Friday, November 15, 2019

9:30am-6:00pm
The Skylight Room (Rm. 9100) at The Graduate Center, CUNY

As we learn more and more about increasingly complex systems, there is a tendency for our models to correspondingly grow in complexity. Is it possible to tame this complexity and develop methods for simplifying complex models into their essential ingredients? In this symposium, we study when model simplification is possible in systems ranging from biochemical networks to artificial neural networks. 

9:30 AM Coffee and bagels

10:00 AM Using simple models to understand complex processes
Mark Transtrum, Brigham Young University

11:30 AM Coffee

12:00 PM Neural network pruning and the lottery ticket hypothesis
Jonathan Frankle, Massachusetts Institute of Technology

1:30 PM Lunch

2:30 PM Finding and explaining structural hierarchies in complex systems
Katherine Quinn, The Graduate Center

4:00 PM Coffee

4:30 PM No equations, no variables, no parameters
Yannis Kevrekidis, Johns Hopkins University 

Register here.
Download Event Flier here.
Download Full Series PDF here.
Download Full Fall 2019 Events PDF here.

Sponsored by the Initiative for the Theoretical Sciences, and by the CUNY doctoral programs in Physics and Biology. Supported in part by the Center for the Physics of Biological Function, a joint effort of The Graduate Center and Princeton University. For more information please visit https://itsatcuny.org and https://biophysics.princeton.edu.