جست‌وجوی استاد و پژوهشگر

Radu Vintanمشاهده پروفایل

پژوهشگر

• Theoretical Computer Science
• Online Algorithms
• Approximation Algorithms
• +۵ مورد دیگر

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

Michele Ceriottiمشاهده پروفایل

دانشیار

• Atomistic computer simulations
• Statistical mechanics
• Machine learning
• +۴ مورد دیگر

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.

• Neuroscience
• Neuroengineering
• Computational Biology
• +۳ مورد دیگر

Pavan P Ramdya is an Associate Professor at EPFL's School of Life Sciences, affiliated with the Brain Mind Institute and Institute of Bioengineering. He leads the Ramdya Lab, focusing on reverse-engineering Drosophila neural systems to uncover principles of biological intelligence and inform AI/robotics. His interdisciplinary approach combines genetics, computational modeling, and neuroimaging. Education: PhD in Neurobiology from Harvard University (2009) Research spans neuroengineering , motor control , and collective behavior . Key contributions include creating NeuroMechFly (neuromechanical model of Drosophila ), mapping descending/ascending neuron networks , and developing DeepFly3D , LiftPose3D software for behavioral analysis. His work reveals how neural populations in the brain and ventral nerve cord coordinate locomotion and social behaviors. Recent publications focus on hierarchical sensorimotor control (Nature Methods 2024), neural substrates of sociability (bioRxiv 2024), and 3D pose estimation algorithms. The lab secures major grants including Kavli Exploration Awards , SNSF Eccellenza Grants , and HFSP Fellowships . Scientific Awards: Kavli Fellowship, SNSF Eccellenza Grant, HFSP Career Development Award, Wellcome Trust Fellowship, UNIL Young Investigator Award As a PhD program committee member for EDBB and EDNE , he mentors students in computational neuroscience and neuroengineering. His team has trained multiple PhD candidates including Victor Lobato-Rios and Pembe Gizem Ozdil.

Dorina Thanouمشاهده پروفایل

پژوهشگر

• Data Science
• Machine Learning
• Graph Signal Processing
• +۳ مورد دیگر

Dorina Thanou is a senior researcher and lecturer at École Polytechnique Fédérale de Lausanne (EPFL), leading strategic research initiatives on AI for Health and Science under the EPFL AI Center. Her work spans graph signal processing , machine learning , and AI for biomedical applications . She holds affiliations with multiple EPFL units, including the Signal Processing Laboratory 4 (STI IEM LTS4), the Chair of Mathematical Data Science (SB MATH MDS1), and the AI Center (VPA-AVP-CP AICENTER). Education: Diploma in Electrical and Computer Engineering (2008), University of Patras, Greece M.Sc. in Communication Systems (2010), EPFL Ph.D. in Electrical Engineering (2016), EPFL Her research focuses on interpretable and robust machine learning models for biomedicine , leveraging graph-based signal processing to analyze complex data in healthcare. Her publication trends emphasize graph representation learning , dynamic 3D data compression , and biomedical applications , particularly in cardiology and neuropathology. Scientific Awards: ELLIS Scholar in Geometric Deep Learning (2021) Best Student Paper Award, IEEE ICASSP (2015) Top 10% Paper Award, IEEE ICIP (2015) Best Paper Award, Picture Coding Symposium (2016) IEEE Senior Member (2023) She mentors PhD students Monteiro Lança Madeira Manuel and Ögüt Sevda and has led interdisciplinary collaborations through the Swiss Data Science Center (2016-2020) and Center for Intelligent Systems (2020-2024). Her labs include the Signal Processing Laboratory 4 , Mathematical Data Science Lab , and the AI Center .

Emmanuel Lantiمشاهده پروفایل

مدرس

• Plasma Physics
• Fusion Energy Research
• Gyrokinetic Simulations
• +۳ مورد دیگر

Emmanuel Lanti is a Lecturer and HPC Applications Specialist at École polytechnique fédérale de Lausanne (EPFL), affiliated with the School of Engineering and the Center for Research and Fundamental Studies (SCITAS). His work focuses on plasma physics, computational methods for gyrokinetic simulations, and high-performance computing (HPC) optimization in tokamak research environments. Research Areas: Plasma physics, fusion energy, electromagnetic turbulence, nonlinear dynamics of Alfven modes, and HPC applications in particle-in-cell (PIC) code development. Technical Expertise: Global gyrokinetic simulations, parallel computing architectures, numerical methods for plasma confinement, and wave-particle interaction analysis. Lanti's recent publications emphasize advancements in: Modeling tokamak plasmas with nonadiabatic chirping-frequency Alfven modes (2023) Optimizing computational strategies for electromagnetic turbulence simulations (2022) Developing numerical tools for burning plasmas and open-field-line configurations (2021-2019) Validating gyrokinetic code performance on many-core and GPU-accelerated systems (2016-2018) His work bridges plasma theory with practical code implementation, targeting improved understanding of magnetic confinement and turbulence in fusion devices. Key methodologies include: Particle-in-cell (PIC) simulations Hybrid kinetic electron models Pade approximation techniques Parallel computing optimizations

• Computer Vision
• Machine Learning
• Robotics
• +۷ مورد دیگر

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.

