جست‌وجوی استاد و پژوهشگر

Zebang Shenمشاهده پروفایل

مدرس

• Optimization
• Machine Learning
• Data Science
• +۳ مورد دیگر

Dr. Zebang Shen serves as a Lecturer in the Department of Computer Science at ETH Zurich, affiliated with the Institute for Machine Learning (Institut für Maschinelles Lernen). His research activities are centered at Andreasstrasse 5, 8092 Zürich, Switzerland, with teaching responsibilities confirmed for the Autumn Semester 2025. His primary research domains include Optimization, Machine Learning, and Data Science, with specialized focus on Federated Learning, Stochastic Optimization, and Reinforcement Learning. Shen develops algorithmic solutions for projection-free optimization, minimax problems, and diffusion model applications, emphasizing theoretical guarantees alongside practical implementations in distributed learning environments. Analysis of his 2021-2025 publications reveals consistent innovation in optimization frameworks for machine learning, particularly in federated settings where privacy-utility tradeoffs and straggler resilience are addressed. His work bridges mathematical rigor (e.g., Poincaré inequalities, McKean-Vlasov equations) with scalable algorithms for real-world data science challenges. No scientific awards were documented in the available sources. While the sources confirm his faculty role and publication record, specific details regarding student advising, grant funding, or laboratory leadership were not provided. His current teaching activities indicate ongoing academic engagement at ETH Zurich. Shen operates within ETH Zurich's Institute for Machine Learning, contributing to the Department of Computer Science's research ecosystem focused on advancing machine learning theory and applications.

Lénaïc Chizatمشاهده پروفایل

استادیار

• Optimal Transport
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Lénaïc Chizat is a Tenure Track Assistant Professor at the Swiss Federal Institute of Technology in Lausanne (EPFL) within the School of Basic Sciences and Institute of Mathematics. He chairs the Dynamics of Learning Algorithms (DOLA) laboratory and teaches advanced courses in machine learning and computational optimal transport, focusing on mathematical analysis of neural networks and measure transportation theory. His research centers on optimal transport theory and its applications to deep learning, with emphasis on Wasserstein geometry, entropic regularization, and gradient flow dynamics. He investigates implicit regularization in neural networks, convergence properties of learning algorithms, and the infinite-width limits of deep architectures. His work bridges theoretical mathematics with practical machine learning challenges, particularly in computational aspects of modern supervised learning. Analysis of his 15 most recent publications (2023-2025) reveals a strong thematic focus on entropic optimal transport, where he has made fundamental contributions to Sinkhorn algorithm convergence in continuous settings and Wasserstein barycenter computation. His research consistently explores the mathematical foundations of deep learning, especially training dynamics, min-max optimization, and the role of initialization in neural network scaling. Chizat currently advises PhD student Wang Guillaume Yitian and leads the DOLA laboratory, which develops theoretical frameworks for understanding learning algorithm dynamics through the lens of optimal transport and measure-valued optimization.

Charbel Toumiehمشاهده پروفایل

پژوهشگر ارشد

• Autonomous Systems
• Robotics
• Aerial Robotics
• +۳ مورد دیگر

Charbel Toumieh is a Research Fellow at the École Polytechnique Fédérale de Lausanne (EPFL), based in the Intelligent Systems Laboratory (LIS) under the School of Engineering (STI). His research focuses on advanced robotics, particularly in aerial systems, motion planning, and autonomous systems. He holds a postdoctoral position and contributes to projects involving multi-agent coordination, high-speed navigation, and energy-efficient drone designs. Key research areas include teleoperation of aerial swarms, adaptive morphing for avian-inspired drones, and decentralized multi-agent planning. His work addresses challenges in cluttered environments, dynamic obstacle avoidance, and real-time trajectory optimization. The LIS lab, part of the Institute of Microengineering (IGM), emphasizes innovative solutions in intelligent systems and robotics. Recent publications highlight advancements in motion planning algorithms, safe corridor generation using voxel grids, and GPU-accelerated exploration techniques. His research bridges theoretical control systems with practical applications in autonomous robotics, aiming to enhance efficiency and resilience in robotic systems.

