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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.

• 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.

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.

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.

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.

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.

Sara GalliniView profile

Assistant Professor

• Cancer Biology
• Cellular Dynamics
• Epithelial Biology
• +3 more

Sara Gallini is an Assistant Professor at EPFL, leading the Gallini Lab within the ISREC Department of the School of Engineering (SV). Her research focuses on understanding how healthy and oncogenic cells compete in skin epithelium, aiming to identify therapeutic targets for skin cancer prevention. She holds a Tenure Track position and teaches in the Life Sciences Engineering program. Her lab employs advanced in vivo imaging and single-cell analysis techniques. Education details are not explicitly provided, but her career includes a postdoctoral fellowship with the HFSP. Her research integrates molecular, cellular, and systems-level approaches to study cancer initiation and tissue homeostasis, particularly in injury-driven dynamics. Lab Members: Includes PhD student Mustafa Öztürk and technical staff Mélanie Sipion. Key Research Themes: Oncogenic cell competition, epidermal regeneration, EGFR/ERK signaling, and therapeutic target discovery. Her lab collaborates with clinical teams and uses models like mouse skin to study tumor suppression mechanisms. Future work aims to leverage healthy cell dynamics for cancer treatment strategies. Contact: SV 2527 office, +41216936764, sara.gallini@epfl.ch .

Claudia R. BinderView profile

Professor

• Urban Sustainability
• Transition Research
• Urban Metabolism
• +5 more

Claudia R. Binder is Full Professor at EPFL's School of Architecture, Civil and Environmental Engineering, leading the Laboratory for Human-Environment Relations in Urban Systems (HERUS) since 2016. Previously, she held professorships at the University of Munich (2011-2016), University of Graz (2009-2011), and University of Zurich (2006-2009). She served as Dean of EPFL's ENAC School from 2020-2023 and holds advisory roles with Swiss federal institutions including the Mercator Foundation since 2024. Her academic foundation includes a Biochemistry degree and PhD in Environmental Sciences from ETH Zurich, followed by postdoctoral research at the University of Maryland. This interdisciplinary background underpins her research approach spanning natural and social sciences. Professor Binder's work centers on urban sustainability transitions, examining urban metabolism dynamics through systems science frameworks. She investigates energy-food-transport interdependencies in cities using transdisciplinary methods that integrate material flow analysis, spatial modeling, and socio-technical assessments. Her research particularly emphasizes regulatory mechanisms and transformation drivers in human-environment systems, with case studies across Swiss and global urban contexts. Recent publications reveal evolving focus from foundational urban metabolism studies toward actionable transition strategies. Her 2024-2025 work increasingly addresses social tipping dynamics, circular decarbonization, and spatially explicit waste management, demonstrating methodological innovation through geo-referenced material flow analysis and participatory backcasting frameworks. Key thematic clusters include energy innovation diffusion, plastic waste governance, and demand-side flexibility in residential systems. She actively mentors 7 current PhD candidates while supervising 11 graduates since 2018, with research spanning urban metabolism modeling, sustainability assessment, and transition governance. Her leadership extends to Swiss National Science Foundation committees and National Research Program 71 on energy consumption management. At EPFL, she directs the HERUS laboratory which develops the Sustainability Solution Space methodology for urban assessment. The lab operates at the intersection of data science, environmental engineering, and social theory, maintaining strong field connections in Switzerland, Indonesia, and Germany for empirical validation of transition models.

Andrea MartinelliView profile

Lecturer

• Optimal Control
• Reinforcement Learning
• Decentralized Control
• +4 more

Dr. Andrea Martinelli is a Lecturer and Postdoctoral Researcher at the Automatic Control Laboratory (IfA), ETH Zurich. He holds a PhD in Automatic Control from ETH Zurich (2024) under Prof. John Lygeros, an MSc in Control Engineering (2017) from Politecnico di Milano, and a BSc in Management Engineering (2015). His research focuses on optimal control, reinforcement learning, and decentralized control strategies for large-scale systems, emphasizing scalability and applicability to renewable energy systems. He received the ETH Medal for his doctoral thesis on data-driven control methods. Education: BSc in Management Engineering, Politecnico di Milano (2015) MSc in Control Engineering with Honours, Politechnico di Milano (2017) PhD in Automatic Control, ETH Zurich (2024) Research Interests: Optimal control and reinforcement learning Data-driven methods for control systems Decentralized control of interconnected systems Dissipativity theory and passivity-based approaches Applications in renewable energy systems (DC microgrids) Teaching & Outreach: Program Manager for the CAS ETH in Automation Teaching a post-graduate course on automation in 2025 Professional Activities: Worked at Laboratoire d'Automatique (EPFL) during MSc thesis (2017) Research Assistant with Prof. R. Scattolini, Politecnico di Milano (2018)

