Join us on May 7, when Guest Lecturer Michael Muehlebach will hold an Online Lecture On A Dynamical Systems View on Accelerated Optimization!

The lecture is part of Tatjana Chavadarova’s “Games in Machine Learning” course.

Join us Online

The lecture will be streamed via Zoom (Meeting-ID: 679 6474 4944 and Password: s98ETetq)

A Dynamical Systems View on Accelerated Optimization

This lecture explores optimization algorithms through the lens of dynamical systems, highlighting how classical gradient methods, momentum schemes, and accelerated algorithms can be understood as discrete-time approximations of continuous dynamics. Starting from gradient descent and Nesterov acceleration, Michael Muehlebach will discuss how geometric and spectral viewpoints explain convergence rates, acceleration, and the role of conditioning in large-scale optimization. The lecture will then connect optimization theory to current questions in deep learning, including the edge-of-stability phenomenon and the implicit regularization effects of training dynamics. Michael Muehlebach will also discuss recent directions in structure-aware learning, with an emphasis on sparse and low-rank training methods for transformer models. Overall, the lecture shows how a dynamical systems perspective sheds light on classical optimization results while also informing the design of algorithms for modern deep learning problems.

About Michael Muehlebach

Michael Muehlebach studied mechanical engineering at ETH Zurich and specialized in robotics, systems, and control during his Master’s degree. He received his BSc and MSc in 2010 and 2013, respectively, before joining the Institute for Dynamic Systems and Control for his PhD. He graduated under the supervision of Prof. R. D’Andrea in 2018 and joined the group of Prof. Michael I. Jordan at the University of California, Berkeley as a postdoctoral researcher. In 2021, he started as an independent group leader at the Max Planck Institute for Intelligent Systems in Tübingen, where he leads the group “learning and dynamical systems”.

He is interested in a variety of subjects, including machine learning, dynamical systems, and optimization. During his PhD, he developed approximations to the constrained linear quadratic regulator problem, a central problem in control theory, and applied these to model predictive control. He also designed control and estimation algorithms for balancing robots and flying machines. His more recent work straddles the boundary between machine learning and optimization, and includes the analysis of momentum-based and constrained optimization algorithms from a dynamical systems point of view.

He received the Outstanding D-MAVT Bachelor Award for his Bachelor’s degree and the Willi-Studer prize for the best Master’s degree. His PhD thesis was awarded with the ETH Medal and the HILTI prize for innovative research. He was also awarded a Branco Weiss Fellowship, Emmy Noether Fellowship, and an Amazon research grant, which fund his research group. He is also member of the editorial board of Foundation and Trends in Machine learning.

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