Manuel Kaufmann is a Lecturer in the Department of Computer Science at ETH Zürich. His work focuses on advanced 3D human motion capture, sensor-based systems, and computer vision applications. He is affiliated with the Institute of Informatics (inf.ethz.ch) and contributes to research in real-time motion tracking, dataset development, and machine learning integration for human-robot interaction. Research interests include holistic human-scene reconstruction from monocular videos, gaze estimation using EEG signals, and expressive avatar creation. His projects emphasize practical applications in robotics, sports analytics, and biomedical engineering, often leveraging electromagnetic and inertial sensors for high-precision data acquisition. His publications reflect a trend toward multi-modal data fusion, real-world dataset creation (e.g., WorldPose, ARCTIC), and addressing challenges in loose garment modeling (Reloo). These efforts aim to improve markerless motion capture, crowd analysis, and human-robot collaboration. No scientific awards or grants are explicitly listed. He has no documented advisees, though his research may involve collaborations with students or teams. His office is located at OAT X 23, Andreasstrasse 5, Zürich, Switzerland, and contact details include a phone number and professional email.
Professor Stefan Goedecker is a distinguished faculty member in the Department of Physics at the University of Basel, Faculty of Science. He holds the position of Professor of Computational Physics and leads an active research group focused on developing advanced computational methods for materials science and quantum physics. Dr. Goedecker received his physics education at the Technical University Munich and the College of William and Mary, followed by a Ph.D. from EPFL Lausanne. His postdoctoral training included positions at Cornell University and the Max-Planck Institute in Stuttgart. In 2003, he was appointed Professor of Computational Physics at the University of Basel, where he has established himself as a leading researcher in computational methods development. His research interests center on computational physics with emphasis on electronic structure calculations, atomistic simulations, and the development of novel algorithms for materials science applications. His work has strong interdisciplinary connections spanning physics, mathematics, material sciences, chemistry, and computer science. Current research directions include machine learning applications in catalysis, fourth-generation neural network potentials for molecular chemistry, and methods for quantifying material synthesizability. Analysis of his recent publications reveals a strong focus on advancing computational methods for electronic structure calculations, with particular emphasis on machine learning potentials, molecular dynamics optimization, and accurate modeling of material properties. His work bridges theoretical physics with practical applications in materials science and nanotechnology, with increasing integration of artificial intelligence techniques into traditional computational physics frameworks. Machine learning for Catalysis (Ongoing) Fourth-Generation Neural Network Potentials for Molecular Chemistry (Completed) Towards Quantifying the Synthesizability of Materials (Completed) Professor Goedecker's research group operates within the Department of Physics at the University of Basel, which is part of the NCCR SPIN initiative focused on silicon-based quantum computing development. The department hosts over 20 research groups with more than 180 teaching staff members, creating a vibrant research environment for computational physics and quantum technologies.
Siyu Tang is an Assistant Professor in the Department of Computer Science at ETH Zürich, where she leads the Computer Vision and Learning Group (VLG) at the Institute of Visual Computing. Her research focuses on computational models for human perception and digitalization through computer vision and machine learning. Her educational background includes: PhD in Computer Science, Max Planck Institute for Informatics (2017), supervised by Prof. Bernt Schiele Master of Science in Media Informatics, RWTH Aachen University Bachelor of Science in Computer Science, Zhejiang University, China Dr. Tang specializes in human-centric computer vision, developing statistical models for motion analysis, pose estimation, and digital human creation. Her work integrates machine learning with optimization techniques to enable machines to interpret human activities from visual data, with applications spanning virtual reality, healthcare, and human-computer interaction. Key research thrusts include generative models for content creation, egocentric vision, and human motion synthesis. Her recent publications (2024-2025) demonstrate intense focus on 3D human modeling and neural rendering, with Gaussian splatting emerging as a dominant technique for efficient avatar creation and scene reconstruction. Significant themes include text-driven motion synthesis using diffusion models, relightable avatars, surgical training applications, and egocentric multimodal pretraining. This work bridges computer vision, graphics, and machine learning to advance human digitalization. No scientific awards were mentioned in the provided text. Dr. Tang leads the VLG research group at ETH Zürich, mentoring PhD and Master's students in human-centric AI. She previously secured an early career research grant from the Max Planck Institute for Intelligent Systems to establish her independent research program. Her group actively pursues funding for projects in human motion analysis, 3D reconstruction, and generative modeling, with strong industry and clinical collaborations. The Computer Vision and Learning Group (VLG) operates within ETH's Institute of Visual Computing, maintaining dedicated facilities for motion capture, 3D scanning, and high-performance computing. The team collaborates internationally with institutions like the Max Planck Society and focuses on scalable solutions for real-world human digitalization challenges, including surgical training systems and immersive virtual environments.
