جست‌وجوی استاد و پژوهشگر

Robin Bruneauمشاهده پروفایل

پژوهشگر

• 3D Reconstruction
• Computer Vision
• Photometric Stereo
• +۳ مورد دیگر

Robin Bruneau is a Postdoctoral Researcher at the University of Zurich (UZH) in the Department of Quantitative Medicine. His research focuses on advancing multi-view 3D reconstruction techniques, particularly integrating normal and reflectance cues. He holds a PhD co-supervised between DIKU (University of Copenhagen) and IRIT (University of Toulouse), completed in 2024, and a Master's degree in Computer Science from Enseeiht (2017–2020). His work emphasizes high-fidelity surface reconstruction under challenging conditions, such as complex materials and refractive interfaces. Key applications include archaeological object digitization and transparent material modeling. Bruneau's research has been supported by grants from the Department of Quantitative Biomedicine at UZH, the French National Research Agency (ANR) via the ALICIA-Vision project, and DIKU's Copenhagen Data+ initiative. Collaborations include partnerships with institutions like the Musée Saint-Raymond (Toulouse) for archaeological data acquisition and OR-X (Switzerland) for translational surgery applications. His publications span top venues in computer vision, including CVPR and SPIE, with a focus on benchmarking datasets like DiLiGenT-MV and Skoltech3D. Notable contributions include the RNb-NeuS framework, which achieves state-of-the-art performance in multi-view photometric stereo.

Daniel Kuhnمشاهده پروفایل

استاد

• Data-driven Optimization
• Stochastic Programming
• Robust Optimization
• +۳ مورد دیگر

Daniel Kuhn is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL), where he holds the Chair of Risk Analytics and Optimization in the College of Management of Technology. His research focuses on developing computational methods for data-driven decision-making under uncertainty, with applications in engineered systems, machine learning, and finance. Previously, he held positions at Imperial College London and Stanford University. Education includes: PhD in Economics, University of St. Gallen MSc in Theoretical Physics, ETH Zurich Research interests span data-driven optimization , stochastic programming , and robust decision-making frameworks . His work develops computationally tractable methods for uncertainty quantification in complex systems, bridging operations research with statistical learning. Current investigations focus on distributionally robust optimization using Wasserstein metrics and applications in energy markets and fair machine learning. Publication analysis reveals three primary trends: 1) Fundamental advances in distributionally robust optimization theory, 2) Machine learning applications with uncertainty guarantees, and 3) Energy system optimization under regulatory constraints. His methodological work consistently emphasizes computational tractability and practical applicability. As lab director of the Risk Analytics and Optimization group, he leads research on: Stochastic control systems Data-driven decision frameworks Robust machine learning Current PhD students include researchers working on federated learning fairness, optimal power flow, and reinforcement learning theory. Past graduates have made significant contributions to Wasserstein distributionally robust optimization and vehicle-to-grid frequency regulation.

Yucheng Yangمشاهده پروفایل

استادیار

• Macroeconomics
• Finance
• Machine Learning
• +۳ مورد دیگر

Yucheng Yang is an Assistant Professor of Finance at the University of Zurich and a member of the Swiss Finance Institute. He earned his PhD from Princeton University in 2023, with prior degrees from Peking University and the University of Wisconsin-Madison. His research focuses on macroeconomics, finance, and machine learning, emphasizing topics like heterogeneous agent models, monetary policy, and big data integration. He has held visiting positions at NYU and Yale and has received awards such as the CICF Yihong Xia Best Paper Award and the CES Gregory Chow Best Paper Award. His work bridges theoretical economics with computational methods, addressing challenges in asset pricing, business cycle analysis, and policy design. Recent research includes applications of deep learning to search and matching models and the development of global solution methods for complex economic systems. Yang is actively involved in teaching and mentoring, currently hiring research assistants in machine learning and macroeconomic modeling. Awards include recognition for his contributions to macroeconomic analysis and financial modeling. His research has implications for central bank policies and financial market dynamics, with a focus on inclusive economic frameworks.

Oleg Prokopyevمشاهده پروفایل

استاد

• Operations Research
• Optimization
• Network Analysis
• +۳ مورد دیگر

Oleg Prokopyev is a Professor of Quantitative Business Administration at the University of Zurich, affiliated with the Department of Business Administration. His office is located at PLR F 105, and he can be reached at +41 44 634 3444. His research focuses on optimization, network analysis, and stochastic programming, with applications in supply chain management, environmental studies, and healthcare systems. He holds a leadership role in the Chair of Quantitative Business Administration, contributing to interdisciplinary research in operations research and mathematical programming. Prokopyev’s work spans theoretical advancements in fractional programming, bilevel optimization, and graph algorithms, alongside practical applications such as wildfire fuel management, supply chain sustainability, and maintenance scheduling. His recent publications address dynamic assortment planning, interdiction models under uncertainty, and centrality measures in networks. His research emphasizes computational methods for solving complex optimization problems, including exact and heuristic approaches for clique relaxations, betweenness centrality, and robust decision-making frameworks. Prokopyev has collaborated on projects involving environmental impact assessments of beef supply chains and optimal design of healthcare systems like surgical patient routing.

