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Meisam RazaviyaynView profile

Associate Professor

• Optimization Algorithms
• Data Science
• Machine Learning
• +4 more

Meisam Razaviyayn is an Associate Professor at the University of Southern California (USC) with joint appointments in the Department of Industrial and Systems Engineering , Computer Science , Quantitative and Computational Biology , and Electrical Engineering . He serves as the Associate Director of the USC-Meta Center for Research and Education in AI and Learning and holds the Andrew and Erna Viterbi Early Career Chair . His academic background includes a Ph.D. in Electrical Engineering with a minor in Computer Science from the University of Minnesota, followed by a postdoctoral fellowship at Stanford University. Razaviyayn maintains active collaborations with industry partners like Google Research and Amazon. Current Roles : Faculty Visitor at Google Research Editorial Roles : Associate Editor for SIAM Journal on Optimization and IEEE Transactions on Signal Processing His research focuses on optimization algorithms for modern data science problems, particularly addressing privacy , fairness , and scalability in machine learning systems. Recent work includes differentally private optimization frameworks and fair learning algorithms applicable to large-scale and decentralized data scenarios. Key scientific awards include: 2022 NSF CAREER Award 2022 Northrop Grumman Excellence in Teaching Award 2021 AFOSR Young Investigator Award 2021 3M Nontenured Faculty Award 2020 ICCM Best Paper Award in Mathematics 2019 IEEE Data Science Workshop Best Paper Award He actively advises Ph.D. students and leads research initiatives in robust and private machine learning . His group has received funding from NSF, Google Research, and Amazon for projects related to generative AI training , differentially private algorithms , and distributed optimization . Razaviyayn also contributes to academic service as a conference chair and workshop organizer.

Víctor Sabanza GilView profile

Researcher

• Artificial Chemical Intelligence
• Sustainable Chemistry
• Catalysis
• +4 more

Víctor Sabanza Gil is a Doctoral Assistant at École polytechnique fédérale de Lausanne (EPFL), affiliated with the School of Basic Sciences (SB) and the Institute of Chemical Sciences and Engineering (ISIC). He conducts research in two prominent laboratories: the Laboratory of Artificial Chemical Intelligence (LIAC) and the Laboratory of Sustainable and Catalytic Processing (LPDC) . He is also a registered student in the Doctoral Program in Chemistry and Chemical Engineering (EDCH) under the EDOC doctoral school. His research interests lie at the intersection of artificial intelligence and chemistry, with a strong emphasis on sustainable and catalytic chemical processes. These areas are reflected in his dual appointments, where he contributes to advancing intelligent systems for chemical discovery and developing efficient, environmentally friendly catalytic technologies. No publications are listed in the provided text, so no trends can be derived at this time. Laboratory of Artificial Chemical Intelligence (LIAC) : Focuses on AI-driven chemical innovation. Laboratory of Sustainable and Catalytic Processing (LPDC) : Specializes in green catalysis and process engineering. Víctor is actively engaged in doctoral research and academic training at EPFL, contributing to cutting-edge research in chemical intelligence and sustainability. He is based in offices CH J2 504 (LIAC) and CH H2 585 (LPDC). No information is available regarding student advising, grants, or awards in the provided content.

Michele CeriottiView profile

Associate Professor

• Atomistic computer simulations
• Statistical mechanics
• Machine learning
• +4 more

