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Robert WestView profile

Associate Professor

• Natural Language Processing (NLP)
• Machine Learning
• Computational Social Science
• +4 more

Robert West is an Associate Professor at EPFL (École polytechnique fédérale de Lausanne) in the School of Computer and Communication Sciences , leading the Data Science Lab (dlab) . His research focuses on Natural Language Processing , Machine Learning , and Computational Social Science , analyzing human-generated data from the web, social media, and online platforms. Education : PhD in Computer Science (2016) - Stanford University MSc in Computer Science (2010) - McGill University BSc in Computer Science (2007) - Technische Universität München Research Interests : West develops algorithms for analyzing large-scale web data, with emphasis on multilingual NLP , social network analysis , and AI ethics . His work bridges machine learning with social science to understand digital human behavior. Scientific Awards : ICWSM’22 Adamic–Glance Distinguished Young Researcher Award Google Faculty Research Award Facebook Research Award Multiple Outstanding Paper Awards at ICWSM and WWW Advising & Grants : He advises 12 PhD students and has secured funding from the Swiss National Science Foundation , Swiss Data Science Center , and industry partners. His lab maintains collaborations with Microsoft Research and CROSS . Labs & Collaborations : West leads the Data Science Lab at EPFL, which focuses on web-scale data analysis , privacy-preserving machine learning , and AI for social good . The lab develops tools like Wikispeedia and Quotebank for public data exploration.

Stanislav SmirnovView profile

Professor

• Mathematical Physics
• Statistical Physics
• Probability Theory
• +4 more

Stanislav Smirnov is a Professor of Mathematics at the University of Geneva, where he has been a faculty member since 2003. He also holds a part-time position at the Chebyshev Laboratory of St. Petersburg State University. As Director of the National Centre of Competence in Research SwissMAP since 2014, he leads a major research initiative funded by the Swiss National Science Foundation. His education includes a mathematics degree from St. Petersburg State University (1992) and a PhD from the California Institute of Technology (1996). Prior to joining Geneva, he held positions at Yale University, the Institute for Advanced Study in Princeton, the Max Planck Institute in Bonn, and the Royal Institute of Technology in Stockholm. Professor Smirnov's research focuses on the mathematical foundations of statistical physics, with particular emphasis on conformal invariance in two-dimensional lattice models. His work has revolutionized our understanding of critical phenomena through rigorous mathematical approaches, establishing deep connections between probability theory, complex analysis, and statistical mechanics. He develops novel discrete complex analysis techniques to prove continuum limits of lattice models at criticality. His publication record shows consistent advancement in understanding random planar geometry, percolation theory, and the Ising model, with increasing attention to Liouville quantum gravity in recent years. His work bridges deep mathematical analysis with physical insights, often providing rigorous proofs for phenomena previously understood only through physical intuition. Fields Medal (2010) for the proof of conformal invariance of percolation and the planar Ising model in statistical physics As an advisor, Professor Smirnov has mentored doctoral students including Ekin Arikök and Nikolai Bobenko. He leads the ANALYSE, PHYSIQUE MATHÉMATIQUE ET PROBABILITÉS research group at the University of Geneva. His research is supported by major grants including the NCCR SwissMAP, European Research Council's AG COMPASP, and Swiss National Science Foundation grants 200020_143434 and 200020_141329. Professor Smirnov has organized numerous influential conferences, including the "Conformal maps from probability to physics" workshop at Monte Verità (2010) and multiple "Random Planar Geometry" meetings in Les Diablerets, fostering collaboration between mathematicians and physicists working on critical phenomena and random geometry.

• Mathematics of Data
• Numerical Linear Algebra
• Randomized Algorithms
• +5 more

