azalia Mirhoseini
Senior Research Scientist at Google Brain

}

11:25am

Panel Session: Autonomous Driving & Future of Mobility

Arif Virani, COO, DarwinAI

Ted Graham Head of Open Innovation, GM

Steven Lake Waslander , Associate Prof at University of Toronto

Applied Machine Learning at New York Times

Christopher Wiggins, Chief Data Scientist, New York Times

Healthy ML for Healthcare- Representation Learning & Fairness in ML for Health

Marzyeh Ghassemim Assistant Professor at UofT, CIFAR AI CHair & Canada Research Chair, Vector Institute

}

11:55am

Networking Lunch , Exhibitions +  Poster Sessions

Professional Brain-dates:
Healthcare, Insurance & Telecomm

}

12:55pm

Scaling Machine Learning

Razvan Peteanu, Lead Architect, Machine Learning, TD Securities

DevOps for Machine Learning and other Half-Truths: Processes and Tools for the ML Life Cycle

Kenny Daniel, Founder,  Algorithmia

Explaining Explainability: Demystifying the Black Box of Deep Learning

Sheldon Fernandez CEO at DarwinAI

}

1:30pm

Trustworthy AI: Model Validation at Scale

Layli Goldoozian, Data Scientist, Lucy Liu, Director, 
Greg Kirczenow, Senior Director, Enterprise Model Risk, RBC

Rearchitecting Legacy Machine Learning Systems

Amit Jain, Machine Learning Team Lead TradeRev

A Flexible Framework for Entity Resolution

Hoyoung Jang, Data Scientist, ThinkData Works

}

2:00pm

Afternoon Networking Break, Exhibitions + Poster Sessions

Professional Brain-dates:
CleanTech, Energy, Manufacturing

}

2:30PM

Panel: Creative Ways to Collect & Use Data for AI 

Helen Ngo, ML Engineer at Dessa

Sarah Sun, Chief Data Strategist  GoldSpot Discoveries

Helen Kontozopoulos, Co-Founder at ODAIA.AI

Rogayeh Tabrizi, CEO Theory & Practice

Deep Reinforcement Learning in Production at Zynga Overcoming the challenges of using RL in Production.

Patrick Halina Software Architect/ML Engineering, Mehdi Ben Ayed, Manager, Zynga

Explain Yourself! Leveraging Language Models for Common Sense Reasoning

Nazneen Rajani, Research Scientist, Salesforce Research

}

3:05pm

Closing Ceremony & Best Poster Award Announcement

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5:15pm to 8:30pm

70+ AI Start-ups  – Career Fair and Expo

Fran's Restaurant and Bar

Pub Social | 20 College St, Toronto, ON M5G 1K2

TMLS is a community with
over 6,000 active members that works to
promote and encourage the adoption of
successful machine learning initiatives
 within Canada.

#TMLS 2019 Speakers/Topics Include

  • Ian Scott, Partner, Chief Data Scientist, Deloitte – Fail to Scale: 5 Challenges to Implementing AI and How to Solve Them
  • Michael Zhang, Researcher, University of Toronto & Vector Institute – Lookahead Optimizer: k steps forward, 1 step back
  • Laila Paszti, Of Counsel, Norton Rose Fulbright – In Data We Trust: Data Governance Strategies for Data Projects
  • Niki Athanasiadou, Data scientist, H2O.ai – Applications of AI in Medicine: Roadblocks and Opportunities
  • Layli Goldoozian, Data Scientist RBC – Trustworthy AI: Model Validation at Scale
  • Lucy Liu, Director, Data Science & Analytics Team, RBC – Trustworthy AI: Model Validation at Scale
  • Bahador Khaleghi, Customer Data Scientist and Solution Engineer, H2O.ai – An Explanation of What, Why, and how of Explainable AI (XAI)
  • Ihab Ilyas, Founder, Professor, Tamr, University of Waterloo – HoloClean: A Scalable Prediction Engine for Automating Structured Data Prep
  • Chanchal Chatterjee, Leader in Artificial Intelligence Solutions, Google – Lessons from Google’s Journey to AI- First
  • Chang Liu, Applied Research Scientist, Georgian Partners – Building Private Machine Learning Models with TensorFlow
  • Satya Krishna Gorti, Machine Learning Scientist, Layer 6 AI – Temporal Concept Localization on YouTube 8M Dataset
  • Azalia Mirhoseini, Senior Research Scientist, Google Brain – Machine Learning for Systems
  • Ted Graham, Head of Open Innovation, GM – Autonomous Driving and the Future of Mobility
  • Steven Lake Waslander, Associate Prof, University of Toronto – Autonomous Driving and the Future of Mobility
  • Kenny Daniel, Founder, Algorithmia – DevOps for Machine Learning and other Half- Truths: Processes and Tools for the ML Life Cycle
  • Sheldon Fernandez, CEO, DarwinAI – Explaining Explainability- Demystifying the Black Box of Deep Learning
  • Sunil Sharma, Managing Director, Techstars Toronto – Debate: Is Sidewalk Labs a net gain for the city?
  • Brian Kelcey, VP Policy & Public Affairs, Toronto Region Board of Trade – Debate: Is Sidewalk Labs a net gain for the city?
  • Mike Cook, President, Identos Inc. – Debate: Is Sidewalk Labs a net gain for the city?
  • Ann Cavoukian, Executive Director, Global Privacy & Security by Design Centre – Debate: Is Sidewalk Labs a net gain for the city?
  • Amit Jain, Machine Learning Team Lead TradeRev – Rearchitecting Legacy Machine Learning Systems

