Current students

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. 

WATORACE — including Cheriton School of Computer Science and Software Engineering students Kyle Anderson (SE), Sinclair Hudson (CS), Ryan Larkin (CS), Dmitry Tsarapkine (CS) and Ben Zhang (BCS’20) — won fourth place in the Indy Autonomous Challenge (IAC) virtual race #3, also capturing the Rising Star Award.

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.

Our bodies are made of trillions of cells that form tissues and organs. The genes inside the nucleus of each cell code for proteins that determine a cell’s structure and function, as well as instruct a cell when to grow, divide and die. Normally, our cells follow these instructions, but if a cell’s DNA mutates it can cause the cell to divide and grow out of control. Cancer is fundamentally a disease of uncontrolled cell growth and regulation, and all cancers ultimately are caused by mutations to the genes that regulate cell division, growth and differentiation.

The International Collegiate Programming Contest is the oldest, largest and most prestigious university-level algorithmic programming contest in the world. Each year, teams of three programmers represent their university as they work together to solve real-world problems, while fostering collaboration, creativity, innovation, and the ability to perform under pressure. 

Thursday, March 11, 2021 12:00 pm - 12:00 pm EST (GMT -05:00)

Seminar • Machine Learning — Steps Towards Making Machine Learning More Natural

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.