Daniel Kuhnمشاهده پروفایل

استاد

• Data-driven Optimization
• Stochastic Programming
• Robust Optimization
• +۳ مورد دیگر

Daniel Kuhn is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL), where he holds the Chair of Risk Analytics and Optimization in the College of Management of Technology. His research focuses on developing computational methods for data-driven decision-making under uncertainty, with applications in engineered systems, machine learning, and finance. Previously, he held positions at Imperial College London and Stanford University. Education includes: PhD in Economics, University of St. Gallen MSc in Theoretical Physics, ETH Zurich Research interests span data-driven optimization , stochastic programming , and robust decision-making frameworks . His work develops computationally tractable methods for uncertainty quantification in complex systems, bridging operations research with statistical learning. Current investigations focus on distributionally robust optimization using Wasserstein metrics and applications in energy markets and fair machine learning. Publication analysis reveals three primary trends: 1) Fundamental advances in distributionally robust optimization theory, 2) Machine learning applications with uncertainty guarantees, and 3) Energy system optimization under regulatory constraints. His methodological work consistently emphasizes computational tractability and practical applicability. As lab director of the Risk Analytics and Optimization group, he leads research on: Stochastic control systems Data-driven decision frameworks Robust machine learning Current PhD students include researchers working on federated learning fairness, optimal power flow, and reinforcement learning theory. Past graduates have made significant contributions to Wasserstein distributionally robust optimization and vehicle-to-grid frequency regulation.

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Amir Zamir is a Tenure-Track Assistant Professor of Computer Science at the Swiss Federal Institute of Technology (EPFL) and leads the Visual Intelligence and Learning Lab (VILAB). Previously affiliated with UC Berkeley, Stanford, and UCF, his research spans computer vision, machine learning, and AI, focusing on embodied intelligence and multimodal learning. Current Appointments: EPFL (Tenure-Track Assistant Professor) Industry Experience: Chief Scientist at Aurora Solar (2015-2022), Chief Scientist at Duranta His research emphasizes advancing vision systems beyond narrow approaches toward general multi-modal/multi-task models that operate in real-world environments. Key projects include 4M (Multimodal Foundation Models), Taskonomy (Task Transfer), Gibson (Sim-to-Real), and Omnidata (Steerable Datasets). Current work explores flexible-length tokenization (FlexTok), visual personalization (ViPer), and robust cross-domain learning . Recent publications demonstrate expertise in autoregressive image generation , vision-language modeling , and embodied robotics . Awards include the ECCV Young Researcher Award , NVIDIA Pioneering Research Award , and multiple CVPR/SIGGRAPH best paper recognitions . He advises PhD students in vision systems, AI, and robotics, and teaches courses on Visual Intelligence, AI Product Management, and Autonomous Robotics.

Ljubisa Miskovicمشاهده پروفایل

پژوهشگر ارشد

• Systems Biology
• Machine Learning
• Dynamical Systems
• +۴ مورد دیگر

Ljubisa Miskovic is a Senior Scientist at École Polytechnique Fédérale de Lausanne (EPFL), holding multiple positions including at the Laboratory of Computational Systems Biotechnology (LCSB) within the School of Basic Sciences (SB) and Institute of Chemical Sciences and Engineering (ISIC), as well as at the Swiss Center for Scientific Computing (SCGC) and the School Council SB. He earned his Ph.D. degree in Automatic Control from EPFL in 2006 under the co-supervision of Dominique Bonvin and Alireza Karimi. Following his doctoral studies, he pursued postdoctoral research at the Centre for Systems Engineering and Applied Mechanics at Universite Catholique de Louvain with Michel Gevers before returning to EPFL to work with Vassily Hatzimanikatis. Dr. Miskovic's research spans systems biology, metabolic engineering, synthetic biology, data-driven control design, system identification, stochastic processes, and estimation theory. His work focuses on developing computational approaches for metabolic network analysis, kinetic modeling of genome-scale metabolic networks, and applying machine learning techniques to metabolic engineering problems. His recent publications demonstrate increasing integration of generative AI methods with traditional metabolic modeling approaches. His publication trends over the last five years reveal a clear progression from traditional metabolic modeling to sophisticated machine learning-enhanced approaches, with particular emphasis on genome-scale kinetic modeling, strain optimization for biochemical production, and uncertainty quantification in biological systems. Much of his recent work centers on applications in S. cerevisiae and P. putida for producing valuable biochemicals including p-coumaric acid and muconic acid. Dr. Miskovic has advised several doctoral students including Toumpe Ilias, Andreozzi Stefano, Choudhury Subham, and Tokic Milenko. He teaches courses in numerical methods and chemical process control at EPFL, demonstrating his commitment to both research and education in systems biotechnology. As a member of the Laboratory of Computational Systems Biotechnology led by Professor Vassily Hatzimanikatis, Dr. Miskovic contributes to a multidisciplinary team that combines expertise in computational biology, systems engineering, and metabolic modeling to advance the field of systems biotechnology.