Women In Data Mentors 

  • Christina Cai, Co-Founder & COO, Knowtions
  • Paula Hodgins, President, Hewlett Packard Enterprise
  • Zoe Katsimitsoulia, Senior Data Scientist, Nudge.ai
  • Susan Chang, Data Scientist, Bell Canada
  • Nour Fahmy, Data Scientist, #paid
  • Helen Ngo, Machine Learning Engineer, Dessa
  • Serena McDonnell, Senior Data Scientist, Delphia
  • Rupinder Dhillon, Chief Data Officer, Hudsons Bay Company
  • Carla Margalef Bentabol, Director of Engineering & Machine Learning, Village Technologies
  • Cecilia Liu, Senior Data Scientist, Wattpad
  • Helen Kontozopoulos, Co-founder, Odaia.ai

Poster Sessions

  • Sicong Huang, Undergrad Research Student, UofT (ICLR 2019)
  • Gavin Weiguang Ding, Senior Researcher, Borealis AI (ICLR 2019)
  • Chundi Liu, Data Scientist Intern, Layer 6 AI (NIPS 2019)
  • Angus Galloway, PhD Student in Machine Learning, University of Guelph (ICML 2019)
  • Peter Starszyk, Data Scientist, PeakPower
  • Harris Chan, Graduate Student, Vector & UofT
  • Neda Navidi, ML researcher, AI-r
  • Dr. Joseph Geraci, CEO, NetraMark Corp
  • Hoora Fakhrmoosavy, Researcher, Ryerson University
  • Farukh Jabeen, Research Scientist, Computation, Science Research and Development
  • Jonathan Lorraine, Graduate Researcher, Vector & UofT
  • Paul Vicol, Graduate Student, Vector & UofT

CAREER FAIR & EXPO 2019

A great opportunity to meet with over 70 of the Top AI Start-ups.

WOMEN IN DATA CEREMONY

Toronto Machine Learning Summit and RBC invite you to this free evening event.

Bonus Workshops  – NOVEMBER 20TH

A bonus day of workshops will be held on November 20th, the day prior to the 2019 TMLS Conference.

No extra costs but please be aware that participation in the workshops is subject to a limited amount of seating.

Interactive Visualization Approaches In Jupyter Notebooks with Chakri Cherukuri, Senior Researcher at Bloomberg LP

 Nov 20th 2019

5:30 PM to 8:30 PM (EST)

Building a Binary Classification ML Model with Jill Cates Data Scientist at BioSymetrics

Nov 20th 2019

2:00 PM to 5:00 PM (EST)

Machine Learning & AI for Executives Seminar with John Boersma is Director of Education for DataRobot

Nov 20th 2019

9:00 AM to 12:00 PM (EST)

Rare Event Prediction with Deep Learning with Chitta Ranjan, Director of Science at ProcessMiner, Inc.

Nov 20th 2019

6:00 PM to 9:00 PM (EST)

BI Analytics Through Time Series Data with Jian Chang, Senior Algorithm Expert, Alibaba Group

 Nov 20th 2019

6:00 PM to 9:00 PM (EST)

Clean Coding for Machine Learning Projects with Garrett Smith, Founder of Guild AI

Nov 20th 2019

2:00 PM to 5:00 PM (EST)

Create a Chatbot from Scratch — Using NLP and ML with Christine Gerpheide CTO at Bespoke Inc.