Michele Ceriotti is an Associate Professor at the École polytechnique fédérale de Lausanne (EPFL) , affiliated with the School of Engineering (STI) and the Institute of Materials (IMX) . He directs the Computational Science and Modeling Laboratory (COSMO) , focusing on atomistic modeling, statistical mechanics, and machine learning. His roles include teaching in the EPFL Doctoral Programs and serving on editorial boards for Journal of Chemical Physics and Physical Review Materials . PhD in Physics, ETH Zürich Junior Research Fellow (JRF), Merton College, Oxford His research centers on developing machine learning models for quantum chemistry, molecular dynamics, and materials science, with a focus on open-source software like i-PI and chemiscope . Recent work includes integrating symmetry into atomic-scale learning and advancing uncertainty quantification in neural networks. Recent publications span topics such as electronic density of states , high-entropy alloy surface segregation , and data-driven chemistry , highlighting his expertise in machine learning and quantum systems . He has received the Junior Research Fellow at Merton College, Oxford award and is actively involved in mentoring PhD students and postgraduates. His laboratory, COSMO, collaborates on multi-energy systems and contributes to the atomistic modeling community through software development and editorial leadership.

• Statistical Physics
• Optimization
• Machine Learning
• +1 more

Damien Barbier is a Lecturer at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Basic Sciences and the Institute of Physics. He is associated with the EDPY-ENS teaching program, which focuses on interdisciplinary education in physics and engineering. Current position: Lecturer Institutional affiliation: EPFL SB IPHYS SPOC1 Teaching focus: Statistical physics for optimization and learning His research interests align with statistical physics , particularly in applications to optimization algorithms and machine learning frameworks . While specific publications and students are not listed in the provided text, his work appears to bridge theoretical physics and computational methods.

• Statistical Physics
• Machine Learning
• Optimization
• +2 more

Ludovic Stephan is a Lecturer at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the EDPY-ENS unit under the Vice Presidency for Academic (VPA) and Dean's Office (AVP-DLE-EDOC). His teaching focuses on interdisciplinary topics bridging statistical physics with optimization and learning. Research Interests: Statistical Physics, Machine Learning, Optimization, Computational Learning Theory, Applied Mathematics As an invited guest lecturer, he contributes to academic programs at EPFL in Lausanne, Switzerland, with office address at ELD 331, Station 11, 1015 Lausanne.

Maximilien DrevetonView profile

Assistant Professor

• Network Analysis
• Community Detection
• Clustering
• +3 more

Maximilien Dreveton is an Assistant Professor (Maître de Conférences) in Statistics at Université Paris Gustave-Eiffel and a member of LAMA (Laboratoire d'analyse et de mathématiques appliquées). Prior to his current position, he was a postdoctoral researcher in the INDY Lab at EPFL under the supervision of Patrick Thiran and Matthias Grossglauser. He earned his PhD in Computer Science in 2022 from Inria Sophia Antipolis with Konstantin Avrachenkov as his supervisor. His research focuses on developing innovative algorithms and understanding the theoretical limits of identifying meaningful patterns and structures within large datasets. He has particular expertise in network analysis, community detection, and clustering. Dreveton co-authored the open-access book Statistical Analysis of Networks , which provides a comprehensive introduction to network statistical analysis covering modeling, community detection, graph-based semi-supervised learning, and network sampling. His recent publications demonstrate a consistent focus on theoretical foundations of network analysis and community detection, with papers accepted at top venues including NeurIPS and COLT. His work on metric backbone preservation of community structure and universal lower bounds for clustering error represents significant theoretical contributions to the field. He also co-presented a tutorial on community detection at ACM SIGMETRICS / IFIP PERFORMANCE in June 2024. Dreveton maintains an active research profile with upcoming presentations at Netsci 2025. He has developed several open-source repositories related to clustering algorithms and graph analysis, reflecting his commitment to reproducible research and community engagement. He has also authored educational materials for mathematics preparation, including Leçons pour l'agrégation de mathématiques - Préparation à l'oral for French mathematics examination candidates.

