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Maliki MoustaphaView profile

Lecturer

• Structural Engineering
• Uncertainty Quantification
• Reliability Analysis
• +3 more

Dr. Maliki Moustapha is a Lecturer at ETH Zürich's Department of Civil, Environmental and Geomatic Engineering, affiliated with the Chair of Risk, Safety and Uncertainty Quantification. He holds a master's degree in structural engineering from the French Institute for Advanced Mechanics (IFMA) and a PhD from Clermont Université and ETH Zurich, focusing on reliable lightweight automotive design under impact conditions. His current work involves advancing reliability-based design optimization techniques for the UQLab software. His research interests span uncertainty quantification, reliability analysis, surrogate modeling, and sustainable building renovation strategies. He has contributed to methodologies addressing stochastic simulators, noise-aware algorithms, and robust design under budget constraints. His work bridges engineering systems and environmental sustainability, with a focus on automotive structures and civil infrastructure resilience. Dr. Moustapha has published extensively on topics including active learning for reliability analysis, stochastic emulators, and cost-effective renovation strategies for Swiss residential buildings. His recent articles emphasize probabilistic modeling, surrogate-based optimization, and the integration of environmental and economic performance metrics in design decisions. Though no scientific awards are explicitly mentioned, his contributions to UQLab's reliability modules reflect his impactful technical leadership. He collaborates on projects involving automotive industry partners (e.g., PSA Peugeot Citroën) and contributes to software development for uncertainty quantification tools.

• Model Predictive Control
• Nonlinear Systems Control
• Robust Control
• +3 more

Dr. Johannes Köhler is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich, Switzerland. His research focuses on advanced control systems, particularly model predictive control (MPC), robust control of nonlinear systems, and data-driven control strategies. He holds a Master's degree in Engineering Cybernetics from the University of Stuttgart (2017) and a Ph.D. in Mechanical Engineering (2021), for which he received the 2021 European Systems & Control PhD Award. His work emphasizes safety, optimization, and adaptability in control systems, with applications ranging from autonomous navigation to energy-efficient building systems. Key academic achievements include the 2022 ETH Zurich Career Seed Award for his work on data-driven predictive control, and the 2020 Best Publication Award at the University of Stuttgart for applying MPC to combat the COVID-19 outbreak. He has authored over 60 peer-reviewed articles, with recent contributions in stochastic MPC, robust nonlinear control, and safety-aware exploration strategies. Education: Ph.D. in Mechanical Engineering, University of Stuttgart (2021) Master of Engineering Cybernetics, University of Stuttgart (2017) His research group at ETH Zürich develops algorithms for real-time control of complex systems, with a focus on embedded MPC for robotics and energy systems. Current projects include safe autonomous navigation, adaptive control under uncertainty, and data-driven methods for nonlinear systems.

Mohamed AbdalmoatyView profile

Researcher

• Data-Driven Modelling
• Control Systems
• Signal Processing
• +5 more

Dr. Mohamed Abdalmoaty is a Researcher at ETH Zürich's Institut für Automatik, specializing in the Department of Automatic Control. His work focuses on Data-Driven Modelling and Control , with expertise in system identification, stochastic systems, and optimal control. He holds an affiliation within the Professorship for Complex Systems Control. His research interests emphasize data-driven approaches for predictive control, frequency-domain identification, and nonlinear dynamical systems. He has contributed to advancements in Kalman filter formulations, stochastic Wiener models, and cybersecurity in control systems, particularly in medical applications like the artificial pancreas. Abdalmoaty’s recent publications (2022–2024) highlight innovations in time-varying normalizing flows, privacy-preserving network control, and robust parameter estimation under uncertainty. His work bridges theoretical control systems with practical applications in machine learning and biomedical engineering. He has developed software tools for system identification and simulation, including implementations for frequency-domain analysis and nonparametric closed-loop identification. His research aligns with emerging trends in hybrid machine learning-control frameworks and resilient cyber-physical systems.