Amy E. FraleyView profile

Assistant Professor

• Natural Products Biosynthesis
• Biotechnology
• Biochemistry
• +6 more

Amy E. Fraley is an Assistant Professor at ETH Zürich, Department of Chemistry and Applied Biosciences, and leads the Medicinal Chemistry Research Group. Her cross-disciplinary work bridges natural products biosynthesis, pharmaceutical sciences, and environmental sustainability through enzymatic chemistry and biotechnology. BSc in Chemistry, Millersville University of Pennsylvania (2014) PhD in Medicinal Chemistry, University of Michigan College of Pharmacy (2019) Postdoctoral work at ETH Zürich Institute of Microbiology under Prof. Jörn Piel Her research focuses on harnessing biosynthetic enzymes (e.g., halogenases, monooxygenases, Diels-Alderases) to create sustainable bioactive metabolites. She explores natural product biosynthesis in fungi and marine bacteria, targeting disease mechanisms and green chemistry applications. Recent work includes metagenomic studies of Lake Chilika microbial mats and polyketide synthase engineering. Notable scientific awards include the 2024 JSP Fellow at Bürgenstock Conference, 2018 Rackham Predoctoral Fellowship, and multiple University of Michigan honors. Her group secured funding from the ETH4D Doctoral Mentorship Grant and Messerli Foundation . Collaborations span structural biology, synthetic chemistry, and microbiology, with key contributions to flavoenzyme characterization and bioactive compound libraries . The lab emphasizes interdisciplinary training and innovation in biocatalytic tools for organic synthesis.

Dennis KommView profile

Associate Professor

• Algorithm Design and Analysis
• Computer Science Education
• Theoretical Computer Science
• +3 more

Prof. Dennis Komm is an Associate Professor at ETH Zurich's Department of Computer Science, leading the group for Algorithms and Didactics. He chairs the Center for Computer Science Education (ABZ) and serves on committees such as the Swiss Maturity Board (Schweizerische Maturitätskommission) and the STEM Commission of the Swiss Academies. Previously, he held roles at RWTH Aachen University (Master's, 2008), ETH Zurich (PhD, 2012), University of Zurich (external lecturer, 2014–2020), and PH Graubünden (including department head and professor of 'Fachdidaktik Informatik'). Education: He completed a Master's in Computer Science at RWTH Aachen (2008), a PhD at ETH Zurich (2012), and studies in Information Technology at Queensland University of Technology (2006). His academic journey includes visiting roles at King's College, Stanford, and Comenius University. He has taught extensively across institutions, emphasizing Python and LOGO-based approaches for beginners. Research focuses on algorithm design, approximation algorithms, reoptimization, and advice complexity in theoretical CS. His work in education explores computational thinking, programming pedagogy (especially for K–12), and interdisciplinary approaches (e.g., robotics in math). Recent trends in his articles highlight advancements in online algorithms, optimization under dynamic conditions, and initiatives to integrate CS into Swiss school curricula sustainably. He actively promotes CS education through platforms like WebTigerPython and collaborates on projects such as CyberQuest and MINTerlink. His outreach includes organizing conferences (e.g., STIU 2025) and workshops on programming and cybersecurity for teachers and students. Despite no listed scientific awards, his contributions to education and theoretical CS are recognized through editorial roles in journals like Informatics in Education and contributions to the TigerJython Group. Grant-related advising includes co-supervising doctoral theses on robotics, USOs, and programming didactics. He advocates for equitable educational opportunities via the Passerelle exam and the Swiss Beaver Competition. His team's work spans teacher training, didactic certifications, and bridging university-school collaborations through initiatives like MINTerlink. Labs and teams: Head of ABZ (ETH's CS education center), collaborator with the Computational Robotics Lab, and part of the TigerJython Group. He also co-organizes the Colloquium on Mathematics, Computer Science, and Education with ETH's Mathematics Department.

