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DTSTART;TZID=America/Toronto:20231115T140000
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URL:/data-science/events/data-science-industry-panel-ev
 ent-industry-and-academic
SUMMARY:Data Science Industry Panel Event: Industry and Academic Perspectiv
 es
CLASS:PUBLIC
DESCRIPTION:Summary \n\nJOIN US NOVEMBER 15\, 2023 AT 2 P.M. IN DC 1302 FOR
  THE DATA SCIENCE\nINDUSTRY PANEL EVENT: INDUSTRY AND ACADEMIC PERSPECTIVE
 S\n\nRegister to attend\n[https://www.ticketfi.com/event/5364/data-science
 -industry-panel-event-industry-and-academic-perspectives]\n\n-------------
 ------------\n\nEVENT HIGHLIGHTS\n\nEXPERT TALKS:\n\nHear from YANNICK LA
 LLEMENT\, VP\, Global Artificial Intelligence &\nMachine Learning at Scoti
 abank and TAMER ÖZSU\, Professor in the\nDavid R. Cheriton School of Co
 mputer Science at the University of\nÀ¶Ý®ÊÓƵ\, as they explore data sci
 ence from an industry and academic\nperspective.\n\nPANEL DISCUSSION:\n\nO
 ur distinguished panel of experts will answer questions and further\ndiscu
 ss perspectives of Data Science.\n\n* Tamer Özsu\, Professor | David R. 
 Cheriton School of Computer\nScience  \n * Yannick Lallement\, VP\, Glob
 al Artificial Intelligence & Machine\nLearning | Scotiabank \n * Jeff Ha
 tcher\, Director\, Advanced Analytic | Canadian Institute\nfor Health Inf
 ormation \n\nNETWORKING OPPORTUNITIES: \n\nThe talk and panel event will
  be followed by an opportunity to network\nwith our experts\, special gues
 ts and fellow classmates in DC 1301.\n_REFRESHMENTS WILL BE SERVED. _\n\n
 -------------------------\n\nEVENT TALKS\n\nSCOTIABANK'S APPROACH TO LARGE
  LANGUAGE MODELS (LLM) \nYannick Lallement | VP\, Global Artificial Intel
 ligence & Machine\nLearning\, Scotiabank \n\nA presentation on Scotiabank
 's risk-based approach to LLM enablement.\nIncluding the usage of ChatGPT\
 , the major use cases identified so far\nand their road to production\, an
 d an update on various LLM pilots the\nbank is running. \n\n A SYSTEMATI
 C VIEW OF DATA SCIENCE \nTamer Özsu | Professor\, David R. Cheriton Sch
 ool of Computer\nScience \n\n​There is a data-driven revolution underwa
 y in science and society\,\ndisrupting every form of enterprise. We are co
 llecting and storing\ndata more rapidly than ever before. There is an incr
 easing recognition\nthat data science can assist in leveraging this data a
 nd the insights\nobtained from it into products\, systems\, and policies. 
 This has\nresulted in the formation within academia of data science resear
 ch\ncentres\, institutes and even academic units and the establishment of\
 nmajor initiatives within every major industrial organization. However\,\n
 our understanding of data science is vague and highly varied and\, in\nman
 y cases\, are squeezed to fit the available openings within an\ninstitutio
 n. There is a need to approach this field systematically to\ndefine its sc
 ope and its boundaries. The objective of this talk is to\nprovide such a c
 onsistent and systematic study of the scoping of data\nscience.   \n 
DTSTAMP:20250524T181222Z
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