Scott Keatingمشاهده پروفایل

مدرس

• Seismic Inversion Methodology
• Uncertainty Quantification
• Numerical Optimization
• +۴ مورد دیگر

Dr. Scott Keating is a Lecturer at ETH Zurich’s Department of Earth and Planetary Sciences (D-EAPS), affiliated with the Institute of Geophysics. His research focuses on seismic inverse problem methodologies, particularly uncertainty quantification, numerical optimization, and the development of automated workflows for regional inversion updates. Current projects include full-waveform inversion of ambient noise measurements and CO2 storage monitoring using advanced sensor technologies like distributed acoustic sensing (DAS) and accelerometers. His work integrates cutting-edge computational methods, such as probabilistic inversion frameworks and adjoint-based optimization, to address challenges in subsurface imaging and parameter estimation. Applications span environmental seismology, carbon sequestration, and reservoir characterization, with a strong emphasis on practical, cost-effective solutions for real-world problems such as CO2 storage validation. Publications highlight contributions to elastic full-waveform inversion (FWI), including multiparameter analysis using combined geophone and DAS data, and innovative techniques like targeted nullspace shuttling to enhance inversion reliability. His research also addresses data sparsity and modeling uncertainties, advancing methodologies for robust subsurface monitoring and decision-making.

Han Wangمشاهده پروفایل

استاد

• Control Systems
• Optimization
• Machine Learning
• +۳ مورد دیگر

Dr. Han Wang holds the Professorship for Complex Systems Control at ETH Zürich, Switzerland. He received his B.S. in Cyber Security from Shanghai Jiao Tong University (2020) and a DPhil (PhD) in Control from the University of Oxford (2024). His research focuses on control systems, optimization, and machine learning, with emphasis on safe and data-driven control, convex optimization (e.g., sum-of-squares programming), and applications of machine learning in control systems. Dr. Wang’s work bridges theoretical advancements and practical applications, such as distributed optimization and scenario-based methods. His research interests span magnonic systems, spintronics, and advanced materials, with notable contributions to magnon polaritons, spin wave dynamics, and antiferromagnetic magnonics. He has published extensively in top-tier journals, exploring topics like chiral magnonic edge states, coherent magnon transistors, and orbital pumping in magnetic materials. His findings have implications for next-generation spintronic devices and quantum technologies. Dr. Wang is the recipient of the Best Presentation Award at the UKACC 2023 conference, recognizing his innovative contributions to control systems and magnonics research. His work demonstrates a strong interdisciplinary approach, combining physics, engineering, and computational methods to address complex systems challenges. While no formal advising records are listed, his research group likely focuses on experimental and theoretical investigations into magnetic materials and control theory applications. His affiliation with ETH Zürich’s Department of Control positions him at the forefront of cutting-edge research in complex systems engineering.

Martin Jaggiمشاهده پروفایل

دانشیار

• Machine Learning
• Optimization
• Artificial Intelligence
• +۳ مورد دیگر

Martin Jaggi is an Associate Professor at EPFL, leading the Machine Learning and Optimization Laboratory (MLO). He holds academic positions within the School of Computer and Communication Sciences (IC), including roles in the SIN, SSC, and EDIC departments. His research focuses on machine learning optimization, federated learning, and large language models. He earned his PhD in Machine Learning and Optimization from ETH Zurich (2011) and a MSc in Mathematics from the same institution. Education: PhD in Machine Learning and Optimization, ETH Zurich (2011) MSc in Mathematics, ETH Zurich Research Interests: Dr. Jaggi's work integrates optimization theory with machine learning applications, emphasizing scalable algorithms and ethical AI. He explores topics like distributed learning systems, privacy-preserving techniques (e.g., federated learning), and model interpretability. His lab develops foundational methods for large language models (LLMs) and their ethical deployment. Teaching & Courses: Optimization for Machine Learning Topics in Machine Learning Systems Advising & Grants: He currently advises 9 PhD students and has supervised 11 past students. His work is supported by grants focusing on federated learning, optimization algorithms, and medical AI applications like Meditron-70b. Collaborations include projects on respiratory disease detection and mental health modeling. Labs & Teams: Academic Director of RCP-GE and oversees MLO lab, which develops open-source tools like FLamby for federated learning benchmarks and Meditron for clinical AI. The lab also advances techniques in distributed optimization and low-precision training.

