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Wednesday, November 15, 2023 2:00 pm - 5:00 pm EST (GMT -05:00)

Data Science Industry Panel Event: Industry and Academic Perspectives

Join us November 15, 2023 at 2 p.m. in DC 1302 for the Data Science Industry Panel Event: Industry and Academic Perspectives

Register to attend

EVENT HIGHLIGHTS

Expert Talks:

Hear from Yannick Lallement, VP, Global Artificial Intelligence & Machine Learning at Scotiabank and Tamer Özsu, Professor in the 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.

  • Tamer Özsu, Professor | David R. Cheriton School of Computer Science  
  • Yannick Lallement, VP, Global Artificial Intelligence & Machine Learning | Scotiabank 
  • Jeff Hatcher, Director, Advanced Analytic | Canadian Institute for Health Information 

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, Global Artificial Intelligence & Machine Learning, 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.