Liza Rebrova is an Assistant Professor in the Department of Operations Research and Financial Engineering (ORFE) at Princeton University's School of Engineering and Applied Science. She is also an associated faculty member of the Program in Applied and Computational Mathematics (PACM) and the Center for Statistics and Machine Learning (CSML). Her research focuses on randomized numerical linear algebra and the mathematics of data science, with connections to high-dimensional probability and stochastic optimization. She develops mathematically justified randomized algorithms for large-scale data, particularly interested in settings where data exhibits mathematical structure such as spectral decay, multi-modality, or non-negativity. Her work addresses challenges in data compression, recovery, and optimization under structured noise conditions. Dr. Rebrova's recent publications show a strong trend toward developing robust algorithms for linear systems, tensor data compression, and nonnegative matrix factorization. Her work bridges theoretical foundations with practical applications in machine learning and data science, with emphasis on memory efficiency and handling corrupted or incomplete data. NSF DMS-2309685 (Single PI, 2024-2026): "Outliers are not what they seem: data-aware, flexible, and robust randomized iterative methods" NSF DMS-2108479 (Collaborative, 2022-2024): "Fast, Low-Memory Embeddings for Tensor Data with Applications" (co-PIs Mark Iwen and Deanna Needell) She currently advises three PhD students (Jackie Lok, Shambhavi Suryanarayanan, and Sofiia Shvaiko) and has previously advised Abraar Chaudhry (PhD 2024) and Nicolo Grometto (Masters thesis 2023). At Princeton, she teaches graduate courses including ORF526 (Graduate Probability), ORF387 (Networks), and ORF523 (Convex and Conic Optimization).

• Distributed Computing
• Wireless Networks
• Distributed Systems
• +4 more

Prof. Roger Wattenhofer is a Full Professor at the Department of Information Technology and Electrical Engineering, ETH Zurich, Switzerland, and Deputy head of the Computer Engineering and Networks Lab. He holds a doctorate in Computer Science from ETH Zurich (1998) and has held positions at Brown University and Microsoft Research before returning to ETH. His research focuses on distributed computing, wireless networks, and algorithmic systems design, with contributions to Byzantine agreement protocols, blockchain technologies, and neural network architectures. He teaches courses such as Distributed Systems and Computational Thinking. Education: Ph.D. in Computer Science (ETH Zurich, 1998). Research interests include distributed systems, network algorithms, and the intersection of machine learning with distributed computing. His work spans both theoretical foundations and practical implementations, addressing challenges in fault tolerance, consensus mechanisms, and algorithmic efficiency. Recent publications explore topics like adversarial robustness in voting systems, privacy in reinforcement learning, and generative music models. He actively contributes to open-source frameworks and benchmarks for neural algorithmic reasoning.

Annina IseliView profile

Lecturer

• Fractal Geometry
• Complex Dynamics
• Geometric Measure Theory
• +3 more

Annina Iseli is a Lecturer in the Department of Mathematics at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the College of Basic Sciences. She holds dual roles as both a Scientist and Lecturer, contributing to research and teaching within the Institute of Mathematics (MATH-GE). Her work bridges pure and applied mathematics, focusing on geometric and analytical properties of fractals. University: École Polytechnique Fédérale de Lausanne (EPFL) School: College of Basic Sciences Department: Institute of Mathematics (MATH-GE) Role: Lecturer and Scientist Research interests span multiple areas in mathematics: Fractal geometry and its connections to complex dynamics Geometric measure theory applications to projections in normed spaces Conformal and quasiconformal geometry Analysis in metric spaces Her publications include work on projection theorems, fractal dimensions, and hyperbolic space properties. She co-organizes the Quasiworld Seminar and EPFL Geometry Seminar, collaborating with researchers like Mario Bonk, Nicolas Monod, and Zoltán Balogh. Contact: annina.iseli@epfl.ch

Malte OppermannView profile

Professor

• Ultrafast spectroscopy
• Chiral dynamics
• Molecular dynamics
• +4 more

Prof. Dr. Malte Oppermann is a full Professor at the University of Basel , leading the Ultrafast Chiral Dynamics Laboratory within the Department of Chemistry. His group specializes in developing cutting-edge time-resolved spectroscopic techniques to probe molecular transformations on femtosecond to microsecond timescales, focusing on chirality and structural dynamics in complex systems. Research Focus: Ultrafast Chiral Spectroscopy : Using circularly polarized laser pulses to resolve structural changes in chiral systems. Molecular Motors & Protein Dynamics : Capturing conformational changes in synthetic motors and proteins in native environments. Photoactive Materials : Investigating light-energy conversion mechanisms in chiral photochemical systems. His research bridges physics and chemistry, employing state-of-the-art laser technology and collaborating internationally across synthesis, spectroscopy, and theory. Publications & Impact: Prof. Oppermann’s work (2011–2025) spans ultrafast spectroscopy , chiral dynamics , nanomaterials , and biomolecular photophysics . Key themes include spin-crossover dynamics, DNA photodamage, and plasmonic nanoparticles, with techniques like transient X-ray absorption and deep-UV circular dichroism. Team & Opportunities: The group actively recruits MSc/PhD students and postdocs in physics, chemistry, and materials science. Interested candidates are encouraged to contact Prof. Oppermann directly.

