جستجوی استاد راهنمای دانشگاهی

• Adaptation and learning theories
• Data and network sciences
• Statistical inference
• +۷ مورد دیگر

Ali H. Sayed is the Dean of the School of Engineering (Faculté des sciences et techniques de l'ingénieur - STI) at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, where he also directs the Adaptive Systems Laboratory (Laboratoire de systèmes adaptatifs). Previously, he served as an emeritus professor and chair of the Electrical Engineering Department at UCLA. He is a highly cited researcher and a member of the US National Academy of Engineering and the World Academy of Sciences. Sayed served as president of the IEEE Signal Processing Society in 2018 and 2019. Professor Sayed's research focuses on adaptation and learning theories, data and network sciences, statistical inference, multi-agent systems, adaptive networks, and optimization. His work bridges theoretical foundations with practical applications in signal processing, machine learning, and network science. He has made significant contributions to distributed learning algorithms, social learning over networks, and adaptive signal processing techniques that have influenced both academic research and practical implementations. His recent publications demonstrate a strong focus on multi-agent systems, distributed learning, privacy-preserving techniques, and social learning over networks. The research trends show increasing emphasis on federated learning with privacy guarantees, graph-based learning approaches, and the intersection of social dynamics with information processing. His work consistently addresses fundamental theoretical questions while maintaining relevance to practical applications in communication networks, social media analysis, and distributed artificial intelligence systems. Professor Sayed has received numerous prestigious awards throughout his career, including: IEEE Fourier Award (2022) Norbert Wiener Society Award (2020) IEEE Signal Processing Society Education Award (2015) Papoulis Award from the European Association for Signal Processing (2014) Technical Achievement Award from IEEE Signal Processing Society (2012) Terman Award from the American Society for Engineering Education (2005) IEEE Donald G. Fink Prize (1996) Multiple Best Paper Awards from IEEE and EURASIP Sayed has authored or co-authored over 570 publications and six monographs. He has mentored numerous PhD students and researchers in the fields of signal processing and adaptive systems. His editorial leadership includes serving as Editor-in-Chief of IEEE Transactions on Signal Processing (2003-2005) and EURASIP Journal on Advances in Signal Processing (2006-2007), as well as Founding Editor-in-Chief of the Open Access Book Series on Information and Learning Sciences. At EPFL, Professor Sayed leads the Adaptive Systems Laboratory, which focuses on developing theoretical frameworks and practical algorithms for adaptive systems, networked learning, and distributed signal processing. The lab's research encompasses both fundamental theoretical investigations and applications to real-world problems in communications, social networks, and computational biology.

Giorgia Ramponiمشاهده پروفایل

استادیار

• Machine Learning
• Mathematical Modelling
• Reinforcement Learning
• +۳ مورد دیگر

Giorgia Ramponi is an Assistant Professor with Tenure Track at the Faculty of Business, Economics and Informatics at the University of Zurich. She is also an affiliated professor at the ETH AI Center and the Data Science and AI, Computer Science and Engineering department at Chalmers University of Technology. Her educational background includes a Ph.D. in Information Technology from Politecnico di Milano (completed June 2021 with honors), advised by Marcello Restelli, and a Master of Science in Computer Science with Honours Programme (110/110 cum laude) from la Sapienza (July 2017), advised by Flavio Chierichetti and Alessandro Panconesi. Dr. Ramponi's research focuses on machine learning and mathematical modeling, with particular emphasis on reinforcement learning and multiagent learning. Her work bridges theoretical foundations with practical applications, exploring how learning algorithms can make optimal decisions in complex environments. She has made significant contributions to areas including inverse reinforcement learning, multi-agent systems, constrained Markov decision processes, and human-AI interaction through preference learning. Her recent publications demonstrate a strong trend toward addressing fundamental challenges in reinforcement learning, particularly in multi-agent settings, constrained optimization, and learning from human feedback. Her work combines theoretical rigor with practical applications across robotics, economics, and decision-making systems. Hassler Research Grant for "Unified Feedback Integration Framework for Reinforcement Learning" Dr. Ramponi actively contributes to the academic community through conference participation, invited lectures (including at the Mediterranean Machine Learning Summer School), and teaching. She designed and taught the "Data Science and Machine Learning" course for the ETH-Ashesi Master program. She is also a member of the ELLIS community, which connects excellence in AI research across Europe. Her research group focuses on developing frameworks for reinforcement learning with various feedback types, including preferences, rewards, and demonstrations. The group aims to advance the theoretical understanding of learning algorithms while addressing practical challenges in real-world applications.