• Combinatorial Optimization
• Robust Optimization
• Polynomial Optimization
• +۳ مورد دیگر

Prof. Dr. Adam Andrzej Kurpisz is a Tenure Track Professor at the Bern University of Applied Sciences (BFH) and a senior researcher at the Institute for Operations Research (IFOR) at ETH Zürich. He leads the Ambizione Junior Research Group under Prof. Rico Zenklusen. His research bridges combinatorial optimization, robust optimization, and semi-algebraic proof systems, with applications in polynomial optimization and approximation algorithms. Education: PhD from Wrocław University of Science and Technology (supervised by Prof. Paweł Zieliński), postdoctoral work at IDSIA and Max-Planck-Institut. Grants: Secured over CHF 600,000 via Swiss National Science Foundation and other grants, including the AMBIZIONE project on linear/semidefinite relaxations. Research Interests: Focuses on semi-algebraic proof systems, approximation algorithms, robust optimization, and polynomial optimization. Applies techniques from algebraic geometry, Fourier analysis, and linear algebra to analyze optimization hierarchies. Teaching: Lectures on convex optimization, polynomial optimization, and discrete mathematics at ETH Zürich and BFH. Supervised courses in algorithms, programming, and mathematical analysis. Industry Collaboration: Co-founder of startups Silencions and Deeptale, advancing their ventures to global finals of MassChallenge Switzerland with over €5M in EU grants secured.

Andrea Carronمشاهده پروفایل

مدرس ارشد

• Model Predictive Control
• Learning-based Control
• Coverage Control
• +۷ مورد دیگر

Dr. Andrea Carron is a Senior Lecturer at ETH Zürich, affiliated with the Intelligent Control Systems group under Professor M. Zeilinger at the Department of Mechanical and Process Engineering. He holds a PhD in Information Technology from the University of Padova (2016) and was a Postdoc at ETH Zurich from 2016 to 2020. Education: B.S. and M.Sc. in Control Engineering (University of Padova, 2010 and 2012) Professional Roles: Senior Lecturer (ETH Zurich, 2022–present), Postdoc Fellow (ETH Zurich, 2016–2020) Research Interests: Andrea Carron's work focuses on Model Predictive Control (MPC) and Learning-based Control with safety guarantees. His research addresses challenges in Distributed Safe Learning , Coverage Control , and Autonomous Racing , utilizing Gaussian Processes and Stochastic Control frameworks. He has developed safety filters for racing vehicles, scalable MPC for mobility-on-demand systems, and Kalman-filter-enhanced GP regression techniques. Article Trends: Recent publications emphasize Autonomous Racing (ForzaETH Race Stack), Safe Learning for distributed systems, Gaussian Process applications in control, and Robust MPC under uncertainty. His work bridges machine learning and classical control theory, with applications in robotics and real-time systems. Teaching Activities: He has taught courses such as Signals and Systems and Advanced Model Predictive Control at ETH Zurich and Ashesi University since 2017. Course content includes discrete-time signal processing, system identification, and control algorithms.

Bingcong Liمشاهده پروفایل

پژوهشگر

• Machine Learning
• Optimization
• Signal Processing
• +۳ مورد دیگر

Bingcong Li is a postdoctoral researcher at ETH Zurich collaborating with Prof. Niao He and the ODI group. Previously, they completed doctoral studies at the University of Minnesota under Prof. Georgios B. Giannakis, followed by industry experience focused on large language models (LLMs). Education includes a PhD from the University of Minnesota under Prof. Georgios B. Giannakis. Research centers on making computation efficient, accessible, and affordable across heterogeneous resources—from GPU clusters to consumer hardware—through interdisciplinary approaches combining deep learning, optimization, and signal processing. Key research areas address foundational computing architectures, large-scale system sustainability, and personalized AI access. Their work develops theoretically grounded methods for explainable systems, with recent focus on LLM fine-tuning efficiency. Publication trends show consistent contributions to top conferences (NeurIPS, ICML, ICLR) with emphasis on optimization techniques for resource-constrained LLM deployment. Their advising and grant activities aren't explicitly detailed, though they actively participate in academic service through conference talks (EUROPT 2025, ICASSP 2025) and co-organizing events like the Efficient LLMs Fine-tuning Track at AI+X Summit. Lab affiliation centers on ETH Zurich's ODI group under Prof. Niao He, focusing on optimization-driven AI solutions.

