Events

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 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 PhD seminar will be given online.

Justin Tracey, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Ian Goldberg

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.

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 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.

Jiliang Tang, Department of Computer Science and Engineering
Michigan State University

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 talk will be given online.

Andrew Beach, Master’s candidate
David R. Cheriton School of Computer Science