Michael MoorView profile

Assistant Professor

• Medical AI
• Machine Learning in Healthcare
• Causal Learning
• +3 more

Prof. Michael Moor is a tenure-track Assistant Professor for Medical AI at ETH Zurich's Department of Biosystems Science and Engineering in Basel. Previously, he conducted postdoctoral research at Stanford University under Prof. Jure Leskovec, focusing on medical foundation models. His work spans causal learning, multimodal AI, and sepsis prediction. Moor holds an MD from the University of Basel and a PhD from ETH Zurich's Machine Learning and Computational Biology Lab under Prof. Karsten Borgwardt. Research Interests: Generalist medical AI models Multimodal medical reasoning Zero-shot and few-shot learning Clinical causal inference Retrieval-augmented language models Sepsis prediction systems Key Achievements: Published foundational work in Nature (2023) on generalist medical AI Developed Med-Flamingo multimodal model (2023) Zero-shot causal learning framework accepted to NeurIPS 2023 (Spotlight) International sepsis prediction study with 156k ICU patients (2023) Lab Activities: Leads ETH's Medical Foundation Models group, collaborating with Stanford HAI and NASA Ames. Active in creating medical AI benchmarks like AgentClinic and developing retrieval-augmented systems like Almanac.

Afonso S. BandeiraView profile

Professor

• High Dimensional Probability
• Random Matrices
• Mathematical Statistics
• +3 more

Afonso S. Bandeira is a Professor in the Department of Mathematics (D-MATH) at ETH Zurich, where he conducts research at the intersection of mathematics, statistics, and computer science. He maintains strong affiliations with several interdisciplinary research centers including the Institute For Operations Research (IFOR), the Max Planck ETH Center for Learning Systems, the ETH Foundations of Data Science, and the ETH AI Center, with a courtesy appointment at D-ITET. Bandeira actively teaches courses including Mathematics of Signals, Networks, and Learning, and Mathematics of Data Science. His research focuses on High Dimensional Probability, Random Matrices, Mathematical Statistics, Theoretical Computer Science, Combinatorics, and Mathematical Optimization. Bandeira's work often explores the theoretical foundations of data science, examining phase transitions in statistical problems, computational barriers, and the geometry of high-dimensional spaces. He maintains a research group blog called Randomstrasse101 that emphasizes open problems in his field. Analysis of his recent publications reveals a strong focus on matrix and tensor concentration inequalities, synchronization problems, nonconvex optimization landscapes, and computational-statistical tradeoffs in high-dimensional inference. His work bridges theoretical mathematics with practical applications in machine learning and data analysis, particularly examining where computational limitations arise in statistical problems. Bandeira actively mentors students and researchers, currently supervising several doctoral candidates including Daniil Dmitriev, Konstantin Donhauser, Anastasia Kireeva, Kevin Lucca, Chiara Meroni, Gil Kur, Petar Nizic-Nikolac, and Almut Roedder. He emphasizes that students working with him should participate in the DACO seminar and group meetings, and encourages prospective students to have completed his Mathematics of Signals, Networks, and Learning or Mathematics of Data Science courses. He leads a research group focused on the mathematics of data science, with regular group meetings and seminars. Bandeira has developed comprehensive lecture notes including 'A Tour Through the Mathematics of Signals, Learning, and Networks' (2025) and 'Mathematics of Machine Learning' (2021), and previously authored 'Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science' (2015).

Thomas MichaelsView profile

Assistant Professor

• Soft Matter Physics
• Living Matter Physics
• Protein Aggregation
• +5 more

Thomas Michaels is an Assistant Professor at the Department of Biology, ETH Zürich, leading the Michaels Group . His research focuses on theoretical models of biomolecular condensates and protein aggregation in biological systems. Research Themes : Protein aggregation, liquid-liquid phase separation, membrane biophysics, and the role of condensates in neurodegenerative diseases like Alzheimer’s and Parkinson’s. Collaborative Approach : Integrates theoretical physics, control theory, and computational biology with experimental validation to design therapeutic strategies. Recent Publications highlight his work on amyloid formation mechanisms, lipid interactions, and phase-separated compartments as biochemical reactors. His group trains PhD students in systems biology and biocondensate physics.

