AI Methods for Scientific Impact

The Artificial Intelligence Methods for Scientific Impact (AIM-SI) cluster: a new UBC Science interdisciplinary cluster will help fuel artificial intelligence innovation and research excellence over the next two years.

 

Tenure Track Positions:

UBC will hire five new assistant professors across the Departments of Computer Science (2 faculty members), Statistics (2 faculty members) and Mathematics (1 faculty member).

You can find the job postings through the links above.  Please note that some of the links are not currently active.

 

The Creation of AIM-SI:

Artificial Intelligence (AI) is fueling profound innovation across science, the economy, and society, with UBC students and faculty at the forefront of many advancements.

The creation of AIM-SI in August 2021 will significantly increase UBC’s teaching and research capacity in AI, thereby meeting a major need: 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.

Over the next two years, UBC will hire five new assistant professors across the Departments of Computer Science (2 faculty members), Statistics (2 faculty members) and Mathematics (1 faculty member). The new hires for the cluster will be funded as part of the President’s Academic Excellence Initiative (PAEI), which is driving the largest recruitment of faculty in UBC history. 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 another key goal of the cluster, adding to existing momentum for diversifying our AI faculty, including within the Computer Science Department.
 

 

“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.”
 

 

 

AIM-SI Vision:

  • Meet the immediate demand for teaching, supervision, and collaboration in AI at UBC.
  • AI is currently transitioning from being a specialization to a field in its own right; AIM-SI intends to put UBC's AI offerings on a firm, interdisciplinary foundation.
  • Increase UBC's strength in AI methods beyond the "steady state" we have maintained over the past fifteen years, making us more comparable to other top institutions both in Canada and worldwide, which have focused on rapidly growing their AI headcounts.
  • Help fuel artificial intelligence innovation and research excellence through the hire of interdisciplinary faculty

 

Artificial Intelligence at UBC:

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

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.

 

AIM-SI Collaborators:

Benjamin Bloem-Reddy Statistics
Alexandre Bouchard-Côté Statistics
Trevor Campbell Statistics
Jeff Clune Computer Science
Cristina Conati Computer Science
Eldad Haber Earth and Ocean Sciences
Kevin Leyton-Brown Computer Science
Mijung Park Computer Science
Yaniv Plan Math
Elina Robeva Math
Mark Schmidt Computer Science
Vered Shwartz Computer Science
Leonid Sigal Computer Science
Danica Sutherland Computer Science
Michiel van de Panne Computer Science
Frank Wood Computer Science
Kwang Moo Yi Computer Science
Ozgur Yilmaz Math

 

Committed to Excellence, Collaboration, and Inclusion

Equity and diversity are essential to academic excellence. An open and diverse community fosters the inclusion of voices that have been underrepresented or discouraged. We encourage applications from members of groups that have been marginalized on any grounds enumerated under the B.C. Human Rights Code, including sex, sexual orientation, gender identity or expression, racialization, disability, political belief, religion, marital or family status, age, and/or status as a First Nation, Metis, Inuit or Indigenous person.

 

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