Find Academic Supervisors

Wolfgang KrögerView profile

Professor

• Risk Analysis
• Vulnerability Assessment
• Critical Infrastructure Security
• +3 more

Prof. Wolfgang Kröger is a renowned academic and researcher in risk analysis and critical infrastructure security, affiliated with ETH Zurich as a former full professor and current Executive Director of the ETH Risk Center since 2011. He also serves as an external faculty member at Bridging Nations University. His expertise spans nuclear energy safety, vulnerability assessment, and risk governance frameworks. Education: Dr.-Ing. habil. (1986, BUGH Wuppertal), Dr.-Ing. (1974, RWTH Aachen), Diploma in Mechanical Engineering-Nuclear Technology (1972, RWTH Aachen). Roles: Former Director of the Laboratory of Safety Analysis (LSA) at ETH Zurich, Acting Director of the Institute of Energy Research (2007–2009), and Founding Rector of the International Risk Governance Council (IRGC). His research focuses on modeling complex engineered systems, critical infrastructure interdependencies, and sustainable energy systems. He has contributed to frameworks for vulnerability analysis and decision-making tools for technological risks. Recent work addresses smart grid security, cyber-physical system vulnerabilities, and post-Fukushima nuclear safety reassessments. Publications: Over 100 peer-reviewed articles and books, including Vulnerable Systems (Springer, 2011) and Risk and Vulnerability of Large-Scale Technical Systems (2012). His articles often explore electric power systems, SCADA vulnerabilities, and global risk governance. Awards: OECD/NEA News Prize (2001), Honorary Membership in SGK (2004), Distinguished Affiliated Professor at TU Munich (2011). Prof. Kröger chairs international committees such as the IRGC Advisory Board and contributes to policy initiatives like the EC Energy Roadmap 2050. His leadership roles include directing academic programs like the 'Risk and Safety' Certificate and advising on nuclear energy sustainability.

• Environmental Impact Assessment
• Risk Assessment
• Energy Systems Analysis
• +3 more

Stefan Hirschberg is a Lecturer at ETH Zurich and École Polytechnique Fédérale de Lausanne (EPFL), specializing in energy systems analysis and risk assessment. Since 2017, he serves as a Senior Advisor at the Paul Scherrer Institute (PSI), contributing to energy technology assessment, environmental impact evaluation, and risk-related research. His work includes managing projects for Swiss authorities and international organizations, focusing on sustainability and decision-support tools. Education: M.Sc. (1975) and Ph.D. (1981) in Engineering Physics and Reactor Physics from Chalmers University of Technology, Sweden. He held roles at ABB (1982–1990) as a Senior Engineer, Group Leader, and Section Manager, specializing in reliability and risk assessment methodologies. During 1990–1992, he served at the IAEA, leading Probabilistic Safety Assessment (PSA) initiatives globally. Research Interests: Environmental Impact Assessment, Comparative Risk Analysis, Sustainability, and Integrated Decision-Support Systems. He lectures on "Reliability and Risks of Complex Systems" at ETH Zurich and contributes to energy systems analysis courses at ETHZ and EPFL. International Contributions: Member of OECD/NEA’s Risk Assessment Working Group since 1990 and Editorial Board of the International Journal of Risk Assessment and Management. Engaged in global programs addressing energy systems’ environmental, economic, and safety implications.

Francesco D'AngeloView profile

Researcher

• Machine Learning
• Artificial Intelligence
• Deep Learning
• +3 more

Francesco D'Angelo is a Researcher and Doctoral Assistant at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Computer and Communication Sciences (IC) and the Institute of Computer Science and Communications Systems (IINFCOM). He is part of the Theory of Machine Learning Laboratory (TML) and enrolled in the Doctoral program in computer and communication sciences (EDIC) under the EPFL Doctoral School (EDOC). His research focuses on foundational aspects of machine learning, including sparse attention mechanisms, Bayesian neural networks, uncertainty estimation, and continual learning. He holds the position of Doctoral Assistant at the TML lab, where he contributes to advancing theoretical and practical applications of machine learning algorithms. Research Interests: Francesco's work bridges theoretical computer science with applied machine learning. His key areas include developing robust models for uncertainty quantification, improving generalization in overparameterized networks through regularization techniques like weight decay, and exploring the interplay between transformer architectures and causal structure induction. He also investigates methods to mitigate catastrophic forgetting in continual learning scenarios and applies Bayesian approaches to enhance out-of-distribution detection capabilities. Publications: His recent work explores topics such as sparse attention mechanisms in transformers, the necessity of weight decay in deep learning optimization, and Bayesian methods for uncertainty estimation. These contributions highlight his expertise in both foundational theory and practical algorithm design within the machine learning domain. Labs & Teams: As part of the TML lab, he collaborates on interdisciplinary projects at the intersection of machine learning theory and applications, contributing to advancements in neural network architectures and probabilistic modeling techniques.