Robert HuberView profile

Lecturer

• Sustainable Agriculture
• Agricultural Policy
• Ecosystem Services
• +4 more

Dr. Robert Huber serves as a Lecturer at the Department of Environmental Systems Science at ETH Zürich, specializing in Agricultural Economics and Policy. His research focuses on the integration of interdisciplinary knowledge to analyze and evaluate management and policy options that promote sustainable development in the agricultural sector. Institution: ETH Zürich Department: Agricultural Economics and Policy Research Focus: Sustainable Agriculture and Policy Methodologies: Bio-economic and agent-based modeling Dr. Huber's research explores how multifunctional agricultural sectors can provide essential non-market goods and services while maintaining their primary economic function of food production. His work applies bio-economic and agent-based modeling techniques to understand farmers' decision-making processes regarding ecosystem services provision, coupled with economic valuation techniques to analyze societal demand for these services. His research aims to identify effective and efficient policy measures that align supply and demand for ecosystem services at landscape scale. His recent publications demonstrate strong expertise in agricultural policy analysis, climate change mitigation strategies, sustainable farming practices, and innovative modeling approaches. His work spans topics from herbicide-free agriculture using remote sensing technologies to biodiversity enhancement in agricultural landscapes and the cost-effectiveness of different climate change mitigation policies at various scales. Remote sensing applications in sustainable agriculture Behavioral aspects of farmer decision-making Policy evaluation methodologies Climate change adaptation in farming systems Economic valuation of ecosystem services Digitalization impacts on agricultural policy Dr. Huber has published extensively in high-impact journals including Journal of Agricultural Economics, People and Nature, and Q Open. His collaborative research often involves interdisciplinary teams addressing complex agricultural sustainability challenges. His publications demonstrate a consistent focus on practical policy implications derived from rigorous empirical analysis.

Eleni ChatziView profile

Professor

• Structural Health Monitoring
• Nonlinear System Identification
• Damage Detection
• +7 more

Prof. Eleni Chatzi is a Full Professor and Chair of Structural Mechanics at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering. She holds a PhD from Columbia University (2010) and has held roles from Assistant to Full Professor at ETH since 2010. Her research focuses on intelligent structural monitoring and data-driven asset management, emphasizing nonlinear dynamics and sensor integration. Affiliations : Institute of Structural Engineering, European Academy of Wind Energy (EAWE President), Swiss Community for Computational Methods (SWICCOMAS Chair) Research interests include Structural Health Monitoring (SHM), system identification, and advanced simulation tools. She pioneered work on data-driven diagnostics and self-aware infrastructure, supported by grants like the ERC Starting Grant (2015). Awards include the 2020 Walter L. Huber Prize and 2024 SHM Person of the Year Award. Her work spans wind energy infrastructure, metamaterials for vibration control, and AI-driven structural analytics. Over 600 publications and 200k+ citations highlight her impact. She teaches computational science and structural dynamics in ETH's programs and collaborates globally on sustainable infrastructure projects.

• Environmental Science
• Sustainability
• Agroecology
• +3 more

Dr. Federico Cammelli is a Researcher at the Department of Environmental Systems Science (ETH Zurich) within the Professorship for Sustainable Agroecosystems. His work focuses on environmental sustainability, deforestation mitigation, and equitable policy design in tropical regions. He investigates the effectiveness of supply chain policies, fire management strategies, and smallholder livelihood challenges in regions like the Amazon, West Africa, and Indonesia. Key research areas include zero-deforestation commitments in cattle and soy sectors, cocoa sustainability in West Africa, and the interplay between fire use, poverty, and conservation. He employs experimental methods and interdisciplinary approaches to address complex environmental governance challenges. Based at ETH Zurich’s Sonneggstrasse 33 campus, Dr. Cammelli collaborates with international institutions to advance sustainable agroecosystems and climate-resilient development. His research bridges environmental science, policy analysis, and socioeconomic dimensions of conservation.