Sebastian Engelkeمشاهده پروفایل

دانشیار

• Extreme Value Theory
• Spatial Statistics
• Graphical Models
• +۵ مورد دیگر

Sebastian Engelke is an Associate Professor at the Research Center for Statistics at the University of Geneva. His research focuses on extreme value theory, spatial statistics, graphical models, and machine learning applications in climate science and risk assessment. He holds an SNSF Eccellenza Grant and has contributed to foundational work on extremal graphical models and causal inference in heavy-tailed systems. Affiliations: University of Geneva, Research Center for Statistics Grants: SNSF Eccellenza Grant (2020–2025), Ambizione Fellowship (2015–2018) Education: PhD in Mathematics (2013, Georg-August-University of Göttingen), studies at UC Berkeley and University of Göttingen. Research Interests Engelke’s work bridges extreme value theory with modern machine learning, addressing challenges in climate extremes, risk quantification, and graphical model structures. Key areas include: Statistical modeling of multivariate extremes Causal discovery in heavy-tailed distributions Applications in environmental science and climate informatics Key Contributions Recent work includes advancements in extremal graphical models (via matrix completions), neural network approaches for extreme quantile regression, and validation methods for deep learning weather models. His research is published in top journals like Annals of Statistics and Journal of the American Statistical Association . Awards & Recognition Lambert Award (2021) Gumbel Lecture (2023) Fields Research Fellowship (2019) Teaching & Supervision Engelke teaches courses on machine learning, probability, and advanced topics in statistics at the University of Geneva. He supervises PhD students and postdocs in machine learning and extreme value theory. His students include Edoardo Vignotto (extremal random forests) and Nicola Gnecco (causal discovery in heavy-tailed models).

Alexander Hägeleمشاهده پروفایل

پژوهشگر

• Machine Learning
• Optimization
• Artificial Intelligence
• +۱ مورد دیگر

Alexander Hägele is a researcher at the Laboratoire d'apprentissage automatique et d'optimisation (Machine Learning and Optimization Laboratory, MLO) within the College of Computer and Communication Sciences at École polytechnique fédérale de Lausanne (EPFL). His work focuses on theoretical and practical aspects of machine learning optimization.

Alexandre Caboussatمشاهده پروفایل

مدرس مشاور

• Numerical Analysis
• Scientific Computing
• Numerical Optimization
• +۵ مورد دیگر

Alexandre Caboussat is a Consultant Lecturer at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Basic Sciences in the Mathematics Department. He contributes to the Euler Programme and the SMA - Teaching unit, focusing on advanced mathematical analysis and computational methods. Research Interests : Numerical Analysis, Scientific Computing, Numerical Optimization, Variational Problems, Computational Fluid Dynamics, Free Surface Flows, Air Quality Modeling, and Computational Chemistry. His recent work emphasizes numerical methods for partial differential equations, particularly the Monge-Ampère equation, adaptive finite element techniques, and physics-informed neural networks. Applications span from optimal transport to laser surface melting and environmental flows. Students : Girardin Maude, Peruso Anna, Diserens Léo Aurélio, Gourzoulidis Dimitrios, Landry Chantal, Mrad Arwa. Contact : alexandre.caboussat@epfl.ch

Julien Mairalمشاهده پروفایل

پژوهشگر

• Machine Learning
• Optimization
• Computer Vision
• +۲ مورد دیگر

Julien Mairal is a Senior Research Scientist at Inria Grenoble within the Thoth research team, focusing on machine learning, optimization, and computer vision. His research spans theoretical foundations and practical applications across multiple domains including signal processing, bioinformatics, and neuroimaging. His primary research interests include machine learning , particularly optimization algorithms for large-scale problems, computer vision with applications in image restoration and super-resolution, and kernel methods for structured data. His work demonstrates strong connections between theoretical optimization and practical implementations, as evidenced by his Cyanure toolbox for empirical risk minimization. Analysis of his recent publications reveals a consistent focus on optimization techniques for machine learning, with increasing emphasis on large-scale applications, self-supervised learning, and connections between kernel methods and deep learning architectures. His work shows strong interdisciplinary applications spanning computer vision, neuroscience, and remote sensing. ERC Consolidator Grant (2023) for the Apheleia project ICML Test of Time Award (2019) ERC Starting Grant (2016) for the SOLARIS project Mairal serves extensively in editorial capacities as area chair for major conferences including ICML, NeurIPS, CVPR, and ECCV. He is an associate editor for the Journal of Machine Learning Research (JMLR) and previously served for IEEE PAMI and IJCV. His Cyanure toolbox has become a significant contribution to the field of large-scale optimization in machine learning. His research demonstrates strong connections between theoretical foundations and practical implementations across multiple application domains.