Florian DörflerView profile

Professor

• Distributed Control Systems
• Optimization
• Cyber-Physical Systems
• +7 more

Florian Dörfler is a Full Professor at ETH Zurich's Department of Information Technology and Electrical Engineering and Deputy Head of the Automatic Control Laboratory. He holds a Ph.D. in Mechanical Engineering from the University of California, Santa Barbara (2013), and a Diplom in Engineering Cybernetics from the University of Stuttgart (2008). Prior to ETH, he was an Assistant Professor at UCLA (2013–2014). His research focuses on distributed control and optimization in complex systems, including smart grids, robotic coordination, and social networks. Key areas include power grid stability, online feedback optimization, and data-driven control. He has advised students who received or were finalists for prestigious awards at major conferences such as the European Control Conference and American Control Conference. Notable awards include the IFAC Manfred Thoma Medal (2020), European Control Award (2020), and IEEE Circuits and Systems Best Paper Award (2016). His work spans theoretical advancements and practical applications, with contributions to stability analysis, decentralized control, and energy systems. Led the Automatic Control Laboratory and contributes to ETH Zurich’s broader efforts in cyber-physical systems. Active in academic service, including editorial roles and conference organization. His lab develops cutting-edge solutions for grid resilience, distributed optimization, and networked systems.

Katrin BeyerView profile

Professor

• Earthquake Engineering
• Structural Dynamics
• Masonry Structures
• +5 more

Katrin Beyer is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) , leading the Earthquake Engineering and Structural Dynamics Laboratory (EESD) and serving as Dean of the School of Architecture, Civil and Environmental Engineering (ENAC) . She also holds roles as Director of the Structural Engineering Group (GIS-GE) and President of the ENAC School Council. Education: BSc in Civil Engineering (ETH Zurich, 2001) Master’s in Earthquake Engineering (IUSS Pavia, 2003) PhD in Earthquake Engineering (University of Pavia, 2007) Her research focuses on the seismic behavior of unreinforced masonry structures , particularly historical buildings. She combines large-scale experimental testing , numerical modeling , and digital twinning to advance structural assessment methodologies. Recent work explores machine learning for crack detection and upcycling demolition waste into sustainable masonry systems. Her publications reflect trends in: seismic risk mitigation , URM building fragility , image-based structural analysis , and innovative digital twin applications . Beyer has advised over 25 PhD students and contributed to Eurocode 8 revisions. She led the ETH Domain’s CHF 15 million Open Research Data program and co-founded the startup SwissInspect for structural assessment technologies.

Sylvain CalinonView profile

Lecturer

• Robotics
• Robot Learning
• Human-Robot Collaboration
• +4 more

Sylvain Calinon is a Senior Research Scientist at the Idiap Research Institute and a Lecturer at the Ecole Polytechnique Fédérale de Lausanne (EPFL). He specializes in robot learning, human-robot collaboration, and optimal control. He holds a PhD from EPFL (2007) and has led research teams at IIT (2009–2014) and Idiap (2014–present). His work focuses on enabling robots to learn skills from demonstrations and interactions, emphasizing geometric and model-based approaches. Affiliations: Idiap Research Institute (Senior Scientist), EPFL (Lecturer in Microengineering) Research Interests: Robot learning, manipulation planning, optimal control, human-robot interaction, and industrial applications. Calinon's research has been supported by grants from the European Commission, Swiss National Science Foundation, and others. He has received multiple awards, including the ABB Award and Best Paper Awards at ICRA, IROS, and RSS. He has advised over 20 PhD students and contributes to editorial roles in top journals like IEEE Transactions on Robotics. His current projects include the H2020 MEMMO (memory of motion) and COLLABORATE (human-robot co-production systems). He also leads initiatives in geriatronics and industrial cobotics, emphasizing real-world applications.

