Find Academic Supervisors

Marco ZaffalonView profile

Professor

• Machine Learning
• Causal AI
• Probabilistic Graphical Models
• +3 more

Marco Zaffalon is a Professor at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI) and Scientific Director of the Dalle Molle Institute for Artificial Intelligence (IDSIA). He holds an MSc in Computer Science (cum laude) and a PhD in Applied Mathematics. His research focuses on causal AI, machine learning, and probabilistic models under uncertainty, with applications in defense, environment, medicine, engineering, and business. He leads a 30-member research group in probabilistic machine learning and has secured over 15 million Swiss Francs in competitive grants. Education: MSc in Computer Science (cum laude), PhD in Applied Mathematics. Professional milestones include roles as a software engineer at an Italian insurance group, consultant for SGS-Thomson, post-doctoral fellow at IDSIA, and tenure as a researcher since 2001. Research interests emphasize foundational and applied aspects of probability theory, particularly imprecise probability, and causal reasoning. His work integrates knowledge-based systems, probabilistic graphical models, and robust algorithms for real-world problems. Notable projects include OmniProfiler , Credal Model Averaging , and Learning under Near-Ignorance . Awards & Recognition: President of SIPTA (2019–present) Senior Area Editor for International Journal of Approximate Reasoning (IJAR) Top 2% Scientist (Stanford’s ranking since 2019) Grants & Leadership: Oversaw 15+ million CHF in competitive research funding Founded and directs a 30-member research group He collaborates on interdisciplinary projects, including environmental modeling, medical applications, and defense-related AI systems through IDSIA’s labs and partnerships.

Leonardo SantoroView profile

Researcher

• Statistical Inference
• Optimal Transport
• Functional Data Analysis
• +4 more

Leonardo Santoro is a Postdoctoral Researcher at EPFL's Chair of Mathematical Statistics within the School of Basic Sciences. His academic appointments include current research roles at EPFL (2024–present) and a research stay at UC Berkeley (2023). His research focuses on theoretical intersections of statistics and machine learning. Key interests include kernel methods for machine learning, nonparametric inference for high-dimensional and functional data, and optimal transport theory. His work addresses inverse problems through mathematical frameworks like covariance operators, transport flows, and diffusions. His publication trends reveal specialization in statistical methodology development, particularly for complex data structures. Research outputs emphasize mathematical rigor in machine learning applications, with emphasis on geometric statistics and probabilistic modeling. Professional trajectory includes doctoral training under Victor Panaretos at EPFL, preceded by masters studies at ETH Zurich and undergraduate education at Università di Trento. His technical profile demonstrates applied experience through an industry internship at Teralytics AG developing mobility models from telco data.

Jeffrey HuangView profile

Professor

• Design Thinking
• Artificial Intelligence in Architecture
• Strategic and Experience Design
• +3 more

Jeffrey Huang is a Full Professor at EPFL, holding joint appointments in the Faculty of Computer and Communication Sciences (IC) and the Faculty of Architecture, Civil and Environmental Engineering (ENAC). He directs the Institute of Architecture and leads the Media x Design Laboratory (MxD Lab). His areas of expertise include Design Thinking, Artificial Intelligence in Architecture, Urban Digital Twins, and Computational Urbanism. Educated at ETH Zurich (DiplArch), Harvard University (Masters and PhD), Huang has held academic roles at MIT, Harvard, Tsinghua University, and the Singapore University of Technology and Design (SUTD). He is a recipient of the Gerald McCue Medal for academic excellence and the 2023 ACSA Best Article Award for his work on GANs and architectural design. Recent research focuses on combining machine learning with architectural design, particularly exploring generative adversarial networks (GANs), spatial analysis, and urban digital twins. His projects include the Blue City Innosuisse Flagship Project and the Singapore Prototypologies initiative. Awards include the 2023 ACSA Best Article Award and the Best Presentation Award at CAAD Futures 2009. He supervises numerous PhD students and has led significant research grants such as the Innosuisse Flagship Project and the MIT-SUTD collaborations. Huang co-founded Convergeo, a strategic design firm, and has been involved in creating new architectural education programs, including the Architecture and Sustainable Design Pillar at SUTD.