• Machine Learning
• Data Science
• Computer Vision
• +3 more

Dr. Andreas Streich is a Lecturer at the Department of Computer Science at ETH Zurich. His research focuses on Machine Learning, Data Science, and their applications in fields like medical imaging and signal processing. He teaches courses including Computer Science I, Building ML/AI Applications, and Data Science & Machine Learning. His work spans neural networks, clustering algorithms, and multi-label classification. He has contributed to advancements in medical diagnostics via deep learning and acoustics-based signal processing. No scientific awards or student advisees are explicitly mentioned in the provided texts.

Anupama UnnikrishnanView profile

Researcher

• Quantum Computing
• Quantum Information Science
• Network Security
• +3 more

Anupama Unnikrishnan is a researcher at ETH Zurich, affiliated with the ETHCQST+ initiative focused on quantum science and technology. Her work intersects quantum computing and computer security, with a particular emphasis on probabilistic data structures and quantum network verification. Research Interests: Quantum computing and cryptography Security analysis of probabilistic data structures Verification protocols in quantum networks Recent Article Trends: Her publications explore adversarial contexts in data structures, authentication mechanisms for quantum teleportation, and anonymity in quantum networks, demonstrating a strong focus on security and integrity in emerging quantum technologies.

• Computer Vision
• Machine Learning
• 3D Reconstruction
• +3 more

Siwei Zhang is a researcher at the ETH Zürich , focusing on computer vision and machine learning. Their work intersects with robotics, human motion analysis, and egocentric vision. Email: siwei.zhang@inf.ethz.ch Research Interests 3D Human Body Modeling Egocentric Vision Systems Motion Reconstruction & Interaction Neural Architecture Optimization Surgical Pose Estimation Selected Publications (2025-2019) Recent work includes neural volumetric body models (2025), egocentric synthetic data generation (2024), robust human motion reconstruction (2024), surgical pose datasets (2023), and foundational studies in probabilistic mesh recovery, whole-body grasping, and face reenactment (2019-2022). Key trends combine computer vision, robotics, and deep learning with applications in scene understanding and human-computer interaction.

Marc PoulyView profile

Professor

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +6 more

Marc Pouly is a Professor of Artificial Intelligence at Lucerne University of Applied Sciences and Arts (HSLU), where he also serves as Co-Head of the Applied AI Research Lab. He holds a PhD in Artificial Intelligence from the University of Fribourg (2008) and has extensive industry experience as a Chief Data Scientist at Jaywalker AG and Scientific Advisor for Alpine AI and Artificialy SA. His research focuses on AI-driven solutions for healthcare, computer vision, natural language processing, and industrial optimization. Education: PhD in Artificial Intelligence (2004–2008), University of Fribourg MSc in Computer Science & Mathematics (2002–2004), University of Fribourg BSc in Computer Science (1999–2002), University of Fribourg Research Interests: His work spans Medical AI (e.g., dermatology diagnostics via computer vision), Generative Models , Recommender Systems , and AI Ethics . He co-developed the SkinApp for automated skin analysis and pioneered projects like CleanPatrick for image data quality audits. His expertise in self-supervised learning addresses data scarcity challenges in healthcare. Key Achievements: 2023: Best Paper Awards (IARIA, MICCAI) 2021: Research Mosaic recognition by swissuniversities 2017: Aha! Award for the SkinApp project Labs & Collaboration: He leads the Applied AI Lab and collaborates with institutions like ETH Zurich (co-supervising PhDs) and industry partners such as Prepress Media AG and the Swiss Allergy Centre. His work bridges academia and industry, emphasizing AI’s practical applications in healthcare, marketing, and cybersecurity.

Radu VintanView profile

Researcher

• Theoretical Computer Science
• Online Algorithms
• Approximation Algorithms
• +5 more

Radu Vintan is a Doctoral Assistant and PhD student at École polytechnique fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences, affiliated with the Institute of Computer Science and the Theory of Computation Laboratory 2 (THL2). He is part of the Doctoral Program in Computer and Communication Sciences (EDIC). Education: Bachelor's and Master's in Computer Science, Technical University of Munich (TUM), Germany PhD in Computer Science, École polytechnique fédérale de Lausanne (EPFL), Switzerland (ongoing) His primary research interests lie in theoretical computer science , particularly online algorithms and approximation algorithms . He also has experience in machine learning and software engineering . His work often involves algorithmic design and analysis for graph and network problems. The recent publications reflect a strong focus on online edge coloring and network update algorithms , with contributions to top-tier conferences such as FOCS, STOC, SODA, and INFOCOM. These works explore both theoretical limits and practical algorithmic solutions, often bridging deterministic and randomized approaches. Scientific Awards: No awards mentioned. Advising and Grants: Radu Vintan is advised by Professor Ola Svensson. There is no mention of him advising students or receiving independent grants. His research is conducted within the Theory group at EPFL, supported through his Doctoral Assistant position. Laboratories and Teams: He is an active member of the Theory of Computation Laboratory 2 (THL2) at EPFL, contributing to foundational algorithmic research in collaboration with leading experts in theoretical computer science.