Elliott AshView profile

Associate Professor

• Law and Economics
• Political Economy
• Econometrics
• +5 more

Elliott Ash is an Associate Professor of Law, Economics, and Data Science at ETH Zurich's Center for Law & Economics. He holds a Ph.D. in Economics and J.D. from Columbia University, a B.A. in Economics, Government, and Philosophy from the University of Texas at Austin, and an LL.M. in International Criminal Law from the University of Amsterdam. His research focuses on empirical legal studies using econometrics, NLP, and ML, examining topics like judicial behavior, legislative impact, and AI-driven governance. He has been funded by the ERC, Swiss NSF, and others. Research Interests: Elliott explores automation of legal decisions, text-as-data analysis in law, and the intersection of AI with legal systems. He develops tools like BallotBot and LePaRD to enhance legal transparency and public understanding. His work bridges law, economics, and computer science, with publications in top journals like the American Economic Journal and Review of Economics and Statistics . Teaching: Courses include Building a Robot Judge , Natural Language Processing for Law , and Big Data for Public Policy . He co-organizes the Zurich Workshop in AI+Economics and Monash-Warwick-Zurich Text-as-Data Workshops. Awards: European Research Council Starting Grant, Swiss National Science Foundation Grant, and multiple grants from U.S. and Swiss institutions. His work has been featured in NPR , VoxEU , and Georgetown Law Journal . Labs/Teams: Leads the Swiss AI Initiative's Human-AI Alignment team, collaborates with the CEPR on Political Economy research, and serves as an Economic Journal Associate Editor.

Alessandro ChiesaView profile

Associate Professor

• Cryptography
• Complexity Theory
• Computer Security
• +3 more

Alessandro Chiesa is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences, holding positions across multiple departments including Computer Security (COMPSEC), SSC, SIN, and EDIC. His office is located at BC 245 in Building BC, Station 14, Lausanne, Switzerland. Chiesa's research spans cryptography, complexity theory, and computer security, with particular focus on the theoretical foundations and practical implementations of cryptographic proofs that are short and easy to verify. His work bridges theoretical computer science with real-world applications, especially in blockchain technology and privacy-preserving systems. He has made significant contributions to zero-knowledge proof systems, including zkSNARKs, and their applications in cryptocurrency. His publications reveal a strong focus on cryptographic protocols, with particular emphasis on privacy-preserving technologies. The analysis of his articles shows consistent work in applying mathematical foundations to practical security problems, especially in the domain of blockchain and digital currencies. His research demonstrates expertise in both theoretical aspects of cryptography and their implementation in real systems. IEEE SP Test-of-Time Award (2024) Sloan Research Fellowship (2021) Okawa Foundation Research Grant (2020) Google Faculty Research Awards (2018 and 2017) MIT Technology Review's 35 Innovators Under 35 list (2018) Chiesa has supervised multiple PhD students including Di Zijing, Fenzi Giacomo, Guan Ziyi, Knabenhans Christian Louis, Manzur Ignacio, Weissenberg Guy, and Zheng Yuxi. His teaching includes Algorithms I, Foundations of Probabilistic Proofs, and Lattice-based Cryptography. He co-founded Zcash and StarkWare Industries, translating his theoretical research into practical applications that have significantly impacted the cryptocurrency space.

Olga FinkView profile

Assistant Professor

• Machine Learning
• Predictive Maintenance
• Industrial IoT
• +3 more

Olga Fink is a Tenure Track Assistant Professor at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Department of Intelligent Maintenance and Operations Systems (IMOS) within the School of Architecture, Civil and Environmental Engineering (ENAC). She also holds roles in PhD program committees for Civil and Environmental Engineering (EDCE) and Robotics, Control, and Intelligent Systems (EDRS). Her research focuses on machine learning for infrastructure monitoring, predictive maintenance, and physics-informed AI models. She teaches courses on machine learning, data science for infrastructure, and advanced deep learning topics. Fink advises multiple PhD students and is involved in interdisciplinary projects such as ThermoNeRF (multimodal 3D thermal modeling) and physics-informed neural networks for fault diagnostics. Her work bridges AI and engineering with applications in smart infrastructure, energy systems, and industrial IoT. Education: PhD in Engineering (inferred from role) Affiliations: IMOS Lab, ENAC-SGC, EPFL PhD Committees (EDCE, EDRS) Key Research Themes: Explainable AI, Digital Twins, Structural Health Monitoring, Domain Adaptation Her publications (2023–2025) emphasize robust AI for industrial systems, including fault detection in high-voltage equipment, multimodal data fusion, and physics-consistent models. She collaborates on EU and industry-funded projects, focusing on real-world applications like predictive maintenance and energy efficiency.