Maryam Kamgarpourمشاهده پروفایل

استادیار

• Control Theory
• Game Theory
• Mechanism Design
• +۶ مورد دیگر

Maryam Kamgarpour is a Tenure Track Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL), School of Engineering. She previously held faculty positions at the University of British Columbia and ETH Zürich. Her work bridges stochastic control , multiagent learning , and game theory , focusing on safety-critical systems. Education: PhD in Engineering from UC Berkeley, BSc in Applied Science from University of Waterloo. Research Interests: Control under uncertainty, game theory, mechanism design, mixed-integer optimization, and applications to transportation, robotics, power grids, and healthcare. Her recent publications emphasize safe reinforcement learning , multirobot coordination , and stochastic trajectory planning , with applications to aircraft navigation and energy systems. She has received the European Union ERC Starting Grant, NASA High Potential Individual Award, and IEEE Transactions on Control of Network Systems Outstanding Paper Award. Scientific Awards: ERC Starting Grant (2016-2021) NASA High Potential Individual Award (2010) NASA Excellence in Publication Award IEEE Outstanding Paper Award (2022) PhD Students: Jordan Philip Christopher Maddux Anna Maria Ni Tingting Ren Kai Salizzoni Giulio Schlaginhaufen Andreas Vaishampayan Saurabh Dilip Vallat Gabriel Rémi Former EPFL student: Guo Baiwei

Gabriel Vallatمشاهده پروفایل

پژوهشگر

• Control Systems
• Multiagent Optimization
• Robotics
• +۳ مورد دیگر

Gabriel Vallat is a Researcher at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Engineering and the SYCAMORE lab (Systems Control and Multiagent Optimization Research). He is also associated with the Creative Computation Laboratory (CRCL) under EPFL's School of Architecture, Civil and Environmental Engineering (ENAC). As a doctoral student in robotics and intelligent systems, his work bridges computational methods with control theory. Institution: EPFL Labs: SYCAMORE , CRCL Location: ME C1 399 (School of Engineering) and GC G1 557 (ENAC) Email: gabriel.vallat@epfl.ch Research focuses on control systems, multiagent optimization, and robotics, with applications in computational engineering and intelligent systems.

Giulio Salizzoniمشاهده پروفایل

پژوهشگر

• Multiagent Learning
• Optimization
• Control Engineering
• +۱ مورد دیگر

Giulio Salizzoni is a Doctoral Assistant at the SYCAMORE lab within the School of Engineering at École Polytechnique Fédérale de Lausanne (EPFL). His research focuses on multiagent learning and optimization, building on a strong foundation in electronic and automation engineering. Education: Bachelor's Degree in Electronic Engineering, Politecnico di Milano Master's Degree in Automation and Control Engineering, Politecnico di Milano Participant in Alta Scuola Politecnica (multidisciplinary honors program) Giulio's work explores the application of the scenario approach to shape stationary state distributions in linear systems, with current emphasis on collaborative learning frameworks for multiagent systems. He contributes to both research and academic activities at EPFL. Roles: Doctoral Assistant, EPFL STI IGM SYCAMORE Doctoral Student in Electrical Engineering, EPFL

Philip Jordanمشاهده پروفایل

پژوهشگر

• Reinforcement Learning
• Multi-agent Systems
• Optimization
• +۲ مورد دیگر

Philip Jordan is a Doctoral Assistant at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Engineering, Institute of Mechanical Engineering (IGM), working within the SYCAMORE laboratory. His research focuses on multi-agent systems and optimization problems. He received both his Bachelor's and Master's degrees in Computer Science from ETH Zurich, with a semester abroad at Princeton University during his undergraduate studies. His Master's thesis investigated independent learning in Markov potential games. His primary research interests include multi-agent reinforcement learning, optimization algorithms, game theory, and their applications in distributed systems. His work often addresses challenges in constrained environments and decentralized decision-making processes where multiple agents interact with shared constraints. Philip has published in top-tier conferences including AISTATS and AAMAS in 2024, with research focusing on independent learning algorithms for constrained Markov potential games and decentralized federated policy gradient methods with Byzantine fault tolerance. His publications demonstrate strong theoretical foundations coupled with practical implementations. He currently serves as a Doctoral Assistant in the SYCAMORE laboratory at EPFL, where he conducts research in systems control and multiagent optimization. His work combines theoretical analysis with practical implementation, as evidenced by the publicly available code repository for his AISTATS 2024 publication. Prior to his doctoral studies, Philip co-founded a start-up in the field of smart contracts, demonstrating practical application of his theoretical knowledge in blockchain technology.

Saurabh Dilip Vaishampayanمشاهده پروفایل

پژوهشگر

• Systems Control
• Multiagent Optimization
• Control Systems
• +۴ مورد دیگر

Saurabh Dilip Vaishampayan is a Doctoral Assistant at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Engineering and the Department of Mechanical Engineering. He works at the SYCAMORE research laboratory, focusing on systems control and multiagent optimization research. Additionally, he is a doctoral student in the Doctoral Program in Electrical Engineering at EPFL. Education: Doctoral Program in Electrical Engineering, École Polytechnique Fédérale de Lausanne (EPFL) Research Interests: Systems Control Multiagent Optimization Control Systems Optimization Electrical Engineering Robotics Artificial Intelligence Contact: Email: saurabh.vaishampayan@epfl.ch Phone: +41 21 695 81 48 Office: ME C1 409