Angelika StegerView profile

Professor

• Probabilistic Methods
• Randomized Algorithms
• Graph Theory
• +3 more

Prof. Angelika Steger is a Full Professor in the Department of Computer Science at ETH Zurich, leading research in theoretical computer science since 2003. She holds a Master's in Applied Mathematics from Stony Brook University (1985) and a PhD from the University of Bonn (1990). Her career includes roles at Kiel, Duisburg, and TU München before joining ETH. She is a Leopoldina member (2007), ICM speaker (2014), and Collegium Helveticum Fellow (2009+). Research focuses on probabilistic methods, randomized algorithms, graph theory, and combinatorial optimization. She has contributed to understanding discrete structures, neural networks, and algorithmic resilience. Awards include recognition in both computer science and mathematics circles. Her work bridges theoretical foundations with applications in AI, neuroscience, and distributed systems.

• Quantum Computing
• Machine Learning
• Probabilistic Programming
• +3 more

Timon Gehr is part of the Professorship for Computer Science at ETH Zurich's Department of Computer Science, affiliated with the Institute of Programming Languages and Systems. His research focuses on quantum computing, probabilistic programming, neural network robustness, and privacy enforcement. He has contributed to the development of quantum languages like Silq and frameworks for certifying adversarial robustness in machine learning systems. His research interests include formal methods for programming languages, scalable symbolic reasoning, and applying probabilistic techniques to security and privacy. Recent work emphasizes robustness certification of neural networks and symbolic integration in machine learning. Key projects involve exact inference for probabilistic programs and differential privacy violation detection. Notable publications include work on quantum uncomputation, adversarial examples, and integrating logic into neural networks. No specific advising or grant details are provided in the text, but his involvement with the Institute of Programming Languages and Systems indicates active participation in academic collaborations and research teams.

Ya-Ping HsiehView profile

Lecturer

• Machine Learning
• Optimization
• Stochastic Processes
• +3 more

Dr. Ya-Ping Hsieh is a Lecturer at the Department of Computer Science at ETH Zürich, affiliated with the Institute for Machine Learning. His research focuses on advanced optimization techniques, stochastic processes, and their applications in machine learning. Key areas include non-convex optimization, diffusion models, and optimal transport theory. His work often addresses convergence properties of algorithms and their theoretical guarantees in complex systems. Dr. Hsieh's recent publications explore topics such as entropy-maximizing exploration via diffusion models, Riemannian stochastic optimization frameworks, and the dynamics of min-max algorithms. His contributions bridge theoretical foundations and practical implementations, emphasizing rigorous mathematical analysis. No scientific awards or grants are explicitly listed in the provided materials. He is part of the Institute for Machine Learning at ETH Zürich, contributing to both academic and applied research in computational methods.

Mengshuo JiaView profile

Researcher

• AI4Science
• Uncertainty Modeling
• Probabilistic Analysis
• +4 more

Dr. Mengshuo Jia is a Senior Scientist and Principal Investigator at ETH Zürich, affiliated with the Power Systems Laboratory (PSL) within the Department of Electrical Engineering. He holds a Ph.D. from Tsinghua University (2016–2021) and a B.Eng. from North China Electric Power University (2012–2016). His roles include Guest Lecturer for 'Optimization in Energy Systems' and Associate Editor for IEEE Systems Journal and IET Renewable Power Generation. Education: Ph.D. in Electrical Engineering, Tsinghua University (2016–2021) B.Eng. in Electrical Engineering, North China Electric Power University (2012–2016) Research focuses on AI4Science , Uncertainty Modeling , Probabilistic Analysis , Stochastic Optimization , and Data-Driven Power Systems . Notable contributions include the RePower LLM-driven research platform and the DALINE toolbox for power flow linearization. His work bridges AI advancements with energy system challenges, enhancing autonomous research and grid optimization. Recent publications emphasize LLM applications in energy systems, small modular reactor integration, and hydrogen supply chain optimization. Over 10 peer-reviewed articles since 2022 highlight interdisciplinary innovation. Awards: 2023 ESI Hot Paper (top 0.1%) and Highly Cited Paper (top 1%) 2023 China First Prize of High-influence Papers 2022 Springer Thesis Award Advising and grants include leadership in Swiss National Science Foundation projects and editorial roles in top-tier journals. Collaborations emphasize data-driven methodologies and privacy-preserving distributed algorithms. Labs: Active in the Power Systems Laboratory (PSL) at ETH Zurich, advancing research in energy system optimization and AI integration.

