Events

Please note: This PhD seminar will take place online.

Alessandra Luz, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Daniel Vogel

Please note: This PhD defence will take place online.

Yuhao Dong, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Raouf Boutaba

Please note: This seminar will take place in DC 1302.

Roswitha Rissner, Department of Mathematics
Alpen-Adria-Universität Klagenfurt, Austria

Given a square matrix B' over a (commutative) ring S, the null ideal N_0(B') is the ideal consisting of all polynomials f in S[X] for which f(B')=0. In the case that S=R/J is the residue class ring of a ring R modulo an ideal J, we can equivalently study the so-called J-ideals

N_J(B) =  { f in  R[X]  |  f(B) in M_n(J) }

Please note: This master’s thesis presentation will take place in DC 1304 and virtually.

Benjamin Thérien, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Krzysztof Czarnecki

Please note: This seminar will take place in DC 1304 and virtually over Zoom.

Jason Li, Postdoctoral Fellow
Simons Institute, University of California, Berkeley

Please note: This master’s thesis presentation will take place in DC 3317 and virtually over Zoom.

Eva Feng, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor David Toman

Please note: This PhD seminar will take place online.

Greg Philbrick, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Craig Kaplan

This paper treats the subject of pseudo-3D modeling (via drawing in projective coordinates). I'll talk about the authors’ methods, as well as my own exploration of pseudo-3D drawing techniques.

Please note: This master’s thesis presentation will take place online.

Odunayo Ogundepo, Master’s candidate
David. R. Cheriton School of Computer Science

Supervisor: Professor Jimmy Lin

Please note: This seminar will take place virtually over Zoom.

Pavel Izmailov, PhD candidate
Computer Science Department, New York University

Please note: This seminar will take place in DC 2585.

Felix Dangel, Postdoctoral Researcher
Vector Institute for Artificial Intelligence

Popular deep learning frameworks prioritize computing the average mini-batch gradient. Yet, other quantities such as its variance or many approximations to the Hessian can be computed efficiently, and at the same time as the gradient mean. They are of great interest to researchers and practitioners, but implementing them is often burdensome or inefficient.