Daniel KuhnView profile

Professor

• Data-driven Optimization
• Stochastic Programming
• Robust Optimization
• +3 more

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.

• Machine Learning
• Natural Language Processing
• Computational Linguistics
• +3 more

Prof. Ryan Cotterell is an Assistant Professor in the Department of Computer Science at ETH Zürich. His research focuses on machine learning and natural language processing, particularly in transformer models, syntactic control of language systems, and computational linguistics. He teaches courses such as Neural Networks and Computational Complexity, Natural Language Processing, and Advanced Topics in Machine Learning. His work bridges theoretical foundations and practical applications, exploring topics like language model expressivity, information locality, and formal language theory. Recent publications highlight innovations in controlled generation, model evaluation, and multimodal systems. While no specific awards are listed, his contributions to the field are evident through his extensive publication record and academic roles. Advising and grant details are not explicitly mentioned in the provided texts, but his involvement in teaching and research indicates active mentorship and project leadership. No lab or team affiliations specific to Ryan Cotterell are detailed here.

• Nonlinear Dynamical Systems
• Reduced Order Modeling
• Hybrid Testing
• +3 more

Tom Simpson is a Researcher at ETH Zurich specializing in computational mechanics and data-driven engineering methods. Previously completing his PhD at the Chair of Structural Mechanics (2018-2023), he focuses on developing advanced simulation techniques for complex engineering systems. His work bridges theoretical mechanics with machine learning applications. His educational background includes a Master's degree in Mechanical Engineering from the University of Sheffield (2018), where his thesis explored manifold learning techniques for nonlinear dynamical systems. During his studies, he gained practical experience at the Advanced Manufacturing Research Centre investigating aerospace alloy machining methods. Simpson's research centers on reduced order modeling, hybrid simulation techniques, and machine learning applications in structural dynamics. His work particularly emphasizes variational autoencoders for nonlinear systems, parametric dependencies in engineering models, and real-time simulation frameworks. Key application areas include structural health monitoring, offshore wind energy systems, and railway vehicle dynamics. His publication record demonstrates consistent focus on integrating physics-based modeling with data-driven approaches. Recent work shows increasing sophistication in error bound estimation, probabilistic modeling, and online monitoring-conditioned simulations. The research trajectory reveals progression from foundational nonlinear dynamics to cutting-edge hybrid AI-physics simulation frameworks. Simpson actively contributes to the DyVirt research project and collaborates extensively with Professor Eleni Chatzi's research group. His work appears in leading engineering journals including Mechanical Systems and Signal Processing, Scientific Reports, and Frontiers in Energy Research, demonstrating both theoretical rigor and practical engineering applications.

• Prevention of Mental Disorders
• Psychiatry
• Psychopathology
• +7 more

Andrea Raballo is a Full Professor at the University of Lugano (USI) within the Faculty of Biomedical Sciences. His research focuses on prevention of mental disorders, psychopathology, and youth mental health, particularly in schizophrenia spectrum disorders and clinical high-risk (CHR-P) populations. He leads projects addressing early intervention strategies, diagnostic accuracy, and the neurodevelopmental underpinnings of psychopathology. His work integrates AI-driven methodologies (e.g., LLMs, adaptive RAG systems) for mental health screening and psychometric analysis. Research Interests Psychosis spectrum disorders and their early detection Neurodevelopmental models of self-disorders Transdiagnostic frameworks for youth mental health Evidence-based interventions in clinical high-risk populations Psychiatric diagnosis beyond traditional heuristics His recent publications emphasize challenges in CHR-P management, including pharmacological transparency and baseline treatment effects. He advocates for methodological rigor in prognostic precision and early intervention services to bridge gaps between child and adult mental health systems. Advising & Grants No formal advisees listed. His research is supported by funded projects focusing on translational psychiatry and clinical staging frameworks. Labs/Teams Part of the Parma Early Psychosis Program and contributes to international initiatives like ENIGMA and the EPA Summer School on Research.

