Mohamed-Ali Belabbas

University of Illinois Urbana-Champaign, USA

Bio: Mohamed-Ali Belabbas obtained his MS and PhD degrees in applied mathematics from Harvard University, working with Prof. Roger Brockett, and undergraduate degrees from Ecole Centrale Paris, France, and Universite Catholique de Louvain, Belgium. He is currently an associate professor in the Electrical and Computer Engineering department in the Grainger College of Engineering at the University of Illinois, Urbana-Champaign and in the Coordinated Science Laboratory. His research interests are in Networked Control System, Geometric Control, Stochastic Control and their applications. He is a recipient of the 2024 Bessel Research Award from the Humboldt Foundation.

Florian Dörfler

ETH Zurich, Switzerland

Title of Speech: Population Dynamics Meet Network Optimization: A Decision-Dependent Stochastic Formulation

Abstract: As networked systems grow in scale, an emerging perspective is to describe the collective states or intrinsic uncertainty of a network with a continuum of agents via a probability measure. This view is relevant in many contexts of decision-making; for instance, a political party seeks to maximize the overall affinity across a population. In general, decision-makers select inputs to drive network-wide states through system dynamics, causing non-stationary population distributions in network optimization problems. We formulate this dynamic setting as stochastic optimization with decision dependence, featuring nonlinear population dynamics that couple the evolving distribution with the decision. This formulation provides a fertile ground to examine the various roles played by dynamics in the composite problem structure. To this end, we develop an online algorithm that achieves optimal decision-making by both adapting to and shaping the population distribution. We adopt a distributional view and demonstrate how this vantage point facilitates characterizations of population dynamics and the optimality of the proposed algorithm. We showcase the theoretical results in a recommender system example of affinity maximization with polarized population dynamics. This is joint work with Zhiyu He, Saverio Bolognani, and Michael Muehlebach.

Bio: Florian Dörfler is a Professor at the Automatic Control Laboratory at ETH Zürich. He received his Ph.D. degree in Mechanical Engineering from the University of California at Santa Barbara in 2013, and a Diplom degree in Engineering Cybernetics from the University of Stuttgart in 2008. From 2013 to 2014 he was an Assistant Professor at the University of California Los Angeles. He has been serving as the Associate Head of the ETH Zürich Department of Information Technology and Electrical Engineering from 2021 until 2022. His research interests are centered around automatic control, system theory, optimization, and learning. His particular foci are on network systems, data-driven settings, and applications to power systems. He is a recipient of the distinguished young research awards by IFAC (Manfred Thoma Medal 2020) and EUCA (European Control Award 2020). He and his team received best paper distinctions in the top venues of control, machine learning, power systems, power electronics, circuits and systems. They were recipient of the 2011 O. Hugo Schuck Best Paper Award, the 2012-2014 Automatica Best Paper Award, the 2016 IEEE Circuits and Systems Guillemin-Cauer Best Paper Award, the 2022 IEEE Transactions on Power Electronics Prize Paper Award, the 2024 Control Systems Magazine Outstanding Paper Award, and multiple Best PhD thesis awards at UC Santa Barbara and ETH Zürich. They were further winners or finalists for Best Student Paper awards at the European Control Conference (2013, 2019), the American Control Conference (2010,2016,2024), the Conference on Decision and Control (2020), the PES General Meeting (2020), the PES PowerTech Conference (2017), the International Conference on Intelligent Transportation Systems (2021), the IEEE CSS Swiss Chapter Young Author Best Journal Paper Award (2022,2024), the IFAC Conference on Nonlinear Model Predictive Control (2024), and NeurIPS (2024). He is currently serving on the council of the European Control Association and as a senior editor of Automatica.

Hideaki Ishii

The University of Tokyo, Japan

Bio: Hideaki Ishii received the M.Eng. degree from Kyoto University, Japan, in 1998,  and the Ph.D. degree from the University of Toronto, Canada, in 2002.He was a Postdoctoral Research Associate at the University of Illinois at Urbana-Champaign, USA, in 2001-2004, and a Research Associate at The University of Tokyo, Japan, in 2004-2007. He was an Associate Professor and then a Professor  at the Tokyo Institute of Technology, Japan, in 2007-2024. Currently, he is a Professor at the Department of Information Physics and Computing, The University of Tokyo, Japan. He was a Humboldt Research Fellow at the University of Stuttgart in 2014-2015. He has also held visiting positions at CNR-IEIIT at the Politecnico di Torino, the Technical University of Berlin, and the City University of Hong Kong. His research interests include networked control systems, multiagent systems, distributed algorithms, and cyber-security of control systems.

