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Accelerate AI Project Ideation Plenary -Technology & Operations Workstream
July 7 @ 3:00 pm - 5:00 pm
The Accelerate AI | Collaborative Strategies project will address common obstacles to AI implementation. Participants will co-create a framework of best practices and strategies for addressing challenges in AI projects including data sharing, legal & compliance requirements, coordinating resources, skills, and domain expertise, obtaining leadership buy-in, and investment.
As part of the Accelerate AI project, we’ll be offering several plenary sessions to discuss how organizations can prepare themselves to deliver AI solutions, from the perspective of facilitating collaboration across teams (both internally and between organizations).
Surface ideas to address collaboration challenges and barriers that impede deployment of AI.
- 3 PM – 5 PM, Tuesday, July 6, 2021: Legal & Compliance Workstream Plenary
- 3 PM – 5 PM, Wednesday, July 7, 2021: Technology & Operations Workstream Plenary
- 1 PM – 3 PM, Thursday, July 8, 2021: Governance & Business Workstream Plenary
Technology & Operations: Identify tools, resources, and processes to enable effective and efficient collaboration to deliver AI solutions across business units and organizations.
July 8th Agenda:
|3:15 pm||Data-Centric Recommendations in the AI Model Validation
Ga Wu, Machine Learning Researcher, Borealis AI
|3:35 pm||Overview of Privacy Enhancing Techniques
Ron Bodkin, VP & CIO, Vector Institute
Engineering Lead, Schwartz Reisman Institute for Technology and Society
Sara El-Shawa, Applied Machine Learning Intern, Vector Institute
Ryan Marten, Applied Machine Learning Intern, Vector Institute
Deval Pandya, Director, AI Engineering, Vector Institute
Shems Saleh, Member of AI Technical Staff, Vector Institute
Sophie Tian, Applied Machine Learning Intern, Vector Institute
|3:55 pm||Generative Models for Synthetic Panel Data
Jessie Lamontagne, Senior Manager, Data Science and Model Innovation, Scotiabank
André dos Santos, Postdoctoral Fellow, University of Alberta and Scotiabank
|4:15 pm||Panel Q&A and Open Discussion|
|4:45 pm||Wrap Up|
Ga Wu is a Machine Learning Researcher in the Model Governance and Validation (MGV) team at Borealis AI. Ga earned a Ph.D. in the field of machine learning at the University of Toronto (2020), a Master of Computing from the Australian National University (2014), and a Bachelor in Computer Science from Northwest Normal University, China (2009). Ga’s research spans a broad range of topics, from the data-driven fields of Machine Learning and Information Retrieval to the decision-driven fields of Artificial Intelligence Research. Ga has applied the analytic and algorithmic tools from these fields to diverse application areas such as recommender systems, AI planning, and model reliability analysis.
Ron is the VP of AI Engineering and CIO at Vector Institute and is the Engineering Lead at the Schwartz Reisman Institute for Technology in Society. Ron is responsible for leading engineering teams that apply Vector’s leading AI research to industry and health problems for Canada, that develop open source software, and that establish and support world class scientific computing infrastructure to scale the adoption of beneficial AI.
Previously, Ron was responsible for Applied Artificial Intelligence in the Google Cloud CTO office where he spearheaded collaborative innovation efforts working with strategic customers and Google AI research and engineering teams. Ron was the founding CEO of Think Big Analytics. Think Big Analytics provided enterprise data science and engineering services and software such as Kylo for enterprise data lakes and data science and was acquired by Teradata in 2014. After the acquisition, Ron led Think Big’s global expansion and created an Artificial Intelligence incubator at Teradata.
Previously, Ron was VP Engineering at Quantcast where he led data science and engineering teams that applied Machine Learning for real-time advertising and audience insights. Ron was also Co-Founder and CTO of C-Bridge Internet Solutions. Ron has an honors B.S. in Math and Computer Science from McGill University and a Master’s in Computer Science from MIT.
Deval is Director of AI Engineering at Vector Institute and one of the 100 Global Future Energy Leaders with the World Energy Council. He is passionate about building Artificial Intelligence and Machine learning systems for expediting energy transition and combating Climate Change. Prior to joining Vector, Deval was leading the Data Science team at Shell focusing on application in New Energies and Asset management. During his career, he has led development of scalable machine learning applications in the domains of nature-based solutions, predictive maintenance, e-mobility, microgrid optimizations and hydrogen value chain. Deval also serves as a Director on the technical steering committee of Moja Global, a not for profit, collaborative project that brings together a community of experts to develop open-source software under Linux Foundation used for country level greenhouse gas accounting from AFOLU sector.
Deval is on the task force for Digitalization in Energy at United Nations Economic Commission of Europe (UNECE) and a mentor at Creative Destruction Labs. He enjoys traveling and cooking in his free time.
Shems Saleh is a member of Vector Institute’s engineering team; a team working with the institute’s researchers and focusing on the development of Applied Machine Learning for applications in health and industry. She’s especially interested in the beneficial deployment of Machine Learning. In addition, Shems likes to explore questions related to social implications of Machine Learning algorithms on different people. Prior to this, Shems was a part of the institute’s Industry Innovation team where she worked on multiple applied projects and was responsible for the Face to Face program; a program facilitating industry-research sessions. She received her Master’s and BSc from the University of Toronto. Her Master’s work, done in affiliation with the Hospital for Sick Children, focused on modeling temporal health data.
Sara El-Shawa is an applied ML Intern at Vector, a 2020-21 Vector Scholarship in AI recipient, and a MASc candidate in Computer Engineering (AI Specialization) at the University of Guelph where she has joined the Machine Learning Research Group led by Vector Faculty Member Graham Taylor. She previously completed her Bachelor’s degree at the University of Toronto with a double major in Computer Science and Biology.
Ryan graduated from the University of Toronto in June 2021 with a Computer Science specialist and a focus in AI.
Sophie is a MASc. student in Industrial Engineering at the University of Toronto with research interests in deep learning and time series analysis.
Jessie is a data scientist at Scotiabank, working in retail models and analytics. Her expertise is in supervised and unsupervised machine learning, transfer learning, ethical AI, and credit risk.
André is a Postdoc researcher of artificial intelligence at the University of Alberta. His research interests include deep learning, synthetic data, probabilistic graphical models, and interpretable AI.