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

Alexandre Alahi is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he leads the Visual Intelligence for Transportation (VITA) laboratory. He is affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC), the Institute of Infrastructure (IIC), and also contributes to diversity initiatives at ENAC. His research focuses on integrating computer vision, machine learning, and robotics to develop socially-aware AI for transportation and autonomous systems. University: École Polytechnique Fédérale de Lausanne (EPFL) School: School of Architecture, Civil and Environmental Engineering Department: Institute of Infrastructure, IIC Research Lab: Visual Intelligence for Transportation (VITA) Alexandre's research interests center on computer vision, machine learning, robotics, and AI safety, particularly in human trajectory prediction, depth estimation, and socially-aware autonomous navigation. He investigates how AI can understand and predict human behavior in complex environments to improve safety in mobility systems. His work bridges theoretical advances with real-world applications in autonomous driving, urban planning, and healthcare. His recent publications span a wide array of topics including omnidirectional stereo matching, trajectory forecasting, cross-view localization, AI security, and depth estimation. These works demonstrate a strong trend toward building generalizable, robust, and socially-compliant AI systems, with increasing focus on uncertainty quantification, safety certification, and real-world deployment. The integration of multimodal data and the development of foundation models are recurring themes. Alexandre has received numerous scientific accolades, including: Top 100 Most Influential Scholar in Computer Vision (2022–2023) Editor’s Choice Award, Image and Vision Computing (2021) Honorable Mention, ICCV Workshop (2019) CVPR Open Source Award (2012) ICDSC Challenge Prize (2009) Top 20 Swiss Venture Leaders (2010) He has advised numerous PhD students whose theses cover diverse topics such as human motion prediction, person re-identification, trajectory forecasting, and AI security. His lab has secured significant recognition and funding, enabling impactful research with real-world applications. Alexandre has also co-founded startups like Visiosafe, demonstrating strong industry engagement and technology transfer. The VITA lab fosters interdisciplinary collaboration, working across computer vision, robotics, transportation engineering, and human-centered AI. The team develops datasets, benchmarks, and open-source tools to advance the field and promote reproducibility.

Friedrich EisenbrandView profile

Professor

• Discrete Optimization
• Algorithms and Complexity
• Integer Programming
• +4 more

Prof. Friedrich Eisenbrand is a Professor at the Institute of Mathematics, EPFL, Lausanne, Switzerland. His research focuses on discrete optimization, algorithms and complexity, integer programming, and geometry of numbers. Heinz Maier-Leibnitz award (2004) Otto Hahn medal (2001) Alexander von Humboldt professorship (2011) His work includes efficient algorithms for integer programming in fixed dimension and the theory of cutting planes. Recent publications explore advancements in integer programming, discrete optimization algorithms, computational geometry, and machine learning applications. He leads the DISOPT laboratory at EPFL, mentoring a team of junior researchers.

Behzad BozorgtabarView profile

Lecturer

• Computer Vision
• Medical Image Analysis
• Machine Learning
• +6 more

Behzad Bozorgtabar is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL) and a Senior Scientist at the Lausanne University Hospital (CHUV) . He leads the Medical Imaging Group at EPFL's Signal Processing Laboratory (LTS5) , with affiliations to the European Lab for Learning & Intelligent Systems (ELLIS) and EPFL’s ELLIS Unit . His research focuses on self-supervised learning , test-time adaptation , and multimodal foundation models , aiming to create robust AI systems that adapt to changing environments. His work addresses domain shift , anomaly detection , and ethical AI deployment in medical imaging and computational pathology. His recent publications highlight trends in test-time adaptation (e.g., ReservoirTTA, OptTTA), self-supervised learning (CrIBo, CrOC), and medical imaging applications (histopathology, chest X-rays, colorectal cancer detection). Scientific Awards: IEEE TMI Distinguished Reviewer Award First Runner-up - AIMIA Workshop, ECCV Elected Member - European Lab for Learning & Intelligent Systems (ELLIS) IBM Research Division Image Award IBM Patent Award Imagine Cup, Australian Finals, Microsoft Advising & Funding: He has mentored PhD students (Tomar, Vray, Stegmüller) and secured grants from the Personalized Health and Related Technologies (PHRT) , Swiss Cancer League , and Discovery Translation Fund (DTF 2.0) . His service includes Area Chair roles for CVPR 2025, WACV 2026, and AAAI 2026.

