Speakers

Distributionally Robust Barriers for Safe Robot Motion in Dynamic Environments

Nikolay Atanasov is an Associate Professor in the Department of Electrical and Computer Engineering at the University of California San Diego, La Jolla, CA, USA. He obtained a B.S. degree in Electrical Engineering from Trinity College, Hartford, CT, USA in 2008, and M.S. and Ph.D. degrees in Electrical and Systems Engineering from University of Pennsylvania, Philadelphia, PA, USA in 2012 and 2015, respectively. Dr. Atanasov's research focuses on robotics, control theory, and machine learning with emphasis on active perception problems for autonomous mobile robots. He works on probabilistic models and inference techniques for simultaneous localization and mapping (SLAM) and on optimal control and reinforcement learning techniques for autonomous robot navigation and uncertainty minimization. Dr. Atanasov's work has been recognized by the Joseph and Rosaline Wolf award for the best Ph.D. dissertation in Electrical and Systems Engineering at the University of Pennsylvania in 2015, the Best Conference Paper Award at the IEEE International Conference on Robotics and Automation (ICRA) in 2017, the NSF CAREER Award in 2021, and the IEEE RAS Early Academic Career Award in Robotics and Automation in 2023.

Sergio Grammatico, TU Delft

Distributionally robust Nash equilibrium problems

Sergio Grammatico is an Associate Professor at the Delft Center for Systems and Control, TU Delft. He received his PhD in Automatic Control from the University of Pisa (2013) and held research positions at ETH Zurich (2013–2015) and TU Eindhoven (2015–2017). His research interests revolve around game theoretic control and optimization for complex systems, for which he was awarded an ERC Starting Grant (2018) and an ERC Consolidator Grant (2025). Dr. Grammatico is a recipient of the 2026 European Control Award, of the 2025 IEEE Transactions on Control of Network Systems Best Paper Award, of the 2021 Roberto Tempo Best Paper Award, of the 2016 International Conference on Network Games, Control and Optimization Best Paper Award, and a co-author of the 2022 IEEE CSS Italy Young Author Best Journal Paper Award. He is an IEEE SYSC Distinguished Lecturer, and he serves as an Associate Editor of IEEE Transactions on Automatic Control and Automatica.

Sophie Hall, ETH Zürich

Carbon-Aware Computing for Data Centers with Probabilistic Performance Guarantees

Sophie Hall is a PhD student at the Automatic Control Laboratory at ETH Zürich since May 2021, working in Prof. Dörfler's group. She completed her undergraduate studies in Mechanical Engineering focusing on medical engineering, control and signal processing at the University of Surrey, UK, and Nanyang Technological University, Singapore. In 2021, she obtained her MSc in Biomedical Engineering from ETH Zürich specializing in modeling and control.

Her PhD research focuses on game-theoretic MPC, its theoretical closed-loop properties, as well as energy and groundwater applications. In addition, she works on carbon-aware computing for global load shifting in collaboration with Google.

Dario Paccagnan, Imperial College London

Strategically robust game theory via optimal transport

Dario Paccagnan is an Associate Professor at the Department of Computing, Imperial College London where he joined in the Fall 2020. Before that, he was a postdoctoral fellow with the Center for Control, Dynamical Systems and Computation, University of California, Santa Barbara. He obtained his PhD from the Automatic Control Laboratory, ETH Zurich, Switzerland, in 2018. He received a B.Sc. and M.Sc. in Aerospace Engineering from the University of Padova, Italy, in 2011 and 2014, and a M.Sc. in Mathematical Modelling and Computation from the Technical University of Denmark in 2014; all with Honors. Dario's interests are at the interface of game theory, control theory, and learning theory with a focus on tackling societal-scale challenges. Dario was a finalist for the 2019 EECI best PhD thesis award and was recognized with the SNSF Early Postdoc Mobility Fellowship, the SNSF Doc Mobility Fellowship, and the ETH medal for his doctoral work. He is the recipient of the best student paper award (as advisor) at AISTATS 2025.

Giovanni Russo, Università di Salerno

The Distributionally Robust Free Energy Principle: Optimize Interactions, Not Just Policies

Giovanni Russo is an Associate Professor of Automatic Control at the University of Salerno, Italy. He was previously with the University of Naples Federico II (Ph.D. in 2010), Italy; Ansaldo STS (System Engineer/Integrator of the Honolulu Rail Transit Project in 2012–2015), Hawaii, USA; IBM Research Ireland (Research Staff Member in Optimization, Control and Decision Science, from 2015 to 2018) and University College both in Dublin, Ireland (in 2018–2020). Dr. Russo has served as Associate Editor for the IEEE Transactions on Circuits and Systems I: regular papers (2016–2019) and the IEEE Transactions on Control of Network Systems (2017–2023). Since January 2024, Dr. Russo is serving as Senior Editor for the IEEE Transactions on Control of Network Systems. He is also a member of the Board of Directors of the Modelling and Engineering Risk and Complexity Ph.D. program at the School for Advanced Studies in Naples. His research interests include control in the space of densities, contraction theory, analysis/control of nonlinear systems and networks, data-driven control, neuro-inspired computation and learning.

Soroosh Shafiee, Cornell University

Wasserstein Distributionally Robust Online Learning

Soroosh Shafiee is an assistant professor in the School of Operations Research and Information Engineering at Cornell University. Before that, he held positions as a postdoctoral researcher at both the Tepper School of Business at Carnegie Mellon University and the Automatic Control Laboratory at ETH Zurich. He held a B.Sc. and M.Sc. degree in Electrical Engineering from the University of Tehran and a Ph.D. degree in Management of Technology from EPFL. His primary research interests revolve around low-complexity decision-making, optimization under uncertainty and optimal transport. He is a recipient of the NSF CAREER Award and the SNF Postdoc.Mobility Fellowship.

Bartolomeo Stellato, Princeton University

Distributionally Robust Analysis and Design of First-Order Methods

Bartolomeo Stellato is an Assistant Professor in the Department of Operations Research and Financial Engineering at Princeton University. He holds associated faculty positions in both the Department of Electrical and Computer Engineering and the Department of Computer Science. Additionally, he is affiliated with the AI at Princeton initiative, the Princeton Institute for Computational Science and Engineering, the Center for Statistics and Machine Learning, and the Robotics at Princeton initiative. He is also a fellow at Princeton's Whitman and Yeh colleges. His research lies at the interface of mathematical optimization, machine learning, and optimal control, focusing on developing data-driven computational tools to make decisions in highly dynamic and uncertain environments.

Bahar Tașkesen, University of Chicago Booth School of Business

Distributionally Robust Linear Quadratic Control

Bahar Tașkesen is an Assistant Professor of Operations Management and Liew Family Junior Faculty Fellow at the University of Chicago Booth School of Business. In June 2024, she completed her PhD in the Risk Analytics and Optimization Lab at École polytechnique fédérale de Lausanne (EPFL) in Switzerland. In 2018, she obtained her Bachelor of Science degree in Electrical and Electronics Engineering from the Middle East Technical University.

Google Sites

Report abuse