Matthieu CordView profile

Professor

• Computer Vision
• Machine Learning
• Deep Learning
• +7 more

Matthieu Cord is a Professor at Sorbonne University and Scientific Director of valeo.ai, leading research in computer vision, deep learning, and computational cooking. He heads the MLIA team at ISIR Lab, focusing on multimodal models, transformers, and efficient architectures. Research areas include computer vision, large language models with vision, and AI-driven food analytics. Key projects: VISA-DEEP AI chair, Foundation VaViM models, and SmolVLA collaboration with Hugging Face. His recent work examines scalable multimodal models , trajectory prediction , and diffusion-based segmentation , with studies on in-context learning and biased shortcut learning in visual question answering. Articles highlight DeiT variants , fishr for OoD generalization , and STEEX for counterfactual explanations . Scientific awards include IUF Honorary Membership (2009), BMVC 2017 Best Paper, and ICIP 2018 Best Paper. As an advisor, he supervised PhD theses on topics like GAN editing , semantic segmentation , and multimodal retrieval . Current roles involve mentoring the 'Research Band' at MLIA and leading EU-funded initiatives like SCAPE. His work bridges theoretical AI exploration with practical applications in autonomous driving and food technology.

• Information Theory
• Statistics
• Machine Learning
• +1 more

Varun Jog is Professor of Information Theory and Statistics in the Department of Pure Mathematics and Mathematical Statistics (DPMMS) at the University of Cambridge, Faculty of Mathematics. Previously, he served as Assistant Professor at the University of Wisconsin-Madison (2016-2020) and at the University of Cambridge (2021-2024). His academic background includes a B.Tech. in Electrical Engineering from IIT Bombay (2010) and a Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley (2015). Professor Jog's research centers on fundamental questions at the intersection of information theory, statistics, and machine learning. He develops theoretical frameworks for statistical inference under constraints such as limited communication and privacy requirements, with significant contributions to hypothesis testing, differential privacy, adversarial risk analysis, and information-theoretic inequalities. His work bridges abstract mathematical principles with practical applications in data science and robust machine learning. Recent publications demonstrate a concentrated focus on distributed inference systems, particularly examining sample complexity limits in hypothesis testing under information constraints and privacy-preserving mechanisms. His research consistently reveals deep connections between information theory and statistical learning, with increasing emphasis on adversarial robustness and foundational inequalities. His scientific contributions have earned recognition through prestigious awards: NSF-CAREER Award (2020) R. Narasimhan Memorial Lecture Award (2020) Eli Jury Award from UC Berkeley EECS Department (2015) Jack Keil Wolf student paper award at ISIT (2015) Professor Jog maintains an active research group, currently supervising one PhD student while having graduated four PhD students and four Master's students. His mentorship extends to postdoctoral researchers including Amir Asadi, Deepanshu Vasal, and Andre Wibisono. Research funding includes the competitive NSF-CAREER grant. He co-organizes the Cambridge Information Theory Seminar, fostering academic exchange and collaboration within the theoretical research community.

Cooper HarshbargerView profile

Lecturer

• Biomechanics
• Medical Technology
• Acoustofluidics
• +3 more

Dr. Cooper Harshbarger is a Lecturer at the Department of Health Sciences and Technology at ETH Zurich , Switzerland. His research bridges biomechanics and acoustofluidics, focusing on spinal surgery and microscale cell manipulation technologies. Email: cooper.harshbarger@hest.ethz.ch Research Interests : Dr. Harshbarger specializes in biomechanical analysis of spinal structures and acoustofluidic device development . His work explores: Biomechanics of the lumbar spine and osteoligamentous complexes Acoustically-driven microfluidic systems for medical diagnostics Cell focusing/trapping technologies using sharp-edge acoustofluidics Scientific Contributions : Recent publications highlight his dual expertise in spinal fusion biomechanics and microscale fluid control , with applications in cancer diagnostics and cell manipulation. Key technologies include BAW-based systems and programmable acoustofluidic chips.