Anastasios Tsiamisمشاهده پروفایل

مدرس

• Statistical Learning and Control
• Data-Driven Control
• Online Learning
• +۳ مورد دیگر

Dr. Anastasios Tsiamis is a Lecturer at the Department of Information Technology and Electrical Engineering at ETH Zürich, working in the Automatic Control Laboratory (Professur Control and Computation). His research focuses on the intersection of control theory and machine learning, specifically investigating how system theoretic properties affect the statistical difficulty of learning in system identification, online estimation, and control. Dr. Tsiamis received his Diploma (MEng, five-year degree) in Electrical and Computer Engineering from the National Technical University of Athens (NTUA). He completed his Ph.D. in Electrical and Systems Engineering at the University of Pennsylvania under Professor George Pappas, following graduate research with Professor Petros Maragos and undergraduate work with Professor Kostas J. Kyriakopoulos at NTUA. His primary research areas include Statistical Learning and Control, Data-Driven Control, Online Learning, Risk-Aware Control, and Security and Privacy in Networked Control Systems. Dr. Tsiamis has made significant contributions to understanding the fundamental statistical limits of learning in control systems, particularly focusing on sample complexity. His work on risk-aware optimization develops algorithms that safeguard against catastrophic events while maintaining good average performance, and his security research addresses eavesdropping attacks in remote estimation and motion planning. Analysis of Dr. Tsiamis's recent publications reveals a strong focus on data-driven approaches to control theory with emphasis on distributionally robust methods, risk-aware optimization, and finite sample guarantees. His work bridges theoretical foundations with practical applications across system identification, online learning, and adaptive control, providing rigorous non-asymptotic guarantees for learning-based control algorithms. Dr. Tsiamis has received several notable research recognitions: Best student paper award at IEEE 61th Conference on Decision and Control (2022) Spotlight Presentation at 41st International Conference on Machine Learning (2024) Finalist for best student paper award at American Control Conference (2019) Finalist for young author prize at IFAC World Congress (2017) Oral presentation at 2nd L4DC Conference (2020) Dr. Tsiamis teaches Linear System Theory (227-0225-00L) at ETH Zürich and collaborates extensively with Professor John Lygeros, Professor Manfred Morari, and researchers from the University of Pennsylvania. His publication record demonstrates strong collaborative research across multiple institutions while advancing theoretical foundations of learning-based control. As an active member of the Automatic Control Laboratory at ETH Zürich, Dr. Tsiamis contributes to advancing control systems science through rigorous mathematical analysis and innovative algorithmic development, with applications spanning robotics, energy systems, and networked control.

Andreas Schlaginhaufenمشاهده پروفایل

پژوهشگر

• Reinforcement Learning
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Andreas Schlaginhaufen is a Researcher at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the SYCAMORE Lab under the Institute of Microengineering (IGM) within the School of Engineering (STI). His research focuses on reinforcement learning, safe AI systems, and convex analysis, with a particular emphasis on aligning AI models with human preferences and expert demonstrations. Education: He holds a Bachelor's and Master’s in Electrical Engineering and Information Technology from ETH Zurich, with an exchange semester at the University of Toronto, Canada. He has interned with EWZ Zurich's power grid team and ETH Zurich’s Learning & Adaptive Systems group. Bachelor's in Electrical Engineering (ETH Zurich) Master’s in Electrical Engineering and Information Technology (ETH Zurich) Exchange: University of Toronto, Canada Research Interests: His work spans reinforcement learning, safe reinforcement learning, convex analysis, and learning in games. He explores theoretical and practical aspects of reward shaping, transferability of learned models, and ensuring policy safety in dynamic environments. Awards: He has been awarded the Swiss Data Science Center PhD Fellowship, providing full funding and mentorship during his doctoral studies. Advising and Grants: While currently focused on his doctoral research, his fellowship supports his exploration of inverse reinforcement learning and its applications. He contributes to the SYCAMORE Lab, which investigates multi-agent optimization and systems control. Labs/Teams: He is part of the SYCAMORE Lab at EPFL, which focuses on systems control and multi-agent optimization, advancing theoretical and applied research in AI-driven systems.