Dr. Ishii has served as an Associate Editor for Automatica,  the IEEE Control Systems Letters, the IEEE Transactions on Automatic Control,  the IEEE Transactions on Control of Network Systems, and the Mathematics of Control, Signals, and Systems. He was a Vice President for the IEEE Control Systems Society (CSS) in 2022-2023, the Chair of the IFAC Coordinating Committee on Systems and Signals in 2017-2023, and the Chair of the IFAC Technical Committee on Networked Systems for 2011-2017. He served as the IPC Chair for the IFAC World Congress 2023 held in Yokohama, Japan. He received the IEEE Control Systems Magazine Outstanding Paper Award in 2015. Dr. Ishii is an IEEE Fellow.

Karl Henrik Johansson

KTH Royal Institute of Technology, Sweden

Title of Speech: Observer Synthesis for Nonlinear Systems via Physics-Informed Learning

Abstract: A novel learning approach for designing Kazantzis-Kravaris-Luenberger (KKL) observers for autonomous nonlinear systems will be presented. The design of a KKL observer involves finding an injective map that transforms the system state into a higher-dimensional observer state with linear dynamics. The observer's state is then mapped back to the original system coordinates via the inverse map to obtain the state estimate. However, finding such a transformation and its inverse is challenging. We propose to sequentially approximate the maps by neural networks that are trained using physics-informed learning. We generate synthetic data for training by numerically solving the system and observer dynamics. Theoretical guarantees for the robustness of state estimation against approximation error and system uncertainties are provided. Additionally, a systematic method for optimizing observer performance through parameter selection is presented. The effectiveness of the proposed approach is demonstrated through numerical simulations on benchmark examples and its application to sensor fault detection and isolation in a network of Kuramoto oscillators using learned KKL observers. The presentation is joint work with Umar Niazi, John Cao, Matthieu Barreau, and Amritam Das.

Bio: Karl H. Johansson is Swedish Research Council Distinguished Professor in Electrical Engineering and Computer Science at KTH Royal Institute of Technology in Sweden and Founding Director of Digital Futures. He earned his MSc degree in Electrical Engineering and PhD in Automatic Control from Lund University. He has held visiting positions at UC Berkeley, Caltech, NTU and other prestigious institutions. His research interests focus on networked control systems and cyber-physical systems with applications in transportation, energy, and automation networks. For his scientific contributions, he has received numerous best paper awards and various other distinctions from IEEE, IFAC, and other organizations. He has been awarded Distinguished Professor by the Swedish Research Council, Wallenberg Scholar by the Knut and Alice Wallenberg Foundation, Future Research Leader by the Swedish Foundation for Strategic Research. He has also received the triennial IFAC Young Author Prize, IEEE CSS Distinguished Lecturer, IFAC Outstanding Service Award, and IEEE CSS Hendrik W. Bode Lecture Prize. His extensive service to the academic community includes being President of the European Control Association, IEEE CSS Vice President Diversity, Outreach & Development, and Member of IEEE CSS Board of Governors and IFAC Council. He has served on the editorial boards of Automatica, IEEE TAC, IEEE TCNS and many other journals. He has also been a member of the Swedish Scientific Council for Natural Sciences and Engineering Sciences. He is Fellow of both the IEEE and the Royal Swedish Academy of Engineering Sciences.

Yue Lu

Harvard University, USA

Bio: Yue M. Lu was born in Shanghai. After finishing undergraduate studies at Shanghai Jiao Tong University, he attended the University of Illinois at Urbana-Champaign, where he received the M.Sc. degree in mathematics and the Ph.D. degree in electrical engineering, both in 2007.

From September 2007 and October 2010, he was a postdoctoral researcher at the Audiovisual Communications Laboratory at Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He then joined Harvard University, where he is currently Gordon McKay Professor of Electrical Engineering and of Applied Mathematics and a Harvard College Professor. He is also fortunate to have held visiting appointments at Duke University in 2016 and at the École Normale Supérieure (ENS) in 2019.……<More>

Sonia Martinez

University of California, San Diego, USA

Bio: She received her PhD degree in Engineering Mathematics from the University Carlos III of Madrid, Spain, in May 2002. After this, she spent two years as a Fulbright Postdoctoral Scholar at the University of Illinois at Urbana-Champaign and the University of California, Santa Barbara. She started as an Assistant Professor at the University of California, San Diego in 2006; and became a Full Professor in Mechanical and Aerospace Engineering in 2014, being affiliated there ever since. She was honored to become a Jacobs Faculty Scholar in 2019. She is a Fellow of the IEEE, class 2018.