Eric Pierre BouilletView profile

Lecturer

• Data Science
• Electrical Engineering
• Optical Networking
• +3 more

Eric Pierre Bouillet is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Computer and Communication Sciences (IC) and the School of Engineering (STI) . He also serves as the Head of Engineering at the Swiss Data Science Center (SDSC), a national R&D center co-hosted by EPFL and ETH Zurich. Bouillet earned his PhD in Electrical Engineering from Columbia University in 1999. Research Interests : Data Science, Electrical Engineering, Optical Networking, Network Optimization, and Stream Processing, with applications in telecommunications, intelligent transport systems, and aircraft reliability control systems. Teaching : Courses on large-scale data science, Python for data engineering, and real-world data analytics pipelines. Previous Roles : IBM Research (2004–2016), Tellium (optical mesh networks), and Lucent Technologies (network optimization). Labs/Teams : Swiss Data Science Center (SDSC), EPFL’s Data Science Teaching programs.

Nicolas MacrisView profile

Adjunct Professor

• Statistical Mechanics
• Information Theory
• Error Correcting Codes
• +3 more

Nicolas Macris is an Adjunct Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences (IC). He is affiliated with multiple groups including the Information Processing group and the Lab for Statistical Mechanics of Inference in Large Systems . His academic career spans roles at the Faculty of Basic Science at EPFL and visiting appointments at institutions like the University College Dublin and ETH Zürich. Macris received his PhD in theoretical physics from EPFL and conducted postdoctoral research at the mathematics department of Rutgers University. His research bridges statistical mechanics , information theory , and quantum information , with a focus on error-correcting codes, graphical models, and learning theory. His work often employs methods from mathematical and statistical physics, probability, and discrete mathematics. His recent publications analyze phase transitions in high-dimensional models, quantum algorithms, and spatial coupling techniques. He has received recognition through IBM Quantum contest awards for his contributions. He actively teaches courses in quantum information processing and mentors PhD students in interdisciplinary research areas. 2019: 2nd place, IBM Quantum contest (Best Paper) 2018: 2nd place, IBM Quantum contest (Best Paper) Macris's work is supported by the Swiss National Foundation for Science , and he collaborates with labs such as the Communication Theories Laboratory and Information Processing group .

• Statistics
• Econometrics
• Algorithmic Decision Making
• +3 more

David Preinerstorfer is a Professor of Statistics and Econometrics at the Vienna University of Economics and Business (WU) and Director of the Swiss Institute for Empirical Economic Research (SEW-HSG) at the University of St.Gallen. His research focuses on the statistical foundations of algorithmic decision making, particularly in dynamic economic systems and uncertain environments. Director, SEW-HSG (2024–2025) Associate Professor, University of St.Gallen (2021–2024) Assistant Professor, Université libre de Bruxelles (2017–2021) PhD in Statistics and Operations Research, University of Vienna (2011–2015) His work integrates reinforcement learning, optimal control, stochastic programming, and optimization to develop robust and scalable algorithmic solutions. Key research areas include high-dimensional inference, moment-based methods, and optimal policy learning with distributional targets. Recent publications focus on moment-dependent phase transitions, power enhancements for testing moment equalities, and regularization in policy learning. He has contributed to journals like Econometrica and Econometric Theory . Scientific Awards Econometric Theory Multa Scripsit Award (2024) Tjalling C. Koopmans Econometric Theory Prize (2024) Adolphe Wetrems Prize (2022) Advanced ARC Grant (2018) University of Vienna Publication Awards (2016) He collaborates with institutions such as Universitat Pompeu Fabra, TU Graz, and Erasmus University Rotterdam. His research emphasizes both theoretical and practical advancements in data-driven decision making.

