2021-2022 Machine Learning Advances and Applications Seminar
Description
This seminar series is the first formal gathering of academic and industrial data scientists across the Greater Toronto Area (GTA) to discuss advanced topics in machine learning and its goal is to build a stronger machine learning community in Toronto. The talks will be given by international and local faculty and industry professionals.
The seminar series is intended for university faculty and graduate students in machine learning across computer science, ECE, statistics, mathematics, linguistics, and medicine, as well as PhD-level data scientists doing interesting applied research in the GTA.
A large emphasis will be placed on the social aspects of the gathering. The Toronto machine learning community will be stronger when we know each other and know what problems people are working on.
Zoom link will be provided upon registration
Schedule
| 15:00 to 16:00 |
Massimiliano Pontil, University College London |
| 15:00 to 16:00 |
Radford Neal, University of Toronto |
| 15:00 to 16:00 |
No Title Specified
Anima Anandkumar, California Institute of Technology |
| 15:00 to 16:00 |
Cynthia Rudin, Duke University |
| 15:00 to 16:00 |
Philipp Hennig, University of Tübingen |
| 15:00 to 16:00 |
Jan Peters, Technische Universitaet Darmstadt |
| 15:00 to 16:00 |
David Pfau, Google DeepMind |
| 15:00 to 16:00 |
Matthew Johnson, Google |
| 15:00 to 16:00 |
Joan Bruna Estrach, New York University |
| 15:00 to 16:00 |
Andreas Krause, ETH Zürich |
| 15:00 to 15:30 |
Michael Brudno, Vector Institute |
| 15:30 to 16:00 |
Rahul Krishnan, University of Toronto |
| 15:00 to 16:00 |
Lester Mackey, Microsoft/Stanford University |
| 15:00 to 16:00 |
Courtney Paquette, McGill University |