Nada AminView profile

Assistant Professor

• Programming Language Theory
• Metaprogramming
• Generative Programming
• +11 more

Nada Amin is an Assistant Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS). She holds a PhD from École Polytechnique Fédérale de Lausanne (EPFL) and degrees from MIT. Her research spans programming language theory, metaprogramming, semantics, and functional programming. Her educational background includes a Doctoral Program in Computer, Communication and Information Sciences from EPFL (2011–2016) and a Master of Engineering and Bachelor of Science from MIT. She has held research positions at TU Darmstadt, University of Cambridge, EPFL, Microsoft Research, and MIT CSAIL. Nada Amin's research focuses on foundational and practical aspects of programming languages. She investigates type systems, staged computation, generative programming, program synthesis, and the semantics of programming languages. Her work often bridges theory and implementation, with applications in verification, compiler construction, and language design. She is particularly known for her contributions to the DOT calculus, collapsing towers of interpreters, and relational programming with miniKanren. Her recent publications reveal a strong trend in advancing programming language foundations, especially in type systems and semantics (e.g., Persimmon, Dolorem, Extensible Metatheory). She integrates functional and logic programming paradigms, explores staged and generative techniques for efficiency and abstraction, and applies these to domains like probabilistic programming and biomedical reasoning. Recipient of the first Michigan Cambridge Research Initiative Grant (with Baris Kasikci, May 2018) 6.170 Letter of Commendation from Prof. Michael Ernst (2005) Peer Bonus at Google for enabling Gmail's CSS compiler for Android Peer Bonus at Google for benefiting numerous projects including Google Apps Nada Amin actively mentors students and collaborates widely in the programming languages community. She has served on numerous program committees (POPL, PLDI, ICFP, OOPSLA, etc.) and organized workshops. Her work is supported by academic and industrial collaborations, and she has contributed to open-source projects. She leads research involving interpreters, compilers, and program transformation systems, often developing tools and frameworks like LMS and Pink.

• Biostatistics
• Statistical Modeling
• Mathematical Statistics
• +3 more

Gellért Géza Perényi serves as a Doctoral Assistant at the Chair of Biostatistics within the Department of Mathematics, School of Basic Sciences at the Swiss Federal Institute of Technology Lausanne (EPFL). Concurrently, he is enrolled as a doctoral student in EPFL's Mathematics Doctoral Program (EDMA), maintaining active academic status through both research and study roles. His research centers on biostatistical methodology development for biological and medical applications, emphasizing mathematical rigor in statistical inference. Key focus areas include high-dimensional data analysis, probabilistic modeling of biological systems, and computational techniques for genomic datasets. This work bridges theoretical statistics with practical healthcare challenges, particularly in epidemiological study design and clinical trial analytics. Perényi operates within EPFL's Biostatistics research ecosystem, contributing to the Chair's mission of advancing statistical theory for life sciences. The group maintains strong collaborative ties with medical institutions and participates in interdisciplinary projects spanning public health surveillance, personalized medicine, and biomarker discovery initiatives across European research networks.

Niao HeView profile

Associate Professor

• Machine Learning
• Optimization
• Reinforcement Learning
• +4 more

Prof. Niao He is an Associate Professor in the Department of Computer Science at ETH Zürich. His research focuses on optimization theory, reinforcement learning, stochastic systems, and their applications in machine learning and multi-agent systems. He holds an academic position at one of the world's leading technical universities, contributing to both theoretical advancements and practical algorithmic solutions. His research interests span optimization theory (e.g., convex/non-convex optimization, stochastic optimization), reinforcement learning (policy gradient methods, multi-agent systems), and statistical learning (risk-averse methods, entropy regularization). He has also explored applications in data science, network revenue management, and deep reinforcement learning for complex systems. Recent work emphasizes algorithmic reproducibility, robustness under model uncertainty, and efficient methods for large-scale problems. Notable contributions include novel approaches to minimax optimization, mean-field games, and adaptive learning frameworks. His articles consistently address theoretical guarantees while maintaining practical relevance for real-world computational challenges.

Francesco CormanView profile

Associate Professor

• Analytics
• Optimization and control in transport systems
• Public transport
• +3 more

