Speakers 2019

Full Speaker List:

• 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
• Kathryn Hume, Director of Product & Business Development, Borealis AI – Keynote
• Jordan Jacobs, Founder, Radical Ventures, Co-Founder, Vector Institute – Keynote
• Darin Graham, Director, R&D Strategy & Operations, Toronto AI Lab, LG – Keynote
• Jian Chang, Senior Algorithm Expert, Alibaba Group – BI Analytics Through Time Series Data
• Vladimir Iglovikov, Senior Computer Vision Engineer, Lyft – Technical Talk TBA
• Raj Verma, Senior Staff Engineer, Uber – Using Machine Learning in Revenue Forecasting and Planning
• Daniel Weimer, Head of AI, Volkswagen – Transformation from Research Lab to Product Centers
• Tomi Poutanen, Co-founder, Co- Founder, Layer6, Vector Institute – Determining Which ML Opportunities You Should Prioritize
• Jaya Kawale, Senior Research Scientist, Netflix – Multi-Armed Bandit Approaches for Recommendations at Netflix
• Trishala Pillai, Partnerships Lead, Myplanet – Determining Which ML Opportunities You Should Prioritize
• Ofer Shai, Chief AI Officer, Deloitte Canada – Determining Which ML Opportunities You Should Prioritize
• Chris Wiggins, Chief Data Scientist, NY Times – Applied Machine Learning at New York Times
• Rupinder Dhillon, Chief Data Officer, SVP Data & AI, Hudson’s Bay Company – Determining Which ML Opportunities You Should Prioritize
• Nicholas Frosst, rSWE, Google Brain – Adversarial Examples and Understanding Neural Network Representation Space
• Marzyeh Ghassemi, Researcher, Vector Institute – Research Talk TBA
• David Duvenaud, Assistant Professor, UofT – Neural Stochastic Differential Equations
• Razvan Peteanu, Lead Architect, ML, TD Securities – Scaling Machine Learning; Choosing the Right Approach
• Helen Ngo, ML Engineer, Dessa – Creative Ways to Collect & Use Data
• William Falcon, Facebook AI Research, NYU – Scaling up PyTorch with PyTorch Lightning
• Simona Gandrabur, Sr. Director, AI Lead, National Bank of Canada – Determining Which ML Opportunities You Should Prioritize
• Patrick Halina, Software Architect/ML Engineering Manager, Zynga – Deep Reinforcement Learning at Zynga: Overcoming the challenges of using RL in production
• Nazneen Rajani, Research Scientist, Salesforce Research – Explain Yourself! Leveraging Language Models for Common Sense Reasoning
• John Boersma, Director of Education, DataRobot – Machine Learning & AI for Executives Seminar
• Faraz Rasheed, Senior AI Architect, Microsoft Canada – Building Sentiment Classifier using Deep NLP
• Shariyar Murtaza, Senior Data Scientist, Manulife Financial – Building Sentiment Classifier using Deep NLP
• Chakri Cherukuri, Senior Researcher, Bloomberg LP – Interactive Visualization Approaches In Jupyter Notebooks
• Jill Cates, Data Scientist, BioSymetrics – Building a Binary Classification ML Model To Predict Hospital Readmission in Patients with Diabetes
• Max Tian, Machine Learning Specialist, GoldSpot Discoveries Corp – Leveraging Transfer Learning to Improve Natural Language Processing
• Chitta Ranjan, Director of Science, ProcessMiner, Inc. – Rare Event Prediction with Deep Learning
• Ajinkya Kulkarni, Senior Director, RBC – Building a Data Science Team
• Afsoon Soudi, Senior Manager, Data Science and Machine Intelligence, CBC – Building a Data Science Team
• Garrett Smith, Founder, Guild AI – Clean Coding for Machine Learning Projects
• Christine Gerpheide CTO, Bespoke Inc – Create a Chatbot from Scratch Using NLP and ML
• Farnoosh Khodakarami, Researcher, Princess Margaret Cancer Centre, – Machine Learning In Healthcare: Using ML to Work with Genomic and Radiomic Data
• Seyed Ali Madani Tonekaboni, ML Specialist, Cyclica – Using ML to Work with Genomic and Radiomic Data
• William Falcon, Creator of Pytorch Lightening, Facebook AI Research, NYU – Scaling up PyTorch with PyTorch Lightning
• Paul Finlay, Machine Learning Lead,Xanadu – A Practical Guide to Trustworthy AI: Hands-On with Silver Hammer
• Ofer Shai, Chief AI Officer, Deloitte Canada – Determining Which ML Opportunities You Should Prioritize
• Hoyoung Jang, Data Scientist, ThinkData Works – A Flexible Framework for Entity Resolution
• Sarah Sun, Chief Data Strategist, GoldSpot Discoveries – Creative Ways to Collect and use Data
• Helen Kontozopoulos Co-Founder, ODAIA.AI – Creative Ways to Collect and use Data
• Rogayeh Tabrizi, CEO, Theory & Practice – Creative Ways to Collect and use Data

Poster Sessions

• Peter Starszyk, Data Scientist, PeakPower
• Harris Chan, Graduate Student, Vector & UofT
• Neda Navidi, ML researcher, AI-r
• Hoora Fakhrmoosavy, Data scientist & student in Big data & Data Analytic Program, Ryerson University
• Angus Galloway, PhD Student in Machine Learning, University of Guelph
• Farukh Jabeen, Research Scientist, Computation, Science Research and Development
• Jonathan Lorraine, Graduate Researcher, Vector & UofT
• Gavin Weiguang Ding, Senior Researcher, Borealis AI
• Sicong Huang, Undergrad Research Student, UofT
• Paul Vicol, Graduate Student, Vector & UofT
• Dr. Joseph Geraci, CEO, NetraMark Corp