Her research interests span all aspects of the control of networked, multi-agent systems, including robotic teams and cyber-physical systems. She have particularly focused on the analysis and design of distributed coordination algorithms for groups of autonomous robots, by leveraging nonlinear control, distributed optimization, and game-theoretic approaches. Current topics of interest include the resilient, safe and robust coordination of mixed multi-agent systems subject to adversarial action.

Yinian Mao

Meituan, China

Bio: Dr. Yinian Mao is head of Meituan’s Drone Delivery Business and Vice President of Meituan. Dr. Mao has more than 20 years of industry experience with Meituan, Qualcomm, and Airlango Technology. He received his Ph.D. degree from University of Maryland, and B.S.E. from Tsinghua University, both in Electrical Engineering.

Shaoshuai Mou

Purdue University, USA

Title of Speech: Fundamental Mechanisms to Coordinate Multi-Agent Systems: Consensus and Beyond

Abstract: Multi-agent systems (i.e. a network of interconnected autonomous systems working as a cohesive whole) usually offer better autonomy than individual systems. Consensus, which requires all agents’ states to reach the same value, has served as one fundamental mechanism to coordinate multi-agent systems. There has been a tremendous amount of research devoted to developing consensus-based distributed algorithms for computation, optimization and reinforcement learning. In this talk, we will briefly introduce our recent progress in consensus-based distributed algorithms for solving linear equations and multi-agent optimization. We will also introduce conservation (i.e. the sum of agents’ states subject to a constant), and edge agreement (i.e. each pair of neighboring agents’ states subject to a linear or nonlinear constraint) to serve as fundamental mechanisms in coordination of multi-agent systems when the  coordination is more than reaching a global agreement in consensus or even dimensions of agents' states are different. In addition, we will present a distributed algorithms based on a simple combination of conversation and consensus for solving linear equations, which shows better scalability than distributed algorithms based on only consensus.

Bio: Shaoshuai Mou is the Elmer Bruhn associate professor in the School of Aeronautics and Astronautics at Purdue University. He received a Ph.D. in Electrical Engineering at Yale University in 2014, worked as a postdoc researcher at MIT for a year, and then joined Purdue University as a tenure-track assistant professor in Aug. 2015. His research group Autonomous & Intelligent Multi-agent Systems (AIMS) lab has been focusing on advancing control theories with recent progress in optimization, networks and learning to address fundamental challenges in autonomous systems, with particular research interests in multi-agent systems, control of autonomous systems, learning and adaptive systems, human-robot teaming, etc. Dr. Mou co-directs Purdue’s Institute for Control, Optimization and Networks (ICON) launched in 2020 consisting of about 100 faculty from 15 departments across Purdue University, with the aim of enhancing research collaboration and educational coordination in autonomous and robotics systems.

Maria Prandini

Politecnico di Milano, Italy

Bio: Maria Prandini is currently full professor in Automatic Control at Politecnico di Milano, Italy, where she has been Chair of the Automation and Control Engineering Study Program from 2019 to 2024. She was elected Fellow of the IEEE in 2020 and received the IEEE Control Systems Society (CSS) Distinguished Member award in 2018. In 2017, she was August-Wilhelm Scheer Visiting Professor and Honorary fellow of the TUM Institute for Advanced Studied. She is Visiting Professor in Engineering at the University of Oxford since 2022. She has been contributing to the IEEE CSS, the International Federation of Automatic Control (IFAC), and the Association for Computing Machinery (ACM)  in different roles. She is currently IFAC President-elect for the triennium 2023-26. Previously, she was Vice-President for conference activities for IFAC (2020-23) and IEEE CSS (2016 and 2017), and a member of SIGBED Board of Directors (2019-21). Her research interests include distributed and data-driven optimization, multi-agent systems, and the application of control theory to transportation and energy systems.

Thomas Parisini

Imperial College London, UK

Bio:  I received the "Laurea'' degree (Cum Laude and printing honours) in Electronic Engineering from the University of Genoa in 1988 and the Ph.D. degree in Electronic Engineering and Computer Science in 1993. I was with Politecnico di Milano, and I hold the Chair of Industrial Control at Imperial College London. I'm serving as the Head of the Control and Power Research Group and I served as Director of Research at the Dept. of Electrical and Electronic Engineering. I'm also Professor and Danieli Endowed Chair of Automation Engineering with University of Trieste. In 2009-2012, I have been appointed Deputy Rector of University of Trieste. 

