LIDS Seminar Series

The LIDS seminar series serves as a focal point in the intellectual life of the lab. Seminar videos, when available, can be found on the LIDS YouTube channel.

October 2, 2023

Complexity of Finding Local Minima in Continuous Optimization

Amir Ali Ahmadi (Princeton)

Can we efficiently find a local minimum of a nonconvex continuous optimization problem? We give a rather complete answer to this question for optimization problems defined by polynomial data. In the unconstrained case, the answer remains positive...

October 16, 2023

Beyond Low-Inertia Systems: Grid-Forming Control Foundations for Converter-Dominated Power Systems

Dominic GroƟ (University of Wisconsin-Madison)

At the heart of the transition to a zero-carbon power system is a technological paradigm shift from conventional generation to renewable generation connected to the grid via power electronics. In this context, the literature and public debate mostly...

October 25, 2023

Inferential Artificial Intelligence (iAI): Cases Studies in Computational Statistics, Machine Learning, and Global Health

Seth Flaxman (Oxford)

Machine learning is the computational beating heart of the modern AI renaissance. Behind the hype, a range of machine learning and computational statistical methods are quietly revolutionizing our approach to difficult statistical and scientific...

October 30, 2023

Formal Methods for Safety-Critical Control

Calin Belta (Boston University)

In control theory, complicated dynamics such as systems of (nonlinear) differential equations are mostly controlled to achieve stability and to optimize a cost. In formal synthesis, simple systems such as finite state transition graphs modeling...

November 13, 2023

Control-Theory Concepts in Systems Biology and Algorithms: Responses to Inputs, Transients, and Asymptotic Behaviors

Eduardo Sontag (Northeastern )

This talk will focus on systems-theoretic and control theory tools that help characterize the responses of nonlinear systems to external inputs, with an emphasis on how network structure constrains finite-time, transient behaviors. Of interest are...

November 27, 2023

Collaborative learning for control

James Anderson (Columbia University )

We begin by studying the problem of learning a linear system model from the observations of M clients. The catch: Each client is observing data from a different dynamical system. This work addresses the question of how multiple clients...

December 4, 2023

Learning from Dynamics

Ankur Moitra (MIT)

Linear dynamical systems are the canonical model for time series data. They have wide-ranging applications and there is a vast literature on learning their parameters from input-output sequences. Moreover they have received renewed interest because...