Nov 20th 2019

9:00 AM to 12:00 PM (EST)

Bonus Evening Panel: Building a Data Team with Ajinkya Kulkarni Senior Director, Data Science and AI at RBC  

Nov 20th 2019
5:30 PM to 9:00 PM (EST)

Machine Learning In Healthcare: Using ML to work with Genomic Data with Dr. Farnoosh Khodakarami Researcher at the Margaret Cancer Centre, Seyed Madani Tonekabon ML Specialist at Cyclica

Nov 20th 2019
2:00 PM to 5:00 PM (EST)

Leveraging Transfer Learning to Improve Natural Language Processing with Max Tian, Machine Learning Specialist at GoldSpot Discoveries Corp

Nov 20th 2019

6:00 PM to 9:00 PM (EST)

Introduction to Deep Learning with Tensorflow 2.0 with WeCloudData

Nov 20th 2019

9:00 AM to 12:00 PM (EST)

Scaling up PyTorch with PyTorch Lightning – A deep hands on tutorial with William Falcon, Facebook AI Research, NYU

Nov 20th 2019

9:00 AM to 12:00 PM (EST)

 A Practical Guide to Trustworthy AI: Hands-On with Silver Hammer with Paul Finlay, Ph.D., Machine Learning Lead at Xanadu

Nov 20th 2019
1:00 PM to 4:00 PM (EST)

Building Sentiment Classifier using Deep NLP with Dr. Shariyar Murtaza, Manulife Financial & Dr. Faraz Rasheed, Microsoft Canada

Nov 20th 2019
6:00 PM to 9:00 PM (EST)

Who Attends

Business Leaders, C-level executives and non-technical leaders will explore immediate opportunities, and define clear next steps for building their business advantage around their data.

Data Practitioners, will dissect technical approaches, case studies, tools, and techniques to explore Natural Language Processing, Neural Nets, Reinforcement Learning, Generative Adversarial Networks (GANs), Evolution Strategies, AutoML and more.

Job Seekers will have the opportunity to hone their skills as well as meet from over 60 Top AI Start-ups and companies during the EXPO & Career Fair.