In 2018, I received an Honorary Doctorate from University of Aalborg, Denmark. Picture below was taken at the Awards Ceremony at University of Aalborg showing the Rector conferring the Honorary Doctorate. I authored or co-authored a research monograph in the most prestigious series of Springer Nature and over 400 research papers in archival journals, book chapters, and international conference proceedings. My research interests include monitoring, diagnosis, control and security of large-scale critical infrastructure systems with applications in smart grids, transportation networks, power electronics and industrial process control. <More>

Lihua Xie

Nanyang Technological University (NTU), Singapore

Bio: Lihua Xie received the B.E. and M.E. degrees in electrical engineering from Nanjing University of Science and Technology in 1983 and 1986, respectively, and the Ph.D. degree in electrical engineering from the University of Newcastle, Australia, in 1992. Since 1992, he has been with the School of Electrical and Electronic Engineering, Nanyang Technological University (NTU), Singapore, where he is currently Professor and Director of $25M National Research Foundation Medium Sized Centre for Advanced Robotics Technology Innovation (CARTIN) and the founding Director, Delta-NTU Corporate Laboratory for Cyber-Physical Systems which received $45M funding from National Research Foundation of Singapore, Delta Electronics and NTU, and has over 100 researchers including 20 professors, 51 research staff and 30 PhD students. He served as the Head of Division of Control and Instrumentation from July 2011 to June 2014 and the Director, Center for E-City from July 2011 to June 2013. He held teaching appointments in the Department of Automatic Control, Nanjing University of Science and Technology from 1986 to 1989.

Dr. Xie’s research interests include robust control, networked control systems, multi-agent networks, indoor positioning, human activity recognition and unmanned systems. He has published 9 books, over 480 journal papers, 380 conference papers, and 17 patents/Technical Disclosures. He has been listed as a highly cited researcher (SCI: 25631, H-index: 77; Google Scholar: 41549, H-index: 99) by Thomson Routers and Clarivate Analytics annually since 2014. He has secured a total research grant of over 90 million Singapore dollars as programme and project PI, and graduated 38 PhD students. He has received many awards for his research including IBM Faculty Award, Changjiang Scholar Award from Ministry of Education of China, and best paper awards at 8 international conferences such as the Guan Zhao Zhi Award from the 29th and 39th Chinese Control Conference(CCC) both of which had over 2000 participants, 7th Asia Control Conference (ASCC), 18th International Conference on Advanced Robotics (ICAR 2017), etc. <More>

Wotao Yin

Alibaba Group, U.S.

Bio: TBD

Keyou You

Tsinghua University, China

Title of Speech: Asynchronous Methods for Distributed Optimization of Multi-agent Networks

Abstract: A popular asynchronousprotocol for distributed optimization is randomized gossip where a pair ofneighbors concurrently update via pairwise averaging. In practice, this createsdeadlocks and is vulnerable to information delays. In this talk, each node of distributedalgorithms updates asynchronously and independently, and does not wait for anyother node. To address the convergence issue of asynchronicity, we propose toadaptively increase the stepsizes of slow nodes to catch up fast nodes and showthe linear rate of for the strongly convex function, where thevirtual index k increases by 1 no matter on which node updates in themulti-agent network. The performance is validated on a logistic regressionproblem in terms of linear speedup and implementations.  

Bio: Keyou You received the B.S. degree in Statistical Science from Sun Yat-sen University, Guangzhou, China, in 2007 and the Ph.D. degree in Electrical and Electronic Engineering from Nanyang Technological University (NTU), Singapore, in 2012. After briefly working as a Research Fellow at NTU, he joined Tsinghua University in Beijing, China where he is now a Full Professor in the Department of Automation. He held visiting positions at Politecnico di Torino, Hong Kong University of Science and Technology, University of Melbourne and etc.

Prof. You’s research interests focus on the intersections between control, optimization and learning as well as their applications in autonomous systems. He received the Guan Zhaozhi award at the 29th Chinese Control Conference in 2010 and the ACA (Asian Control Association) Temasek Young Educator Award in 2019. He received the National Science Funds for Excellent Young Scholars in 2017, and for Distinguished Young Scholars in 2023. Currently, he is an Associate Editor for Automatica and IEEE Transactions on Control of Network Systems.