Data Science Industry Panel Event: Industry and Academic Perspectives
November 15, 2023 | 2:00 p.m. | DC 1302
Register to attend
EVENT HIGHLIGHTS
Expert Talks:
Hear from Yannick Lallement, VP, Corporate Functions Analytics and Chief AI Officer (CAIO) at Scotiabank and Tamer Özsu, Professor with David R. Cheriton School of Computer Science at the University of Waterloo as they explore data science from an industry and academic perspective.
Panel Discussion:
Our distinguished panel of experts will answer questions and further discuss perspectives of Data Science.
- Yannick Lallement, VP, Corporate Functions Analytics and Chief AI Officer (CAIO) | Scotiabank
- Tamer Özsu, Professor | David R. Cheriton School of Computer Science
- Jeff Hatcher, Director, Advanced Analytic | Canadian Institute for Health Information
- Kamil Malikov, Director, Health Data Science Branch | Ministries of Health and Long-Term Care
- Bethany L, Supervisor, Data Science, Cyber Threat Assessment, Cyber Centre | Canadian Centre for Cybersecurity
Networking Opportunities:
The talk and panel event will be followed by an opportunity to network with our experts, special guests and fellow classmates in DC 1301. Refreshments will be served.
EVENT TALKS
Scotiabank's approach to Large Language Models (LLM)
Yannick Lallement | VP, Corporate Functions Analytics and Chief AI Officer (CAIO), Scotiabank
A presentation on Scotiabank's risk-based approach to LLM enablement. Including the usage of ChatGPT, the major use cases identified so far and their road to production, and an update on various LLM pilots the bank is running.
A Systematic View of Data Science
Tamer Özsu | Professor, David R. Cheriton School of Computer Science
There is a data-driven revolution underway in science and society, disrupting every form of enterprise. We are collecting and storing data more rapidly than ever before. There is an increasing recognition that data science can assist in leveraging this data and the insights obtained from it into products, systems, and policies. This has resulted in the formation within academia of data science research centres, institutes and even academic units and the establishment of major initiatives within every major industrial organization. However, our understanding of data science is vague and highly varied and, in many cases, are squeezed to fit the available openings within an institution. There is a need to approach this field systematically to define its scope and its boundaries. The objective of this talk is to provide such a consistent and systematic study of the scoping of data science.