Attendees Include
Business Leaders Data Practitioners Researchers/Students
Chief Client Officer
Chief Digital Officer
Chief Executive Officer
Chief Experience Officer
Chief Operating Officer
Chief Scientific Officer
Chief Technology Officer
Director of Innovation
Director of Talent
Director, Applied Analytics & Innovation
Investor
Enterprise Data Innovation
Executive Director, Enterprise Applications
Founder
Global Manager, Marketing
Legal Advisor
MD, Innovation and Emerging Tech
Portfolio Manager
President
Product Manager
Project Manager
Recruiter
Scrum Master
Senior Product Manager
Senior Associate
Strategy & Business Development
SVP Product
SVP Professional Services
Trader
VP, Digital Data and Analytics
VP, Marketing and Communications
VP Growth
VP Strategy & Operations Business Intelligence
VP, Artificial Intelligence
AI Developer
AI Lead
AI Project Lead
AI Solution Architect
Big Data Engineer
Biomed Engineer
Chief Data Scientist
Chief Scientist
Computational Linguist
Data Science Lead
Data Science Tooling Lead
Data Warehouse Lead
Director of Data
Director of Engineering
Distinguished Data Scientist
Full Stack Developer
Lead Firmware & Systems Engineer
Machine Learning Developer
Machine Learning Engineer
Machine Learning Engineer (NLP)
Machine Learning Researcher Machine
Learning Specialist
Manager, Application Development
Manager, Mobile Architecture
Platform Architect
Principal Consultant
Principal Data Scientist
Principal Software Engineer
Principle Architect
Senior Computer Vision Engineer
Senior Data Analyst
Senior Data Manager
Senior Data Scientist
Software Engineer
Sr. Cyber Security Advisor
Sr. Dir Engineering
AI Researcher
Applied Research Scientist
Assistant Professor
Data Science fellow
Director Meteorological R&D
Graduate Research Assistant
Informatics Research Associate
ML Masters Student
Machine Learning Researcher
MASc Student Researcher
Masters of Management in Artificial Intelligence
PhD Student, Researcher
Portfolio Manager/Student
Post-graduate Researcher
Postdoctoral Fellow
Postdoctoral Researcher
Principal Researcher
Professor
Quantum Deep Learning Researcher
Research Data Analyst
Research Engineer
Research Scientist
Researcher and Software Developer
Scientific Adviser
Senior Analyst – Research and Development
Senior Research Associate
Senior Research Economist
Senior Research Scientist
Software Research Developer, Algorithms
Sr. Research Methods Specialist
Student, Machine Learning
Attending Companies
Allstate Insurance Canada
Apotex
AT&T
Aviva
Bell Canada
Borealis AI
CAMH
Canada Pension Plan Investment Board
Canadian Red Cross
Canadian Tire
Centre for Addiction and Mental
Health (CAMH)Crater Labs
CIFAR
Coca-Cola
CPP Investment Board
Deloitte
Element AI
Epson
Ernst & Young LLP
Exiger
Finastra
Flipp Inc
General Motors Canada
Georgian Partners
Google
GoDaddy
Home Depot
Huawei
IBM
Indigo
Institute for Quantum Computing
Intact Financial
Integrate AI
Interac
Jam3
John Hancock / Manulife
Johnson & Johnson
KDnuggets
KPMG
Loyalty One
Loblaw Companies Limited
McDonald’s Canada
Maple Leaf Sports & Entertainment
Mastercard Canada
McKinsey
Metrolinx
Microsoft
ModiFace Inc
MoneyKey
Nascent
Nestle
Ontario Genomics
Ontario Institute for Cancer Research (OICR)
Ontario Investment Office
Ontario Teachers Pension
Pelmorex
Postmedia Network
President’s Choice Financial
Princess Margaret Cancer Research Center
Rangle.io
PwC
SAS
Samsung Canada
Shopify
SickKids
StackAdapt
Statflo
Sun Life Financial
Symcor
Tableau Canada
Tangerine
TD Bank
TELUS
The Ontario Brain Institute
Thomson Reuters
TIBCO
TMX Group Limited
TSX
Uber
Vector Institute
VoiceX Labs
Wattpad
Walmart

PARTICIPANTS

companies

Researchers / Experts

Business Leaders

FAQ

What's the refund policy?

Tickets are refundable up to 30 days before the event.

Why should I attend the TMLS?

Developments are happening fast – it’s important to stay on top.

For businesses leaders: you will have direct contact content our Steering Committee determined was the most impactful for our industry. You’ll also have a chance to with the largest Canadian community of ML practitioners, Data Scientists and peers working in AI!

For data practitioners, you’ll have an opportunity to fast-track your learning process with access to relevant use-cases, and top quality speakers and instructors that you’ll make lasting connections with while building your network.

For Researchers, you’ll have a chance to hear from top ML academics as well as senior researchers from industry!

The event is casual and tickets are priced to remove all barriers to entry. Space, however, is limited.

Who will attend?

The conference portion will have three tracks that attract a different audience:

1. A “Businesses” track that attracts top executives and folks working on data strategies and roadmaps to AI implementation.

2 . A “Case Study and ML in Production” track for data practitioners; data scientists, ML engineers etc.

3. A “Research & Advanced Technical” Track for academic researchers, scientists leading research and development projects in industry. 

Please message info@torontomachinelearning.com for a more detailed breakdown of attendee demographics; titles etc. 

 

Will you focus on any industries in particular?

Yes, we will have researchers and use cases from various industries. Also content that is applicable across industry.

We will also have targeted networking time; “brain-dates” for folks working with specific interests to meet at the breaks and network with those in your industry.  This will be supported, as well, by our event app. 

Do you have any ticket deals?

Our Early-bird, and Diversity & Inclusion tickets have closed. However, TMLS acknowledges that there shouldn’t be a barrier of entry for those who want to upskill themselves. If you’re confident the content of our conference will make a difference in your development but the price is inaccessible for you, message us directly and we can help.  

Please don’t wait to long to message us !  Tickets will sell out and we’ll be limited in what we can offer closer to the event. 

You can also message (info@torontomachinelearning.com) for bulk prices. 

Do you serve food?

Yes light breakfast will be served, as well as lunch both days. 

I'm not sure artificial intelligence can benefit my business. Is this still relevant?

Yes, a large component of the business track will be dedicated towards understanding the potential of machine learning and ensuring ROI. 