Anas Barakatمشاهده پروفایل

پژوهشگر ارشد

• Multi-Agent Learning
• Reinforcement Learning
• Stochastic Optimization
• +۵ مورد دیگر

Anas Barakat is a Research Fellow at the Singapore University of Technology and Design , where he develops theory and algorithms for learning in strategic, structured, and dynamic environments. His work integrates reinforcement learning, game theory, online learning, stochastic optimization, and dynamical systems to build robust multi-agent learning systems. PhD : Applied Mathematics and Computer Science, 2021, Institut Polytechnique de Paris (Télécom Paris) MSc : Data Science, 2018, Université Paris Saclay MSc : Applied Mathematics and Computer Science, 2018, Télécom Paris His research focuses on multi-agent learning in strategic environments, behaviorally aligned reinforcement learning (e.g., incorporating psychological biases), and adaptive optimization via dynamical systems analysis. He explores feedback loops, non-stationary objectives, and structured games like Markov potential games and zero-sum linear quadratic games. Recent publications (2025–2023) span multi-agent control , symmetric cone games , policy gradient frameworks , and Adam algorithm convergence . His work appears in venues like IEEE CDC , NeurIPS , ICML , and AISTATS , with preprints on arXiv. At ETH Zurich (2022–2024), he taught Optimization for Data Science and Foundations of Reinforcement Learning , and at Télécom Paris (2018–2021), he assisted courses in machine learning and optimization.

Bartolomeo Stellatoمشاهده پروفایل

استادیار

• Mathematical Optimization
• Machine Learning
• Optimal Control
• +۳ مورد دیگر

Bartolomeo Stellato is an Assistant Professor at Princeton University's Department of Operations Research and Financial Engineering. He is associated faculty in Electrical and Computer Engineering and Computer Science, and affiliated with Princeton's AI, Robotics, and Center for Statistics and Machine Learning initiatives. His research bridges mathematical optimization, machine learning, and optimal control for real-time decision-making in uncertain environments. PhD advisors: Georgina Hall, Brandon Amos Key collaborators: Bart Van Parys (MIT), Jaime Fernandez Fisac (UPenn), Adrien Taylor (INRIA) His work on Mean Robust Optimization combines robust and distributionally robust optimization through data clustering, achieving 3+ orders of magnitude speedups in portfolio optimization. He also develops learned optimizers (e.g., L2WS, GeNIOS) and OSQP , which won the Beale–Orchard-Hays Prize. Recent grants include NSF CAREER (2023) and ONR Young Investigator (2025) awards. Article Trends : His research spans optimization verification (3/7 of 2025 articles), data-driven methods (5/7 of 2024-2025 articles), control applications (4/15 articles), and equitable resource allocation (2/15 articles). Key subfields include algorithm verification, stochastic optimization, multi-agent systems, and hardware acceleration. Scientific Awards : ONR Young Investigator (2025), Howard B. Wentz Award (2024), NSF CAREER (2023), ISSNAF Strazzabosco (2022) Students : Mentored 12+ graduate students/postdocs including Irina Wang (Wallace Fellowship), Rajiv Sambharya (Best Paper Award at JMLR), and Vinit Ranjan (co-author Mathematical Programming paper) He leads the Princeton Optimization, Learning, and Control Workshop and collaborates with institutions like MIT, INRIA, and Los Alamos National Laboratory. His group develops open-source tools (OSQP, L2WS) with over 300,000 downloads.

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

Roland Schwan is a researcher at the Automatic Control Laboratory (LA) within the School of Engineering at the École Polytechnique Fédérale de Lausanne (EPFL) . His work focuses on control systems, optimization algorithms, and the integration of machine learning with physical models. He is actively involved in projects involving model predictive control (MPC), stability verification of neural network controllers, and real-time optimization-based control for robotics applications. His research interests span physics-informed machine learning , quadratic programming solvers , and embedded systems . Key contributions include the development of the PIQP solver for convex quadratic programming and stability analysis frameworks for neural network controllers. He has also explored applications in hovercraft dynamics identification and rocket trajectory control using advanced MPC techniques. Schwan collaborates closely with institutions like the Swiss National Science Foundation and has published extensively in venues such as IEEE Transactions on Automatic Control and IEEE Conference on Decision and Control . His work bridges theoretical control methodologies with practical embedded system implementations.

