Wei Hu, Department of Computer Science
Princeton University
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.
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.
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.
Ryan Goldade, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Christopher Batty
Justin Tracey, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Ian Goldberg
Wajih Ul Hassan, Department of Computer Science
University of Illinois at Urbana-Champaign
Karl Knopf, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Xi He
Mustafa Korkmaz, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Ken Salem
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.