• Robotics
• Human-Computer Interaction
• Machine Learning
• +3 more

Dr. Yi-Chi Liao is a Lecturer in the Department of Computer Science at ETH Zürich, specializing in intelligent interactive systems. Their research focuses on human-robot interaction, wearable technology, and optimization techniques for user interface design. Key areas include developing datasets for naturalistic handover behaviors with robotic limbs, human-in-the-loop optimization methods, and computational workflows for designing input devices. Research Interests: Explores the intersection of robotics, machine learning, and human-centered design. Recent work emphasizes Bayesian optimization techniques, affordance theory, and tactile feedback systems. Projects like the 3HANDS dataset and ThirdHand wearable robotic arm demonstrate innovation in human augmentation and interaction design. Advising: Supervises doctoral student Peizhuo Li in the D-INFK program. Research outputs span 2015–2025, with notable contributions to haptic interfaces, multi-objective optimization, and wearable computing. Active in conferences and journals addressing HCI, robotics, and design automation.

Erwin RieglerView profile

Lecturer

• Information Theory
• Signal Processing
• Communications
• +3 more

Dr. Erwin Riegler is a Lecturer at the Department of Information Technology and Electrical Engineering (D-ITET) at ETH Zurich. His research focuses on information theory, signal processing, and their applications in communication systems, with particular expertise in analog compression, matrix completion, and uncertainty principles. He has contributed to theoretical frameworks for rate-distortion analysis, lossless compression techniques, and the design of efficient algorithms for MIMO systems. Key research areas include: Information-theoretic limits of data compression Signal processing in analog systems Algorithmic approaches for sparse signal recovery Optimization techniques in MIMO communication channels Recent work emphasizes interdisciplinary applications, such as neural networks for measure theory and probabilistic methods for signal separation. No scientific awards are explicitly mentioned in the provided texts. His academic contributions span over 30 peer-reviewed articles since 2001, covering topics from string theory to modern communication systems.

Anna ScampicchioView profile

Researcher

• Intelligent Control Systems
• Model Predictive Control
• Machine Learning
• +3 more

Dr. Anna Scampicchio is a Researcher at ETH Zürich, affiliated with the Professorship for Intelligent Control Systems. Her work focuses on advancing control theory and machine learning methodologies, particularly in data-driven control systems, model predictive control, and Bayesian learning techniques. She has contributed to the development of algorithms for system identification, optimal control, and robust control strategies. Her research integrates theoretical analysis with practical applications in robotics and dynamical systems. Key publications include studies on kernel methods, randomized signatures for learning dynamics, and Bayesian multi-task learning approaches. Dr. Scampicchio holds a Ph.D. (implied by her title) and has published extensively in top-tier journals and conferences, addressing challenges in intelligent control systems and machine learning integration.

• Machine Learning
• Biomedical Informatics
• Medical Genomics
• +3 more

Prof. Gunnar Rätsch is a Full Professor in the Department of Computer Science at ETH Zürich and Deputy Head of the Institute for Machine Learning. His research focuses on developing machine learning methods for biomedical applications, including genomics, medical imaging, and clinical decision support systems. He specializes in integrating multi-omics data, spatial transcriptomics, and time-series analysis to address challenges in precision medicine and critical care. His work emphasizes ethical AI frameworks, algorithmic fairness, and robust clinical prediction models. Key research areas include: Deep learning for medical imaging and histopathology Single-cell analysis and tumor profiling Reinforcement learning for treatment optimization in ICUs Multimodal data integration for clinical applications Recent work highlights advancements in: Standardizing single-cell cytometry readouts for clinical use Developing foundation models for critical care time-series analysis Creating interpretable survival models for ICU patients His contributions span foundational machine learning theory and applied healthcare technologies, with a focus on translational research to improve clinical outcomes. Current projects include the Tumor Profiler Study for multi-omic tumor analysis and ethical frameworks for clinical AI systems.

Gerhard SchrattView profile

Professor

• Molecular Neuroscience
• Synaptic Plasticity
• Non-coding RNAs
• +1 more

Gerhard Schratt is Full Professor and Head of the Institute for Neuroscience at ETH Zürich. His laboratory investigates molecular mechanisms of synapse development and plasticity, with emphasis on non-coding RNA regulation in neurological disorders. Research integrates molecular neurobiology with computational approaches to study microRNA functions in neuronal development, synaptic homeostasis, and stress responses. Current projects examine RNA-based therapies for neurodegenerative conditions and computational pathology platforms for digital histology. Professor Schratt's group develops AI frameworks for neurological assessment including multimodal dementia diagnosis, voice-based cognitive testing with privacy protection, and digital pathology for kidney disease. Collaborative projects with the Chan Zuckerberg Initiative explore mitochondrial regulation by microRNAs. He teaches molecular neurophysiology and neuroscience courses, supervising doctoral candidates in systems neuroscience approaches.

