Welcome to Mila Tea Talks!
What are tea talks ?
Tea talks are scientific keynote-like talks given at Mila on Friday mornings (10:30am EDT). We either have an internal or invited speaker give a ~1 hour talk with about 15 minutes of questions interspersed throughout. These talks are open to the public, but the vast majority of attendees are Mila students and professors, so the talks are aimed at that level of understanding. We also encourage our speakers to stay after the talk and interact with students in one-on-one or group meetings to discuss research interests and ideas, and we're happy to organize that as well.
What's in this webpage?
You will find the recording and slides (when available) of the past tea talks. We ordered them by session.
Would like to get schedule notifications ?
Simply click here with a quick description of who you are ( XX from YY is enough) to join the external mailing list.
The schedule is also publicly available on Mila website
Would like to suggest a speaker?
You can email us (admin-teatalks@googlegroups.com) and/or fill the suggestion form here!
LiveStream
Talks are Live-streamed on Google Meet. Link here.
Current Session>WINTER 24
Friday, 31 May, 2024
Thomas Bury
Recording : TBD
Deep learning for early warning signals of tipping points
Tipping points — abrupt changes in the state of a dynamical system — can occur in systems ranging from the Earth’s climate to the human heart, often with dire consequences. An abundance of research has focused on the development of early warning signals for tipping points based on generic properties of dynamical bifurcations, such as critical slowing down. In our work, we have trained a deep learning classifier to predict tipping points using a massive library of randomly generated dynamical systems. I will show how the classifier generalises to predicting tipping points in real ecological and cardiac systems, and does so with greater accuracy than conventional indicators. Finally, I will present recent work on using reinforcement learning to discover triggers for cardiac arrhythmia by interacting with a mathematical model for cardiac tissue. This talk will highlight the utility of combining deep learning with dynamical systems to better understand natural systems on which humanity relies.