Please note: This seminar will be given online.
Danfei Xu, Computer Science Department
Stanford University
Please note: This master’s thesis presentation will be given online.
MohammadReza Karegar, Master’s candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Lukasz Golab
Laurie Williams
Distinguished University Professor
Department of Computer Science, North Carolina State University
Please note: This PhD seminar will be given online.
Nafisa Anzum, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Semih Salihoglu
Please note: This talk will be given online.
Andrew Beach, Master’s candidate
David R. Cheriton School of Computer Science
Please note: This PhD defence will be given online.
Bryce Sandlund, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor J. Ian Munro
This thesis considers the study of data structures from the perspective of the theoretician, with a focus on simplicity and practicality. We consider both the time complexity as well as space usage of proposed solutions. Topics discussed fall in three main categories: partial order representation, range modes, and graph cuts.
Please note: This seminar will be given online.
Andrea Tagliasacchi, Research Scientist
Google Brain
It is not uncommon to think of computer graphics and computer vision as loosely disconnected disciplines; the former dealing with the synthesis of visual phenomena and the latter with analysis. However, recent advances in deep learning have blurred the boundary between the two. As a consequence, the research path to develop algorithms that effectively interpret the 3D scene “behind” an image has never seemed so well within reach.
Please note: This seminar will be given online.
Jiliang Tang, Department of Computer Science and Engineering
Michigan State University
Please note: This PhD defence will be given online.
Rina R. Wehbe, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Edward Lank
Please note: This PhD seminar will be given online.
Anil Pacaci, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Tamer Özsu
Modern applications in many domains now operate on high-speed streaming graphs that continuously evolve at high rates. Efficient querying of these streaming graphs is a crucial task for applications that monitor complex patterns and relationships.
Please note: This seminar will be given online.
Deepak Narayanan, Department of Computer Science
Stanford University
Deep Learning models have enabled state-of-the-art results across a broad range of applications; however, training these models is extremely time- and resource-intensive, taking weeks on clusters with thousands of expensive accelerators in the extreme case.
Please note: This PhD defence will be given online.
Mustafa Korkmaz, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Ken Salem
Please note: This PhD seminar will be given online.
Karl Knopf, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Xi He
Please note: This seminar will be given online.
Wajih Ul Hassan, Department of Computer Science
University of Illinois at Urbana-Champaign
Please note: This PhD seminar will be given online.
Justin Tracey, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Ian Goldberg
Please note: This PhD defence will be given online.
Ryan Goldade, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Christopher Batty
Please note: This seminar will be given online.
Weihao Kong, Postdoctoral researcher
Department of Computer Science, University of Washington
In this talk, I will discuss several examples of my research that reveal a surprising ability to extract accurate information from modest amounts of data.
Please note: This seminar will be given online.
Florian Tramèr, Computer Science Department
Stanford University
Failures of machine learning systems can threaten both the security and privacy of their users. My research studies these failures from an adversarial perspective, by building new attacks that highlight critical vulnerabilities in the machine learning pipeline, and designing new defenses that protect users against identified threats.
Please note: This PhD seminar will be given online.
Akshay Ramachandran, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Lap Chi Lau
The matrix normal model, the family of Gaussian matrix-variate distributions whose covariance matrix is the Kronecker product of two lower dimensional factors, is frequently used to model matrix-variate data. The tensor normal model generalizes this family to Kronecker products of three or more factors.
Please note: This seminar will be given online.
Wei Hu, Department of Computer Science
Princeton University
Please note: This seminar will be given online.
Mengye Ren, Department of Computer Science
University of Toronto
Over the past decades, we have seen machine learning making great strides in AI applications. Yet, most of its success relies on training models offline on a massive amount of data and evaluating them in a similar test environment. By contrast, humans can learn new concepts and skills with very few examples, and can easily generalize to novel tasks.
Please note: This PhD seminar will be given online.
Georgios Michalopoulos, PhD candidate
David R. Cheriton School of Computer Science
Supervisors: Professors Ian McKillop and Helen Chen
Please note: This seminar will be given online.
Ellen Vitercik, Computer Science Department
Carnegie Mellon University
Please note: This PhD seminar will be given online.
Xiang Fang, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Stephen Mann
A new set of schemes are designed to create smooth surfaces with continuous curvatures or higher order continuity for triangular scattered data sites, without complex computation.
Please note: This seminar will be given online.
Dani Yogatama, Research Scientist
DeepMind