Can my company have a display?

Yes, there will be spaces for company displays. You can inquire at info@torontomachinelearning.com.

Will tickets include access to the after-party?

Yes, attendees will have full access to both night’s post event networking social.

Where and how can I register?
Can I speak at the event?

Yes you can always submit abstracts here. They will be considered for our future events. 

*Our speaking spots are non-commercial and cannot be purchased. 

What's the date, time?

Workshops are November 20th at various locations around the city. Conference events start at 21st of November, at 8:00 AM – 5:00 PM and Friday 22nd November 2019 9:00 AM- 5:00 PM (not including evening festivities). 

Where is the event taking place?

The event will take place at the historic Carlu at College and Yonge Street, Downtown Toronto; 7th floor, 444 Yonge St, Toronto, ON M5B 2H4.

Do you have a hotel block or discounts?

We do not have hotel discounts.

Who can I speak with for questions?

You can visit here for more information, or email info@torontomachinelearning.com and somebody will be in contact within 24 hours.

Join Our Community

Why join? It's free and you'll receive exclusive video's content, with executive summaries, & community networking invites.

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.

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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Cynthia Rudin
Professor of Computer Science, Electrical and Computer Engineering and Statistical Science, Duke University

Cynthia Rudin

Professor of Computer Science, Electrical and Computer Engineering and Statistical Science, Duke University

Cynthia Rudin is a professor of computer science, electrical and computer engineering, and statistical science at Duke University. Previously, Prof. Rudin held positions at MIT, Columbia, and NYU. Her degrees are from the University at Buffalo and Princeton University. She is a three time winner of the INFORMS Innovative Applications in Analytics Award, was named as one of the "Top 40 Under 40" by Poets and Quants in 2015, and was named by Businessinsider.com as one of the 12 most impressive professors at MIT in 2015. She has served on committees for INFORMS, the National Academies, the American Statistical Association, DARPA, the NIJ, and AAAI. She is a fellow of both the American Statistical Association and Institute of Mathematical Statistics.

Talk: Interpretability vs. Explainability in Machine Learning

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Talk: Understanding Content Using Deep Learning for Natural Language Processing

Abstract:

Tubi is an advertiser based video on demand service that allows its users to watch content online. For a lot of the content, there is a large amount of textual data in the form of user reviews, synopsis, title plots and even Wikipedia. Furthermore, there is a large amount of metadata in the form of actors, ratings, year of release, studio, etc. In this talk, I will present some of the challenges in understanding the data and present our platform for content understanding.

What You'll Learn:

Content understanding, deep learning for natural language processing, challenges in an industrial setting

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Talk: Assessing China’s AI Capabilities

Abstract:

The talk will give an overview of China's AI/ML ecosystem, and a deep dive into its capabilities when it comes to leading edge research in neural networks.

What You'll Learn:

You'll learn about China's role in the global flows of AI research talent, and what implications this has for government policy in the US, Canada and Europe.

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

Abstract:

In machine learning often a tradeoff must be made between accuracy and intelligibility: the most accurate models usually are not very intelligible, and the most intelligible models usually are less accurate. This often limits the accuracy of models that can safely be deployed in mission-critical applications such as healthcare where being able to understand, validate, edit, and ultimately trust a model is important. We have developed a learning method that is as accurate as full complexity models such as boosted trees and random forests, but even more intelligible than linear models. This makes it easy to understand what a model has learned and to edit the model when it learns inappropriate things. Making it possible for medical experts to understand and repair a model is critical because most clinical data has unexpected problems. I’ll present several healthcare case studies where these high-accuracy GAMs discover surprising patterns in the data that would have made deploying a black-box model risky, and also allow us to learn important new insights from our healthcare data.

What You'll Learn:

1) The risk of using machine learning in healthcare when you can't understand what the model is learned.
2) How new methods in intelligible machine learning can help mitigate this risk.
3) The amazing things we can learn about healthcare by using this kind of model on medical data.

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Talk: Race Data As An Anti-Racism Tool

Abstract:

Race is a concept, a tool, and a structure that defines a set of relationships between people. We will unpack the idea of race as relationships and race as data in its historical and current contexts. We will discuss what it means to build equity into data practices and what dismantling systemic racism can look like in technology (and the pitfalls to avoid).

What You'll Learn:

You will learn about and better understand what systemic racism is, the historical legacy of race data and how to challenge and question data practices for a more equitable society.

