October 2, 2023
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
Dominic Groß (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
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
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
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
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
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...
December 8, 2023
Jesús Rodríguez-Molina (Technical University of Madrid)
In the contemporary era, Sustainable Development Goals (SDGs) serve as the backbone of global efforts to ensure a future that harmonizes environmental conservation, social equity, and economic growth. One of the critical challenges facing these...
February 5, 2024
Urbashi Mitra (USC)
Many agent-based systems interacting with their environments are well-modeled by Markov Decision Processes (MDPs). MDPs also form the ``model ‘’ framework for many learning strategies such as reinforcement learning. We focus on policy design to...
February 26, 2024
Tamer Başar (UIUC)
Decision-making in dynamic uncertain environments with multiple agents having possibly misaligned objectives arises in many disciplines and application domains, including control (particularly networked control, such as control and operation of...
March 11, 2024
Daniel Liberzon (UIUC)
In this talk we will discuss estimation entropy for continuous-time nonlinear systems, which is a variant of topological entropy formulated in terms of the number of functions that approximate all system trajectories up to an exponentially decaying...
March 18, 2024
Jason Speyer (UCLA)
A recursive, analytic, real-time state estimation algorithm for linear and nonlinear systems, referred to as the Multivariate Cauchy Estimator (MCE), is presented. The algorithm enables robust state estimation performance for applications where the...
April 1, 2024
R. Srikant (UIUC)
We consider a version of a policy optimization in reinforcement learning where one has to learn rewards through human feedback. We study the sample complexity of this algorithm and compare it to the sample complexity of an algorithm where the...
April 8, 2024
Naira Hovakimyan (UIUC)
Learning-based control paradigms have seen many success stories with various robots and co-robots in recent years. However, as these robots prepare to enter the real world, operating safely in the presence of imperfect model knowledge and external...
April 22, 2024
Francesco Bullo (UC Santa Barbara)
I survey recent advances on contraction theory for dynamical systems, as a robust, computationally-friendly and modular stability theory. Starting from basic notions, I will present novel theoretical properties and examples of contracting dynamics,...
April 29, 2024
Na Li (Harvard)
The explosive growth of machine learning and data-driven methodologies have revolutionized numerous fields. Yet, their application to real-world dynamical physical systems remains a significant challenge. Closing the loop from data to actions in...
May 13, 2024
Sanjog Misra (University of Chicago)
Humans have an amazing ability to describe the structure of the world in ways that allows for constraints, realisms and boundaries to be respected. This structure facilitates the notion of counterfactuals which is a fundamental element of any...
October 28, 2024
Murat Arcak (UC Berkeley)
“Data-Driven Approaches for Estimating Reachable Sets in Complex Dynamical Systems”
The computation of reachable sets is essential for characterizing and verifying the behavior of safety-critical systems. However, many practical systems are high-...
November 18, 2024
Bassam Bamieh (UC Santa Barbara)
“Architectural Questions in Distributed Systems and Controls”
In the design and control of large-scale distributed systems, architectural considerations are arguably some of the most important ones. These include local versus global feedback in...
November 25, 2024
Miloslav Krstic (UCSD)
"PDE Backstepping: Offline Learning Enables Online-Adaptive Control"
The talk combines a history lecture, commissioned for the 2024 IEEE Control History Forum, with the 2023 IEEE Bode Prize Lecture, on the future of model-based control in the era of...