Vincent Dufour-Décieux is a researcher at the Professorship for Energy and Process Systems Engineering at ETH Zürich , focusing on developing computational methods for material screening in separation processes and global net-zero transitions. He earned his Master's in Materials Chemistry from Ecole Polytechnique (France) and a PhD in Materials Science from Stanford University , where he pioneered statistical methods combining Kinetic Monte Carlo and random graph theory to study planetary diamond formation. Research Highlights: Application of Classical Density Functional Theory (cDFT) for 100x faster adsorption property predictions in porous materials Development of science-based definitions for "hard-to-abate" emissions to guide climate action prioritization Integration of Coulombic interactions in cDFT for CO2 adsorption accuracy Article Trends : His work spans computational materials science (cDFT, random graph theory) and climate policy analysis, with recent publications in Joule , AIChE Journal , and Physical Review E . These studies emphasize scalable solutions for carbon capture, material screening efficiency, and accurate thermodynamic modeling. Collaborations : Active in international conferences (FOA15, MolMod, Gordon Research Conference) and cross-institutional projects with teams at Stanford, ETH Zürich, and industry partners.
Sai Praneeth Karimireddy is an Assistant Professor in the Thomas Lord Department of Computer Science at the University of Southern California (USC), with a courtesy appointment in the Ming Hsieh Department of Electrical and Computer Engineering. He previously held an SNSF postdoctoral fellowship at UC Berkeley under Michael I. Jordan and earned his PhD at EPFL advised by Martin Jaggi. He co-leads the Federated Learning and Data Quality working group at MONAI (NVIDIA) and collaborates with researchers at Apple Research. His research lies at the intersection of optimization, machine learning, statistics, and economics, with a strong focus on federated learning, privacy-preserving machine learning, data valuation, and AI for healthcare. He investigates how data quality, privacy, and incentives shape collaborative ML systems, especially in high-stakes domains like medicine. His work has been deployed at companies such as Meta, Google, OpenAI, and Owkin. His recent publications span top-tier venues including NeurIPS, ICML, ICLR, and JMLR, with influential contributions such as the SCAFFOLD algorithm for federated learning. His research shows a consistent trend toward building robust, private, and incentive-compatible collaborative learning systems, with increasing emphasis on real-world deployment in healthcare and decentralized data markets. 2023 SNSF Mobility Fellowship 2022 Patrick Denantes Memorial Prize for best thesis in computer science 2022 EPFL thesis distinction (top 8%) 2021 Chorafas Foundation Prize for exceptional applied research Capitol One Fellow (2025) He is actively mentoring PhD students and leads a research group focused on foundational and applied challenges in federated and privacy-preserving ML. He teaches graduate courses at USC, including CSCI 599 on Optimization for Machine Learning and CSCI 699 on Privacy-Preserving Machine Learning. He serves as an area chair for ICLR 2025 and co-organizes major workshops on incentives in data sharing and federated learning. His lab collaborates with institutions like NVIDIA, Apple, and Argonne National Laboratory, and he is building a research program centered on sustainable, equitable, and trustworthy AI ecosystems.