Mohammadsadegh Khorasaniمشاهده پروفایل

پژوهشگر

• Business Analytics
• Data Science
• Network Analysis
• +۳ مورد دیگر

Mohammadsadegh Khorasani is a Researcher and doctoral assistant at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the College of Management of Technology (CDM) and the School of Computer and Communication Sciences (IC). He is part of the Business Analytics (BAN) Chair and the Laboratoire de la dynamique de l'information et des réseaux 1 (INDY1). His research focuses on interdisciplinary areas combining business analytics, data science, and network analysis, with a particular emphasis on machine learning applications and information systems. His work bridges theoretical and applied domains, including optimization algorithms for policy gradients, causal structure learning in complex systems, and genomic studies related to cancer biology. He collaborates across departments at EPFL, contributing to both technical and managerial aspects of data-driven decision-making. Dr. Khorasani’s publications reflect his expertise in machine learning, statistical modeling, and bioinformatics. While no specific awards are listed, his contributions highlight ongoing research in algorithm development and interdisciplinary applications.

Verena Häberleمشاهده پروفایل

مدرس

• Power System Dynamics
• Automatic Control
• Power Electronics
• +۴ مورد دیگر

Verena Häberle is a PhD student at ETH Zurich's Automatic Control Laboratory (IfA) and a Lecturer at Ashesi University's ETH-Ashesi Master's Program in Mechatronic Engineering. She holds a Bachelor's (2018) and Master's (2020) in Electrical Engineering from ETH Zurich, specializing in power systems control and optimization. Her research focuses on power system dynamics, grid-forming control, dynamic virtual power plants (DVPPs), and renewable energy integration. She has conducted visiting research at UC Berkeley and Lawrence Berkeley National Laboratory. Her honors include the ETH Medal, SGA Award 2020, and Silver Medal of ETH Zurich for academic excellence. Teaching roles include co-lecturing Control Systems at ETH and lecturing mechatronics in Ghana. Research interests span power electronics, data-driven converter control, and grid-code design. Publications emphasize grid stability, converter control, and ancillary services. She actively contributes to experimental validation of control concepts via hardware-in-the-loop testing.

• Statistical Estimation
• High-Dimensional Data Analysis
• Convex Regression
• +۳ مورد دیگر

Dr. Gil Kur is a Lecturer in the Department of Mathematics at ETH Zürich. His research focuses on statistical estimation, high-dimensional data analysis, convex regression, machine learning theory, optimization, and probability theory. He has contributed to areas such as nonparametric estimation, convex body approximation, and differential privacy mechanisms. His work bridges theoretical foundations with applications in computational statistics and optimization. Key research themes include analyzing convergence rates of estimators, developing optimal algorithms for convex regression, and studying geometric properties of high-dimensional spaces. His recent articles explore topics like debiased LASSO methods, log-concave maximum likelihood estimation, and the performance of empirical risk minimization under various constraints. Kur’s publications demonstrate a strong focus on rigorous mathematical analysis, often combining tools from probability, functional analysis, and convex geometry. While no specific awards or grants are listed, his active publication record reflects sustained contributions to statistical theory and machine learning fundamentals.

• Mixed Integer Optimization
• Mixed Integer Linear Programming
• Mixed Integer Convex Optimization
• +۵ مورد دیگر

Robert Weismantel serves as Full Professor at ETH Zürich's Department of Mathematics and Deputy Head of the Institute for Operations Research. His research establishes foundational frameworks for Mixed Integer Optimization across linear, convex, and nonlinear domains, with significant theoretical contributions to algorithm design and computational complexity. His primary research domains include: Mixed Integer Linear Optimization using cutting plane methods based on lattice-free polyhedra Mixed Integer Convex Optimization combining polytope shrinking techniques with convex programming Mixed Integer Nonlinear Optimization focusing on convex relaxations for chemical engineering applications Polynomial Optimization in fixed dimensions with combinatorial substructure analysis Professor Weismantel's methodological innovations bridge discrete and continuous optimization, particularly through Mirror-Descent Methods and Lipschitz-continuous function minimization over integer points. His work demonstrates practical applications in chemical engineering through the SFB/TR 63 InPROMPT research collaboration. With an extensive supervision record spanning over two decades, he has guided 17 doctoral students to completion and mentored two successful habilitations. Current advisee Sabrina Bruckmeier continues this legacy of training optimization specialists. His research group maintains active projects including Mixed Integer Convex Minimization with Timm Oertel, Integer Polynomial Optimization with Kevin Zemmer, and Mixed-Integer Nonlinear Optimization applications with Martin Ballerstein and Dennis Michaels. The team operates within ETH's Institute for Operations Research, contributing to both theoretical advances and practical implementations of optimization algorithms.