Francesco Corman is an Associate Professor at the Department of Civil, Environmental and Geomatic Engineering at ETH Zürich, where he also serves as the Head of the Institute for Transport Planning and Systems (IVT). His academic career spans from his doctoral studies at Delft University of Technology to his current position at one of Europe's leading technical universities. He has established himself as a leading researcher in transport systems with a focus on railway operations and optimization. His educational background includes: Doctoral Degree (Ph.D.) from Delft University of Technology (2007-2010) Master's Degree in Management & Automation Engineering from Roma TRE University, Italy (2004-2006) Bachelor's Degree in Computer Science Engineering from Roma TRE University, Italy (2001-2004) Professor Corman's research focuses on analytics, optimization and control in transport systems, with particular emphasis on public transport, railway networks, and logistics systems. His work bridges the gap between theoretical optimization models and practical applications in real-world transportation networks. He has developed innovative approaches to railway traffic management, public transport operations, and freight logistics that address contemporary challenges in transportation systems. His recent publications demonstrate a strong trend toward data-driven approaches in transportation, with increasing integration of machine learning techniques, particularly deep learning and Bayesian networks, into traditional transportation optimization problems. There's also a growing emphasis on sustainability considerations in transportation systems, as evidenced by research on environmental impacts of railway infrastructure. His scientific contributions include: Development of advanced models for railway traffic management and optimization Innovative approaches to public transport disruption analysis and recovery Integration of on-board monitoring data for railway infrastructure management Probabilistic modeling of transportation operations under uncertainty Professor Corman leads several significant research projects including ESTRA (Efficient Safe Train Dynamics), LeRaBe (Learning railways for better schedules), NCCR (Dynamic stochastic learning of train dynamics as enabler to highly automated train operation), RaDiCa (Modeling the Impact of Digitalization on Railway Capacity), and UrbanEcho (Envisioning tomorrow - A digital twin technology for sustainable urban planning in data poor regions). He teaches multiple courses at ETH Zürich including Public Transport Design and Operations, Public Transport and Railways, Logistics and Freight Transportation, and contributes to doctoral seminars on Data Science and Machine Learning in Civil Engineering. His teaching reflects his research expertise, bridging theoretical concepts with practical applications in transportation systems.

Rohan SenView profile

Researcher

• Kernel Methods
• Statistical Learning Theory
• Econometrics
• +3 more

Rohan Sen is a PhD Assistant at the Faculty of Economic Sciences, University of Italian Switzerland (USI), affiliated with the Institute of Finance (IFin). His research focuses on kernel methods, statistical learning theory, econometrics, and explainability, with expertise in mathematical finance and statistics. He holds a Master's in Mathematics and Computing from IIT Guwahati (2019) and is pursuing his PhD under Prof. Paul Schneider and Prof. Michael Multerer. Research interests include kernel-based methods for moment problems, financial econometrics, and explainable AI. Recent work addresses observation-specific explanations via scattered data approximation and fast empirical scenarios in computational finance. He actively contributes to conferences like the World Conference on Explainable AI and FinEML. No scientific awards are listed. His professional experience includes a stint as a Senior Software Engineer at HCL Technologies (2019-2020). He has participated in workshops on decision-making under uncertainty and advanced C++ programming. Current activities involve probabilistic energy forecasting and kernel-based quantile regression.

• International Trade
• Economic Forecasting
• Bayesian Neural Networks
• +5 more

Davide Arioldi is a Postdoctoral Researcher and Scientific Collaborator at the Institute for Economic Research (IRE) within the Faculty of Economic Sciences at the University of Italian Switzerland (USI). He holds a PhD in Economics from USI (2018) with a thesis on international trade and has prior experience as a quantitative analyst and portfolio manager in investment firms. His current research focuses on Bayesian Neural Networks, Probabilistic Programming, causal structure learning via graphical models, and text analysis. He is affiliated with the Observatory of Public Finance and Energy (O-FPE), where he develops tax revenue forecasting models and compiles comparative studies on cantonal public finances. He also contributed to the PanelCODE project and the contati.ch platform analyzing policy impacts. Research expertise includes international trade dynamics, consumer behavior, productivity-wage gaps, bargaining theory, firm production, and network analysis. His work spans economic policy evaluation, unstructured data analysis, and regional economic disparities. He is co-developer of the contati.ch platform funded by a private foundation to assess policy impacts on local populations. Key projects include annual cantonal public finance studies, tax revenue modeling at O-FPE, and business dynamics analysis using unstructured online data since 2016. He collaborates across interdisciplinary teams in Lugano, Switzerland.

Markus MeiererView profile

Assistant Professor

• Marketing Analytics
• Customer Relationship Management
• Customer Lifetime Value
• +7 more

Markus Meierer is an Assistant Professor at the Institute of Management, Geneva School of Economics and Management (GSEM), University of Geneva. He holds a Ph.D. in Business and Economics from the University of Trier, Germany, and previously served as a Senior Associate at the University of Zurich, where he led the 'Networks & Consumers' project in the University Research Priority Program 'Social Networks'. His research lies at the intersection of marketing, statistics, computational linguistics, and computer science, with a focus on customer purchase behavior, customer lifetime value, and marketing analytics. He employs advanced statistical and machine learning methods to model customer attrition, transaction behavior, and spending patterns, particularly in non-contractual settings. Meierer's recent work emphasizes uplift modeling for marketing campaigns, integration of user-generated content (images and text) for brand positioning, and the development of open-source tools like CLVTools and REndo in R. His publications appear in leading journals such as Marketing Science and Journal of Statistical Software . Rigor and Relevance Research Award, Swiss Academy of Marketing Science He teaches executive courses in Marketing Management and Applied Programming with R and Python. He actively disseminates his research through academic conferences and seminars globally and contributes to the research community via software development and open science practices. His work has practical applications in customer base analysis, marketing strategy, and decision support systems.