Sangwoo Kim is a Tenure Track Assistant Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) in the Institute of Mechanical Engineering. He leads the Mechanics of Soft and Biological Matter Laboratory (MESOBIO), focusing on the interplay between mechanics, physics, and biology in living systems. His research spans soft matter physics, developmental biology, and mechanical engineering. 2023–Present: Tenure Track Assistant Professor, EPFL School of Engineering Postdoctoral Fellow, UC Santa Barbara Mechanical Engineering Ph.D. in Theoretical and Applied Mechanics, University of Illinois at Urbana-Champaign Kim’s research investigates fundamental properties of biological and soft materials, including: Tissue morphogenesis and embryonic development Mechanical behavior of amorphous and active matter Non-equilibrium dynamics in cellular systems Phase transitions in biological tissues Stress and osmotic pressure quantification His recent publications reveal a focus on: Biological jamming and fluidization Zebrafish axis elongation mechanics Energy landscapes of cellular matter Active matter modeling Statistical mechanics of soft materials Developmental force transmission Kim supervises PhD students and teaches courses in structural mechanics at EPFL, emphasizing problem-solving in engineering design.
Martin Rajman is a Senior Scientist at École Polytechnique Fédérale de Lausanne (EPFL) with multiple affiliations across the institution. He holds positions in the School of Computer and Communication Sciences (SIN - Teaching, SCI IC MR Group, SSC - Teaching) as well as in the Vice Presidency for Strategic Development (VPS Artificial Intelligence) and the Vice Presidency for Academic Affairs (SNAI Administration). He serves as the Executive Director of Nano-tera.ch, a large Swiss Research Program funding collaborative multi-disciplinary projects in Health and the Environment. Rajman's research spans the intersection of artificial intelligence, natural language processing, and information retrieval. His work demonstrates a consistent focus on developing practical applications of computational linguistics and machine learning techniques. Early in his career, he contributed significantly to syntactic parsing, stochastic language models, and vector space representations for text. More recently, his research has expanded into deep learning applications for 3D reconstruction, empathetic conversational agents, and distributed analytics systems. His publications reveal a trajectory from foundational NLP research toward increasingly applied and interdisciplinary work connecting AI with healthcare, environmental monitoring, and human-computer interaction. Analysis of his recent publications (2015-2024) shows a clear evolution toward more applied AI research with strong interdisciplinary connections. While maintaining his core expertise in natural language processing and information retrieval, his work has expanded into computer vision, healthcare applications, and sustainable computing. The publications demonstrate increasing collaboration across disciplines, with applications in medical imaging, mental health support systems, environmental monitoring, and human-centered AI. His leadership role in the Nano-tera.ch program reflects this interdisciplinary approach, connecting computing research with real-world challenges in health and environmental contexts. Rajman has mentored several PhD students including Ailomaa Marita, Eckard Emmanuel, Melichar Miroslav, and Veselý Martin. His research has been supported through the Nano-tera.ch program, which has funded more than 100 research projects with over 95 million CHF in public funding. He has also managed more than 20 European projects during his tenure as Director of the EPFL Global Computing Center. As Executive Director of Nano-tera.ch, Rajman leads a significant research initiative connecting EPFL with national and international partners. His work bridges academic research with industry applications, notably through collaborations with eBay on product ranking technology and with Elsevier on article recommendation systems. His leadership extends to managing large-scale research programs while maintaining an active research agenda and mentoring the next generation of computer scientists.
Giorgia Ramponi is an Assistant Professor with Tenure Track at the Faculty of Business, Economics and Informatics at the University of Zurich. She is also an affiliated professor at the ETH AI Center and the Data Science and AI, Computer Science and Engineering department at Chalmers University of Technology. Her educational background includes a Ph.D. in Information Technology from Politecnico di Milano (completed June 2021 with honors), advised by Marcello Restelli, and a Master of Science in Computer Science with Honours Programme (110/110 cum laude) from la Sapienza (July 2017), advised by Flavio Chierichetti and Alessandro Panconesi. Dr. Ramponi's research focuses on machine learning and mathematical modeling, with particular emphasis on reinforcement learning and multiagent learning. Her work bridges theoretical foundations with practical applications, exploring how learning algorithms can make optimal decisions in complex environments. She has made significant contributions to areas including inverse reinforcement learning, multi-agent systems, constrained Markov decision processes, and human-AI interaction through preference learning. Her recent publications demonstrate a strong trend toward addressing fundamental challenges in reinforcement learning, particularly in multi-agent settings, constrained optimization, and learning from human feedback. Her work combines theoretical rigor with practical applications across robotics, economics, and decision-making systems. Hassler Research Grant for "Unified Feedback Integration Framework for Reinforcement Learning" Dr. Ramponi actively contributes to the academic community through conference participation, invited lectures (including at the Mediterranean Machine Learning Summer School), and teaching. She designed and taught the "Data Science and Machine Learning" course for the ETH-Ashesi Master program. She is also a member of the ELLIS community, which connects excellence in AI research across Europe. Her research group focuses on developing frameworks for reinforcement learning with various feedback types, including preferences, rewards, and demonstrations. The group aims to advance the theoretical understanding of learning algorithms while addressing practical challenges in real-world applications.