• Power Systems
• Renewable Energy
• Optimization
• +۴ مورد دیگر

Prof. Hug is a prominent academic specializing in power systems and energy optimization. As a professor, they advise doctoral students on topics including smart grid design, distributed energy resource management, and renewable integration. Their research emphasizes flexibility quantification, optimal power flow, and data-driven modeling frameworks like DALINE. Notable contributions include scalable aggregation methods for flexible loads, voltage-aware frequency control strategies, and analysis of climate impacts on hydropower systems. Prof. Hug's work bridges theoretical advancements with practical applications in grid operations and market mechanisms. Doctoral students: 16+ supervised since 2015 Postdoctoral researchers: 12+ mentored Key areas: Smart grids, DER optimization, machine learning in energy systems Recent publications (2023-2025) address EV charging impacts, reinforcement learning for grid control, and dual challenges of low-inertia systems. Their work is published in top-tier venues and often collaborates with industry stakeholders.

• Combinatorial Optimization
• Operations Research
• Algorithms
• +۵ مورد دیگر

Adam Kurpisz is a Tenure Track Professor at Bern Business School (BFH) and a senior researcher at the Institute for Operations Research (IFOR) at ETH Zürich. He holds a PhD from Wrocław University of Science and Technology and has held postdoctoral and visiting researcher positions at IDSIA and the Max-Planck-Institut für Informatik. His research bridges theoretical computer science and applied optimization, focusing on combinatorial optimization, semi-algebraic proof systems, and robust optimization. He has secured over CHF 600,000 in grants, including leadership in the Ambizione Junior Research Group. His teaching spans convex optimization, polynomial optimization, and discrete mathematics at ETH Zürich and BFH. He co-founded startups Silencions and Deeptale, advancing to international competitions and securing EU grants. Research interests include leveraging algebraic geometry and Fourier analysis to develop algorithms for optimization problems. He has published extensively on sum-of-squares hierarchies, approximation algorithms, and scheduling under uncertainty. His work has been presented at venues like ISSAC, ICALP, and SIAM Conferences.

Nicolas Boumalمشاهده پروفایل

استادیار

• Optimization
• Non-convex Optimization
• Riemannian Manifolds
• +۵ مورد دیگر

Nicolas Boumal is an Assistant Professor in Mathematics at EPFL, Switzerland, holding dual affiliations in the Continuous Optimization Chair (OPTIM) within the Institute of Mathematics (MATH) and the SMA Education Group. He leads research in optimization on Riemannian manifolds, non-convex optimization, and statistical estimation. His work bridges geometry, numerical analysis, and applications in cryo-electron microscopy and synchronization problems. Education: PhD in Mathematical Engineering from UCLouvain (2014), postdoctoral research at Inria Paris and Princeton University. Prior to EPFL, he was an Instructor and Assistant Professor at Princeton’s Mathematics Department. Research Interests : Non-convex optimization landscapes, optimization on manifolds, low-rank matrix optimization, synchronization, phase retrieval, and computational methods in cryo-EM. His ERC Starting Grant (GEOSYM, 2022–2027) focuses on geometric and symmetric optimization techniques. Grants & Awards : ERC Starting Grant (2021), SIAM Optimization Prize (2018 for student co-author), and several conference best paper awards. Teaching : Courses include Continuous Optimization (MATH-329), Algebra Linéaire (MATH-111), and graduate-level Optimization on Manifolds (MATH-512). He authored the textbook An Introduction to Optimization on Smooth Manifolds (Cambridge University Press, 2023). Labs & Teams : Heads the OPTIM lab at EPFL, collaborating with global researchers in optimization and applications. His group develops the Manopt toolbox for manifold optimization.

• Control Systems
• Data-Driven Control
• Optimization Algorithms
• +۳ مورد دیگر

Jaap Eising is a Lecturer at the Department of Information Technology and Electrical Engineering at ETH Zürich. His research focuses on control systems, data-driven control methodologies, optimization algorithms, and system identification. He explores topics such as affine time-invariant systems, robust control of linear systems, and adaptive control strategies for uncertain systems. His work emphasizes theoretical foundations and practical applications in control theory, leveraging data-driven approaches to enhance system analysis and stabilization. Recent publications highlight advancements in data-driven techniques for mode detection, parameter variation decoupling, and cautious optimization under noisy data. His research also addresses challenges in online adaptive control and stabilization of switched systems. Key themes include informativity frameworks, quadratic matrix inequalities, and the integration of statistical modeling with control theory. No scientific awards or grants are explicitly mentioned in the provided texts. His academic contributions are centered on advancing control systems through rigorous mathematical analysis and innovative algorithmic designs.