Organized by Tim Kim, Yiliu Wang, and Adrienne Fairhall

12:00pm – 1:00pm: Lunch and introduction

Session 1(1:00pm – 2:00pm)

1:00 – 1:30

Rajesh Rao, Professor, Computer Science & Engineering

“Brain state-space models: From Kalman filters to active predictive coding”

1:30 – 1:45

   Ziyu Lu, Graduate Student, Applied Mathematics

   “Benchmarking Probabilistic Time Series Forecasting Models on Neural Activity”

1:45 – 2:00

   Yiliu Wang, Shanahan Fellow, Allen Institute

   “Interpretable time series analysis with Gumbel dynamics”

 

Coffee break (2:00pm – 2:30pm)

 

Session 2 (2:30pm – 3:30pm)

 

2:30 – 3:00

Nathan Kutz, Professor, Applied Mathematics

“Learning dynamics with shallow recurrent decoders”

3:00 – 3:15

   Nick Zolman, Graduate Student, Mechanical Engineering

“Characterizing Extreme Events in Turbulent Flows through Sensitivity-Based Modal Decomposition”

3:15 – 3:30

   Elliott Abe, Postdoctoral Fellow, Biology

“TiDHy: Timescale Demixing via Hypernetworks to learn simultaneous dynamics from mixed observations”

Coffee break (3:30pm – 4:00pm)

Session 3 (4:00pm – 5:00pm)

4:00 – 4:30

Matthew Golub, Assistant Professor, Computer Science & Engineering

“Deep Learning Dynamical Systems for Identifying Communication Between Brain Regions”

4:30 – 4:45

   James Hazelden, Graduate Student, Applied Mathematics

“KP-Flow: Uncovering Implicit Constraints in Recurrent GD Learning”

4:45 – 5:00

   Ulises Pereira