Giacomo De Giorgi is Full Professor of Economics at the Institute of Economics and Econometrics, Geneva School of Economics and Management (GSEM), University of Geneva, and holds visiting appointments at UC Irvine. An alumnus of University College London (Ph.D.), he previously served as assistant professor at Stanford University (2006–2013), visiting professor at UC Berkeley and Columbia University, research professor at ICREA-MOVE/Barcelona GSE, and senior economist at the Federal Reserve Bank of New York (2014–2016). He is an associate editor of the Journal of the European Economic Association and a research fellow of CEPR and BREAD. Education Ph.D. in Economics, University College London Research Interests De Giorgi’s research integrates applied micro-econometrics with policy-relevant questions in development, labor, and household finance. His work explores how social networks shape economic decisions, the distributional impacts of cash and in-kind transfer programs, lifecycle inequality across racial and migrant groups, and the determinants of micro-enterprise formalization. Methodologically, he leverages large-scale randomized controlled trials, quasi-experimental designs, and cutting-edge network econometrics. Recent projects investigate: • Consumption and inequality : quantifying racial gaps in income and consumption dynamics over the lifecycle; • Financial inclusion : analyzing credit market imperfections, subprime borrowing, and the effects of business literacy interventions; • Migration and integration : comparing economic trajectories of migrants and natives in Denmark and the impact of refugee inflows on child poverty. Publications & Impact With more than 25 peer-reviewed articles in top journals—including the American Economic Review , Review of Economic Studies , Economic Journal , and Journal of Public Economics —De Giorgi’s scholarship has advanced understanding of social spillovers, program evaluation, and macro-financial linkages. His 2020 Review of Economic Studies paper on “Consumption Networks Effects” (with Frederiksen & Pistaferri) has become a benchmark for identifying neighborhood and family influences on household spending. Scientific Awards 2020 Banamex Prize for the best article published in American Economic Journal: Economic Policy Seminar & Grant Activity De Giorgi co-founded and co-organizes the Virtual Development Economics Seminar (VDEV), a joint initiative of BREAD, CEPR, and VDEV that hosts weekly online seminars reaching a global audience. He has delivered over 150 invited talks at institutions such as the World Bank, NBER, LACEA, and universities across five continents. His research has been supported by grants from the World Bank, CEPR, and private foundations. Teaching & Programs At Geneva, he teaches Development Economics, Labor Economics, and Advanced Econometrics at both graduate and executive levels. He co-founded the IEE Honors’ Program, an innovative selective track that immerses top undergraduate students in frontier research and policy engagement.
Katharine G. Abraham is a Distinguished University Professor of Economics at the University of Maryland, College Park, with affiliations in the Department of Survey Methodology. She serves as Director of the Maryland Center for Economics and Policy and is a Research Associate at the National Bureau of Economic Research (NBER). Her career includes leadership roles as Commissioner of the Bureau of Labor Statistics (1993-2001) and member of the President’s Council of Economic Advisers (2011-2013). Ph.D. in Economics, Harvard University (1982) B.S. in Economics (with minor in Statistics), Iowa State University (1976) Research Focus : Labor economics, economic measurement, and policy analysis. Key areas include gig economy dynamics, retirement decisions, health insurance impacts, labor force participation trends, and improving economic data systems. Her work bridges theoretical analysis with practical survey methodology improvements. Recent projects examine mobile device data for onsite work analysis, self-employment patterns among older workers, and labor market effects of policy changes. She contributes to measuring human capital and addressing survey nonresponse biases. Scientific Recognition : Member, National Academy of Sciences Member, American Academy of Arts and Sciences Distinguished Fellow, American Economic Association Fellow, American Statistical Association and IZA Institute Policy Impact : Chair of the Commission on Evidence-Based Policymaking (2016-2017), which shaped the Foundations for Evidence-Based Policymaking Act (2018). Currently serves on advisory committees for the Congressional Budget Office, Bureau of Economic Analysis, and Federal Reserve Bank of Chicago.
