AI Methods for Scientific Impact

UBC Announces Major Hiring Cluster in AI 

August 12, 2021

 

A new UBC Science interdisciplinary cluster will help fuel artificial intelligence innovation and research excellence over the next two years. The Artificial Intelligence Methods for Scientific Impact (AIM-SI) cluster will hire five new assistant professors across the Departments of Computer Science (two faculty members), Statistics (two faculty members), and Mathematics (one faculty member). The $23M investment will be funded by the President’s Academic Excellence Initiative (PAEI). The cluster will also draw together 14 existing UBC faculty members spanning Computer Science, Statistics, Math, and Earth, Ocean and Atmospheric Sciences—including UBC’s existing six Canada CIFAR AI Chairs. Increasing diversity in AI Methods research and teaching at UBC is a key goal of the cluster, adding to existing momentum for diversifying AI faculty, including within the Computer Science Department.

AIM-SI will be organized as a cluster of Science faculty within UBC’s Centre for Artificial Intelligence Decision-making and Action (CAIDA). The cluster’s Director will be Kevin Leyton-Brown (CS); its Steering Committee will consist of Alexandre Bouchard-Côté (Stats), Cristina Conati (CS), Michiel van de Panne (CS), and Ozgur Yilmaz (Math).

Leyton-Brown explains the cluster's rationale. “Over the past several years, the university has faced unprecedented demand for AI teaching and AI research collaborations across all scientific disciplines. Better meeting this demand will lead to more scientific breakthroughs across the university and produce a much larger pool of highly-qualified graduates for Vancouver’s red-hot tech sector.”

As UBC's interdisciplinary AI research organization, CAIDA brings together over 100 professors and their research associates, spanning 27 departments and institutes within UBC. The centre's focus is the development, analysis, and application of AI systems for decision-making and action, enabled by core AI technologies such as machine learning and automated reasoning. CAIDA research spans eleven focus areas, ranging from fundamental to applied. To complement existing research collaborations at UBC, CAIDA organizes events such as seminar series (divided into streams on AI Methods, Applications, and Ethics), job fairs, industry outreach days, and open houses. CAIDA also represents UBC’s AI community in its relationships with CIFAR, Canada’s Digital Technology Supercluster, and other provincial, national, and international AI organizations.