• Structural Health Monitoring
• Wind Energy
• Graph Neural Networks
• +3 more

Dr. Gregory Duthé is a researcher at the ETH Zürich in the Structural Mechanics and Monitoring department. His work bridges computational modeling and structural diagnostics with applications in renewable energy systems. Research contributions include: Development of Graph Neural Networks for aerodynamic flow reconstruction Advancements in wake-induced load estimation for wind farms Innovations in unsupervised fault detection for offshore turbine systems Key publication trends show focus on physics-informed machine learning (2025), multi-agent infrastructure decision support (2025), and leading edge erosion modeling (2021). He combines graph-based architectures with structural health monitoring techniques across wind energy applications. Contact: duthe@ibk.baug.ethz.ch

• Probabilistic Machine Learning
• Causal AI
• Imprecise Probabilities
• +2 more

Marco Zaffalon is Professor and Scientific Director at IDSIA (Istituto Dalle Molle di Studi sull'Intelligenza Artificiale), affiliated with the Università della Svizzera italiana's Faculty of Informatics. He leads a 30-member research group on probabilistic machine learning and has published over 150 papers. Education: M.Sc. in Computer Science (Università degli Studi di Milano) Ph.D. in Applied Mathematics (Università degli Studi di Milano) Research spans probabilistic machine learning, causal AI, imprecise probabilities, and quantum computation. His work develops theoretical foundations for uncertainty reasoning and applies them to AI systems. Recent publications focus on causal inference with LLMs, counterfactual computation, and quantum decision models. Articles consistently explore intersections of probability theory, computational methods, and real-world applications like healthcare. Trends include advancing tractability in causal queries and bridging logical frameworks with machine learning. Administrative roles include co-founding Artificialy (as Chief Scientist) and directing IDSIA since 2019. He teaches courses in Causal AI, Uncertain Reasoning, and Probability.

Daniele ZambonView profile

Researcher

• Graph Representation Learning
• Machine Learning
• Learning in Non-Stationary Environments
• +7 more

Daniele Zambon is a postdoctoral researcher at the Dalle Molle Institute for Artificial Intelligence (IDSIA), affiliated with Università della Svizzera italiana (USI) in Lugano, Switzerland. He is a member of the Faculty of Computer Science and the Graph Machine Learning Group, as well as the IEEE Task Force on Learning for Graphs. PhD : Informatics, Università della Svizzera italiana (USI), 2022 Master’s & Bachelor’s : Mathematics, University of Milan, Italy Visiting Researcher : University of Florida, University of Exeter Internship : STMicroelectronics, Italy His research lies at the intersection of machine learning and graph-structured data, with a strong emphasis on graph representation learning , learning in non-stationary environments , and time series analysis . He explores how to model dynamic graphs, detect anomalies and changes over time, and develop deep learning methods for spatiotemporal forecasting. His work integrates statistical testing, geometric deep learning, and neural architectures like Graph Neural Networks (GNNs) and Neural ODEs. The recent publications highlight a clear trend toward temporal and dynamic graph modeling , especially for time series forecasting and irregularly sampled data . There is a growing focus on generative and foundation models for graphs , uncertainty-aware learning , and the creation of benchmark datasets like PeakWeather. His work bridges theoretical contributions (e.g., statistical tests, Kalman filters on graphs) with practical applications in sensing, environmental modeling, and system monitoring. Co-author of patent: Method for the Detecting Electrocardiogram Anomalies and Corresponding System (US10610162B2) PhD thesis featured in D22 Excellent Computer Science Dissertations (2022) Associate Editor, IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS) Organizer of tutorials and special sessions at ICML, LoG, KDD, and ESANN Daniele actively contributes to the academic community through advising and teaching at USI’s Bachelor’s and Master’s programs. He has co-supervised research projects and co-organized educational initiatives such as tutorials on graph deep learning. His collaborative work involves grants and partnerships with institutions like MeteoSwiss, leading to impactful datasets and applied research. He is deeply involved in building research capacity through workshops and community engagement in the graph learning field. He is a core member of the Graph Machine Learning Group at IDSIA and contributes to the IEEE Task Force on Learning for Graphs , fostering international collaboration and setting research agendas in the domain of graph-based AI.