Yoav Zemel is a Lecturer at the École polytechnique fédérale de Lausanne (EPFL) , affiliated with the School of Basic Sciences and Department of Mathematics . He specializes in statistical aspects of optimal transport and related geometric methods. B.Sc. Mathematics & Economics, Hebrew University of Jerusalem (summa cum laude, 2010) M.Sc. Applied Mathematics, EPFL (2012) PhD Mathematical Statistics, EPFL (2017), advised by Victor M. Panaretos His research focuses on geometrical statistics , point processes , shape theory , and optimal transportation . Recent work explores covariance operators, Gaussian processes, and stochastic algorithms in Wasserstein spaces. Publications bridge theoretical advancements with applications in ecology, genetics, and machine learning. Scientific awards include the Robert May Prize , Swiss government scholarship , and multiple Hebrew University honors. He has taught courses on probability, statistical machine learning, and optimal transport at EPFL, Göttingen, and Cambridge.
Franck Iutzeler is a Professor of Applied Mathematics at Université de Toulouse, working within the Statistics & Optimization team of the Institut Mathématique de Toulouse and teaching in the Department of Mathematics. He previously served as an Assistant Professor at Université Grenoble Alpes from 2015 to 2023 and completed his Habilitation à Diriger des Recherches in 2021. His research focuses on the intersection of optimization, statistics, and optimal transport theory to develop robust data-driven models. Key areas include numerical optimization, statistical learning, stochastic programming, and optimal transport. He is particularly interested in distributionally robust optimization using Wasserstein metrics and has developed the skwdro Python library for implementing these methods. Iutzeler's recent publications demonstrate a strong focus on Wasserstein Distributionally Robust Optimization (WDRO), with multiple papers in top venues like NeurIPS and SIAM Journal on Optimization. His work bridges theoretical guarantees with practical implementation, particularly through the skwdro library which provides efficient code for WDRO in machine learning applications. ANR JCJC grant for project STROLL: Harnessing Structure in Optimization for Large-scale Learning Co-PI of ANITI chair on Trust and Responsibility in Artificial Intelligence led by JM. Loubes and J. Bolte Iutzeler actively supervises PhD students including Yu-Guan Hsieh (awarded Université Grenoble Alpes's PhD award), Gilles Bareilles, Waïss Azizian, and Victor Mercklé. He has secured research funding through the ANR (MAD project on Automatic Differentiation) and ANITI. His current research includes statistical fairness using optimal transport theory and automatic differentiation for stochastic optimization. He leads the development of the skwdro library for Wasserstein Distributionally Robust Optimization and is involved with ANITI (Toulouse's AI Cluster), where he also took responsibility for the 2nd year of the Master SID in Data Science & Engineering in September 2024.