Xin WenView profile

Researcher

• Energy System Modeling
• Uncertainty Quantification
• Renewable Energy Integration
• +7 more

Xin Wen is a Senior Researcher at the Renewable Energy Systems (RES) group at the University of Geneva, where she conducts advanced research on uncertainty modeling for long-range energy projections across Europe. She is actively involved in the Swiss National Science Foundation project 'Accuracy' and contributes to teaching in the Energy, Climate, and Environment course. Research Interests: Dr. Wen's work centers on energy system modeling, uncertainty quantification, stochastic optimization, and renewable energy integration. She investigates how statistical and optimization models can improve the accuracy of energy projections, particularly at sub-national levels, and evaluates the performance of major energy agencies. Her research supports energy policy development by incorporating societal factors and historical transitions into modeling frameworks. Publication Trends: Her recent publications (2021–2025) demonstrate a strong focus on improving energy modeling accuracy through hindcasting, probabilistic projections, and model inter-comparison. She frequently publishes in high-impact journals such as Applied Energy , Energy , and Nature Energy , addressing topics including electricity market governance, technology diffusion, and microgrid optimization under uncertainty. Scientific Contributions: Key contributor to the Swiss National Science Foundation project 'Accuracy' Co-authored foundational datasets for evaluating European electricity transitions Developed methods for assessing model accuracy and incorporating societal factors in energy modeling Teaching and Collaboration: Dr. Wen teaches in the Energy, Climate, and Environment program and collaborates extensively with researchers like Evelina TrutnevYTE and Marc Jaxa-Rozen. She is based at the University of Geneva's Renewable Energy Systems group, contributing to both research and policy-relevant analysis. Laboratory Affiliation: She is a core member of the Renewable Energy Systems (RES) group at the University of Geneva, which focuses on data-driven modeling of sustainable energy transitions and policy evaluation in Switzerland and Europe.

Taiji SuzukiView profile

Associate Professor

• Machine Learning
• Statistical Learning Theory
• Deep Learning
• +6 more

Prof. Taiji Suzuki is an Associate Professor at the University of Tokyo in the Department of Mathematical Informatics . He also serves as Team Leader of the "Deep Learning Theory" team at AIP-RIKEN , Japan. With a PhD in Information Science and Technology from the University of Tokyo (2009), he has held academic positions at the University of Tokyo (2009-2013) and Tokyo Institute of Technology (2013-2017) before returning to the University of Tokyo in 2017. University of Tokyo (2004: BEng in Mathematical Engineering) University of Tokyo (2006: MSc in Information Science and Technology) University of Tokyo (2009: PhD in Information Science and Technology) His research interests span statistical learning theory , deep learning , kernel methods , sparse estimation , and stochastic optimization . He investigates how neural networks adapt to function smoothness, avoid the curse of dimensionality, and achieve global optimization through mean-field dynamics. His work bridges theoretical guarantees (minimax optimality, convergence analysis) with practical implementations (transformers, graph neural networks, diffusion models). The 15 most recent articles focus on transformers' representation power, graph neural networks' limitations, diffusion models' convergence, and optimization theories for deep learning. Key themes include information-theoretic bounds , feature learning dynamics , and mean-field analysis . He explores applications in AI for medicine, federated learning, and biomedical modeling. Awards & Recognition: Outstanding Paper Award, ICLR 2021 MEXT Young Scientists’ Prize Outstanding Achievement Award, Japan Statistical Society 2017 Outstanding Achievement Award, Japan Society for Industrial and Applied Mathematics 2016 Best Paper Award, IBISML 2012 Best Paper Candidate, ICDM 2019 He has served as Area Chair for NeurIPS, ICML, ICLR, AISTATS, and as Program Chair for ACML. His scientific contributions include convergence theories for stochastic gradient methods, minimax analysis of deep learning vs kernel methods, and novel frameworks for distributional optimization in diffusion models.