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

Abstract:

The practice of apply machine learning technology in healthcare, especially to deal with corona virus pandemic.

What You'll Learn:

The challenges, the solutions, the effectiveness, and the remaining issues, including technology progress and institution reform.

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Talk: Interpretability vs. Explainability in Machine Learning

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

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 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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Talk: Fine-Grained Emotion Detection for Products & Research

Abstract:

Understanding emotion expressed in language has a wide range of applications, from building empathetic chatbots to detecting harmful online behavior. In this talk, we will present our work at Google AI Research towards building GoEmotion, a large-scale dataset containing 58K social media comments labeled with a fine-grained emotion taxonomy, which is adaptable to multiple downstream tasks. We will share results demonstrating generalizability towards existing emotion benchmarks from other domains. Lastly, we will share how organizations could use this dataset to train custom models for their use cases.

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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Navid Kaihanirad
Data Scientist, Scotiabank

Navid Kaihanirad

Data Scientist, Scotiabank

Navid is an applied research scientist with a master's degree in computer science from the University of Toronto. His ultimate objective is to apply cutting-edge researches and latest breakthroughs to solve real-world issues. He enjoys challenging himself to solve problems that we don’t necessarily know if there’s, yet, a solution for

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

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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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Cheng Chen
Data Scientist, Scotiabank

Cheng Chen

Data Scientist, Scotiabank

Cheng is a PhD of economist turned data scientist. With 6+ years of experience in conducting empirical research in applied microeconomics, she is proficient in economic modelling and machine learning techniques

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

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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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Dana Movshovitz-Attias
Software Engineer, Google Research

Dana Movshovitz-Attias

Software Engineer, Google Research

Dana Movshovitz-Attias is a Staff Software Engineer and Researcher at Google Research, where she leads an NLP research group focused on Conversational AI, Graph ML, and Efficient ML Computation. She has over 15 years of experience in Natural Language Processing and Machine Learning. Her work has been published in top-tier conferences, incl. ACL, NeurIPS, and NAACL, and appeared in the press.

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:

Understanding emotion expressed in language has a wide range of applications, from building empathetic chatbots to detecting harmful online behavior. In this talk, we will present our work at Google AI Research towards building GoEmotion, a large-scale dataset containing 58K social media comments labeled with a fine-grained emotion taxonomy, which is adaptable to multiple downstream tasks. We will share results demonstrating generalizability towards existing emotion benchmarks from other domains. Lastly, we will share how organizations could use this dataset to train custom models for their use cases.

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

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

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:

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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Danit Gal
Technology Advisor, The United Nations

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

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

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 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

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

Mutale Nkonde

CEO, AI For the People

Talk:

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Alba Cervera Lierta
Postdoctoral Researcher, University of Toronto

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

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

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 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 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 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

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

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

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

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

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

Inmar Givoni

Director Of Engineering, Uber

Talk: Autonomous Vehicles - The Next Step Forward

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Nima Ashtari
Founder, X-Matik Inc.

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.

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 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 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

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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:

Exciting directions and opportunities for assisting machine learning with quantum computers.

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

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Talk: The Quest for the Final Quantum Computer

Abstract:

In this talk, I will overview the basic concepts of quantum computing and its applications. I will present what are the state-of-the-art quantum algorithms, its advantages and limitations. Finally, I will explain the state of development of experimental quantum computers and future prospects.

What You'll Learn:

The current state of quantum computation;

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Talk: Indigenous Language Technologies: Neural Machine Translation for Inuktitut

Abstract:

Recent advances in machine translation have resulted in systems of very high quality, but only for a very limited set of the world’s more than 7000 languages. This talk provides a brief overview of Indigenous language technology projects at the National Research Council of Canada, before focusing on one project in particular: the development of neural machine translation systems to translate between Inuktitut and English. We will discuss challenges, applications of state of the art models, and future use cases.

What You'll Learn:

Neural machine translation, applications of machine learning to Indigenous languages, challenges of domain adaptation in low-resource settings

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

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

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What You'll Learn:

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

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Talk: The Role of ML in Climate Change

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Talk: The Role of ML in Climate Change

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Talk: The Role of ML in Climate Change

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Talk: Autonomous Vehicles - The Next Step Forward

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Talk: Autonomous Vehicles - The Next Step Forward

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Talk: Autonomous Vehicles - The Next Step Forward

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