Gil RegevView profile

Lecturer

• Requirements Engineering
• Business Modeling
• Enterprise Architecture
• +7 more

Gil Regev is a Lecturer at the Laboratory for Applied Software Engineering ( SSC-ENS ), part of the School of Computer and Communication Sciences ( IC ) at École Polytechnique Fédérale de Lausanne ( EPFL ). He serves as Mobility Delegate for IC-GE and has been affiliated with EPFL since 1997 after 9 years at Logitech. His academic work spans Requirements Engineering Business Process Modeling Enterprise Architecture Systems Thinking His research explores the intersection of organizational self-understanding and information systems design. Key trends in his work include Systemic approaches to service design Integration of appreciation and homeostasis in business processes Declarative specification methods Value network modeling Primitive obsession in software engineering Contextual requirements elicitation He has contributed to methodological frameworks like SEAM and practical tools for Service lifecycle management Business-IT alignment Flexible process modeling Regulation-based requirements Genomic data structure optimization Collaborative concept mapping

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +5 more

Jean Hennebert is an Ordinary Professor at the Haute école d'ingénierie et d'architecture de Fribourg (HES-SO) and serves as the Institute Head for iCoSys (Institute of Complex Systems). His research spans multiple domains including Machine Learning , Deep Learning , Artificial Intelligence , and Data Science , with applications in smart building systems , medical diagnostics , and document image analysis . PhD , Master in Architecture (HES-SO) BSc in Computer Science (HES-SO) His academic contributions focus on adaptive document image binarization (YinYang, ZigZag algorithms), time series generation (diffusion models), and smart building data platforms (BBData v2.0). His work integrates ML engineering with practical implementations in energy efficiency and medical device monitoring (e.g., contact lens sensor analytics for glaucoma detection). Key projects include the iCoSys Institute infrastructure for urban data analysis , radon concentration monitoring systems , and industrial process monitoring architectures . Publications demonstrate technical expertise in transformer-free models (SLANet-1M), recurrent neural networks , and low-cost sensor networks with active learning.

Juliane HollenderView profile

Adjunct Professor

• Fate of organic contaminants
• Organic trace analytics
• Bioaccumulation
• +7 more

Prof. Dr. Juliane Hollender serves as Senior Scientist and Group Leader of the 'Contaminant fate processes' research group at Eawag (Swiss Federal Institute of Aquatic Science and Technology) in the Department of Environmental Chemistry. She is also an Adjunct Professor in the Department of Environmental Systems Science at ETH Zurich since 2010 and has been lecturing there since 2007. Previously, she was an Adjunct Professor for Environmental Hygiene & Ecological Chemistry at Technical University of Aachen (2006-2014) and held a Group Leader & Lecturer position at the same university from 1994-2005. Dr. Hollender completed her PhD studies in Environmental Engineering at Technical University of Berlin (1991-1994) and earned her Diploma in Chemistry from University of Bonn & Freiburg i.Br. with specialization in biochemistry (1984-1990). Her research focuses on the fate of organic contaminants in natural and engineered environments, organic trace analytics using LC-HRMS/MS for identification of transformation products, bioaccumulation and biological transformation of organic compounds in aquatic organisms, and linking exposure with effects of organic pollutants. She is an expert in computational methods, biological degradation, bioaccumulation, groundwater analysis, and mass spectrometry. Her recent publications demonstrate a strong emphasis on non-target and suspect screening methodologies, bioaccumulation studies, and the development of analytical approaches for identifying emerging contaminants in water systems. Her work bridges analytical chemistry with environmental risk assessment, particularly focusing on pharmaceuticals, pesticides, and other micropollutants in aquatic environments. Dr. Hollender leads the research group 'Contaminant fate processes' at Eawag, which includes PhD students, postdoctoral researchers, and technicians working on various aspects of environmental contaminant analysis and fate. Her team is involved in major projects including PARC (assessment of risks from chemicals), advancing bioaccumulation science, and PFAS studies during wastewater treatment.