Dr. Fatine Ezbakhe serves as a Senior Research Associate (Scientific Collaborator, CS2) at the Institute for Environmental Sciences (ISE) and the Geneva Water Hub at the University of Geneva. She joined the institution in September 2019 as a post-doctoral researcher in the 'Monitoring for international hydropolitical tensions' project and was promoted to her current position in October 2020. Additionally, she coordinates the Mediterranean Youth for Water (MedYWat) network, supported by the Center for Mediterranean Integration, since May 2020. Dr. Ezbakhe holds a PhD in Civil Engineering from the Polytechnic University of Catalunya (Barcelona, Spain), completed in 2019, with specialization in decision analysis for sustainable development. Her academic background includes a Master's degree in water and sanitation for development, establishing her interdisciplinary expertise at the intersection of engineering, environmental science, and policy analysis. Her research program centers on the relationship between data, information, and knowledge in water and environmental governance, with particular emphasis on the political dimensions of data production and utilization in policymaking. A unifying thread throughout her work is the conceptualization of water as both a physical and social resource, addressing this dual nature through interdisciplinary collaboration. Her current research investigates how discourses function as both data sources and instruments for shaping water politics and policies within transboundary contexts. Dr. Ezbakhe's scholarly output reveals a clear trajectory of increasingly sophisticated engagement with the nexus of water governance, political science, and data analysis. Her most recent publications demonstrate a methodological shift toward discursive approaches in hydropolitics, examining how language and narratives influence water-related decision-making processes across international boundaries. This evolution reflects her growing interest in the epistemological foundations of water governance and the power dynamics embedded in knowledge production. Coordinator of Mediterranean Youth for Water (MedYWat) network Contributor to Certificate of Advanced Studies (CAS) in Water Governance Research support for Geneva Water Hub's scientific direction Former research support officer at Engineering Sciences and Global Development group As Coordinator of MedYWat, Dr. Ezbakhe plays a pivotal role in developing the next generation of water professionals across the Mediterranean region, fostering capacity building and knowledge exchange among young water specialists. Her leadership in this initiative underscores her commitment to bridging academic research with practical water governance challenges facing the region.
Andreas Taras is a Full Professor at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering, where he serves as Deputy Head of the Institute of Structural Engineering. His research focuses on steel and composite structures, structural stability, and fatigue behavior. Key research domains include: innovative steel-concrete-timber composite systems, stability and reliability of metallic structures, fatigue life prediction in bridges, and structural optimization using advanced analysis methods. His work integrates experimental testing with computational approaches to advance sustainable structural design. Recent investigations explore cutting-edge applications like iron-based shape memory alloys in structural glass systems, robotic additive joining for reclaimed steel, and novel hybrid systems combining folded sheet steel with cement-free concrete. Publications demonstrate consistent innovation in structural materials and connection technologies.
Bastian Alexander Grossenbacher is a Full Professor of Machine Learning at the University of Fribourg, where he leads the AIDOS Lab within the Department of Informatics, Faculty of Science and Medicine. He holds secondary appointments at the Institute of AI for Health and the Helmholtz Pioneer Campus of Helmholtz Munich. He is also a TUM Junior Fellow and a member of ELLIS and AI-LIFE. He co-directs the Applied Algebraic Topology Research Network (AATRN) and is involved in the Topology, Algebra, and Geometry in Data Science (TAG DS) initiative. His research lies at the intersection of geometry, topology, and machine learning, with a focus on Geometric Deep Learning, Topological Deep Learning, and Topological Data Analysis. He develops novel machine learning methods that use topological and geometric priors to improve model robustness and interpretability, particularly in biomedical applications. His work spans theoretical foundations and practical implementations in areas such as single-cell analysis, medical imaging, and network science. The 15 most recent publications (2023–2025) reveal a strong trend toward integrating algebraic and differential topology into deep learning architectures. Key themes include the use of Euler Characteristic and magnitude transforms, curvature-based analysis of graphs and data, simplicial and manifold-based neural representations, and the application of topological methods to biomedical data. His articles appear in top-tier venues such as Nature Communications, ICML, NeurIPS, ICLR, and IEEE conferences, reflecting both theoretical depth and high-impact applications. ERC Starting Grant Bastian Rieck is actively involved in mentoring and academic leadership. He supervises PhD students and postdoctoral researchers in the AIDOS Lab and is open to new collaborations, particularly with candidates from interdisciplinary backgrounds. He has secured significant research funding, including the ERC Starting Grant, and leads multiple collaborative research initiatives. He emphasizes open science, sharing code, data, and educational materials publicly. He leads the AIDOS Lab, which focuses on advancing machine learning through topological and geometric principles. He is also a co-director of the Applied Algebraic Topology Research Network (AATRN), which hosts a large online seminar series. Additionally, he maintains DONUT, a curated database of non-theoretical applications of topology, and is active in the ELLIS and AI-LIFE networks, fostering international collaboration in AI and health.
