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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