Romain PicView profile

Researcher

• Probabilistic Forecasting
• Machine Learning
• Meteorology
• +3 more

Romain Pic is a Researcher (Post-doctoral) at the Research Institute for Statistics and Information Science, University of Geneva. He holds a PhD in Statistics and Machine Learning from Université Bourgogne Franche-Comté (2024), and Master’s degrees in Statistics and Machine Learning (Université Lyon 2) and Physics of Complex Systems (Sorbonne Université). His research focuses on probabilistic forecasting, machine learning applications in meteorology, and statistical evaluation methods. He has collaborated with institutions like ETH Zurich and Météo-France. Key research interests include theoretical properties of probabilistic forecasts, ensemble postprocessing, and the development of proper scoring rules for multivariate predictions. Recent work includes advancements in distributional regression U-Nets for precipitation forecasting and aggregation-based scoring rules. His articles emphasize practical applications in weather systems and methodological contributions to statistical theory. Publications span topics such as CRPS evaluation frameworks, Wasserstein distance applications, and ensemble forecast verification. Romain has contributed to open-source code repositories for his methodologies, reflecting his commitment to reproducible research.

• Computational Thinking
• Algorithmic Assessment
• Educational Technology
• +3 more

Giorgia Adorni is a Researcher with a Ph.D. at the Department of Innovative Technologies (DTI) within the University of Applied Sciences and Arts of Southern Switzerland (SUPSI). She specializes in computational thinking education, algorithmic assessment, and AI-driven educational tools. Her work focuses on developing frameworks for computational thinking problem design, multi-interface learning platforms, and intelligent tutoring systems using Bayesian networks and reinforcement learning. Affiliations: Institute of Information Systems and Networking (ISIN) Her research interests include the intersection of technology and education, particularly in measuring and enhancing algorithmic thinking skills. She contributes to projects like the Virtual CAT platform for gesture-based algorithm assessment and frameworks like FADE-CTP to guide educational activity design. Recent work explores hybrid reinforcement learning approaches to solve complex educational tasks optimally.

Emtiyaz KhanView profile

Research Professor

• Machine Learning
• Bayesian Statistics
• Deep Learning
• +9 more

Emtiyaz Khan is a Research Professor and Team Leader of the Adaptive Bayesian Intelligence team at the RIKEN Center for Advanced Intelligence Project (AIP) in Japan. His research focuses on developing AI systems that can autonomously learn to perceive, act, and reason throughout their lives, bridging the gap between how living beings and computers learn. Dr. Khan's research interests span multiple areas of machine learning including: Approximate inference and Bayesian statistics Deep learning and neural network optimization Reinforcement learning and active learning Information geometry and signal processing Online learning and continual adaptation His recent work has focused on developing theoretically grounded Bayesian methods for deep learning, with applications in computer vision and decision-making systems. Dr. Khan has made significant contributions to variational inference, Bayesian optimization, and continual learning frameworks that enable AI systems to retain knowledge while learning new tasks. Dr. Khan has received substantial research funding including: (2021-2026, USD 2.23 Million) JST-CREST and French-ANR's grant, The Bayes-Duality Project (2020-2023, USD 167,000) KAKENHI Grant-in-Aid for scientific Research (B), Life-Long Deep Learning using Bayesian Principles (2020-2023, USD 11,000) KAKENHI Grant-in-Aid for Chellenging Research (Exploratory), Linear algebra for continuous learning of large neural networks (2019-2022, USD 237,000) External funding through companies for several Bayes related projects Dr. Khan is actively involved in the machine learning community, having served as General Chair for AISTATS 2026 and given keynotes at major conferences including Bayes Comp 2025 and the 1st EurIPS conference. His work has resulted in multiple papers accepted at top-tier conferences like NeurIPS, with some receiving spotlight presentations.