Dongyang Fanمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning
• Natural Language Processing
• +۵ مورد دیگر

Dongyang Fan is a Researcher at the Swiss Federal Institute of Technology Lausanne (EPFL), affiliated with the School of Computer and Communication Sciences and the Institute of Computer Science. She works in the Machine Learning and Optimization Laboratory (MLO) under EPFL's Doctoral Program in Computer and Communication Sciences, conducting cutting-edge research in artificial intelligence and machine learning. Her research spans Machine Learning, Deep Learning, Natural Language Processing, Optimization, Federated Learning, Mixture of Experts architectures, On-device Learning, and Ethics in AI. She focuses on developing efficient, ethical, and personalized AI systems with practical applications in edge computing and collaborative environments, addressing challenges in model training, data valuation, and real-world deployment constraints. Analysis of her recent publications reveals a concentrated effort on large language models (LLMs), with significant contributions to on-device collaborative learning frameworks, Mixture of Experts optimization, and ethical implications of web data usage. Her work bridges theoretical advancements in model architecture with practical implementations for resource-constrained devices, emphasizing privacy-preserving techniques and fairness-aware design principles. She is an integral member of EPFL's Machine Learning and Optimization Laboratory, contributing to research teams focused on next-generation AI algorithms and their optimization for real-world applications in distributed systems and edge computing environments.

Luca Righettiمشاهده پروفایل

پژوهشگر

• Materials Science
• Theoretical Materials
• Simulation of Materials
• +۲ مورد دیگر

Luca Righetti is a researcher at the École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the School of Engineering (STI) and the Institute of Materials (IMX) . He works at the Laboratoire de théorie et simulation des matériaux (THEOS) , focusing on materials science and computational simulations. Additionally, he is part of the PRN-MARVEL (National Center of Competence in Research Materials' Simulation and Design) network under the PRNMARVEL-GE subunit. His contact details include the email luca.righetti@epfl.ch and office location: MED 2 1018, Station 9, 1015 Lausanne, Switzerland. Luca is also a doctoral student in the Doctoral Program in Materials Science and Engineering at EPFL, indicating dual roles in research and education. His work aligns with theoretical materials, solid-state physics, and advanced simulations, reflecting interdisciplinary collaboration within EPFL's materials research ecosystem.

• Quantum Computing
• Nanoscience
• Nanotechnology
• +۷ مورد دیگر

Heike Riel is an IBM Fellow and the Head of Science of Quantum and Information Technology at IBM Research. She also holds the Rudolf Diesel Industry Fellowship at the Institute for Advanced Study (TUM-IAS), Technical University of Munich (TUM). Her work focuses on advancing quantum computing, nanoscale electronics, and neuromorphic devices through materials science innovations. She earned a PhD in Physics from the University of Bayreuth (2003) for research on organic light-emitting diodes (OLEDs) at IBM Research, following an MBA from Henley Business School (2010) and an internship at Hewlett-Packard Research Lab. Her career at IBM began in 1998, progressing to leadership roles in nanowire and quantum device research. Her research spans semiconducting nanowires, hybrid quantum systems, and spintronic devices, with recent publications emphasizing quantum dots, Josephson junctions, and epitaxial growth techniques. She has authored over 140 peer-reviewed papers and filed more than 50 patents, reflecting her technical impact. Scientific honors include: 2013 IBM Fellow (IBM's highest technical distinction) 2017 David Adler Lectureship Award (Materials Physics) 2020 American Physical Society (APS) Fellow 2015 Honorary Doctorate from Lund University 2012 SVIN Innovation Award Heike serves on advisory boards for Forschungszentrum Jülich and the National Metrology Institute of Germany (PTB), while leading quantum research initiatives across Europe. She is a life member of the Schweizerischen Akademie der Technischen Wissenschaften (SATW) and the German National Academy of Sciences Leopoldina.

Sereina Rinikerمشاهده پروفایل

استاد

• Computational Chemistry
• Molecular Dynamics Simulations
• Cheminformatics
• +۳ مورد دیگر

Sereina Riniker is a Full Professor of Computational Chemistry at the Department of Chemistry and Applied Biosciences , ETH Zurich. Her career at ETH Zurich began in June 2014 as Assistant Professor (tenure track), followed by promotions to Associate Professor (April 2020) and Full Professor (April 2025). The group focuses on developing advanced computational methods combining molecular dynamics simulations with cheminformatics techniques . Born in Switzerland (1985) MSc in Chemistry (ETH Zurich, 2008) PhD in Molecular Dynamics (ETH Zurich, 2013) Research interests include: QM/MM hybrid simulations Machine learning for molecular modeling Free energy calculations Implicit solvent models Conformational analysis Drug discovery applications Recent publications show strong trends in combining quantum mechanical accuracy with machine learning efficiency , including novel approaches like neural network potentials and multiresolution modeling . The group has received multiple grants from the Swiss National Science Foundation (SNF) for their work on free energy calculations and molecular modeling . Awards and honors include: 2024 Hansch Award 2018 ETH Latsis Prize 2017 OpenEye Outstanding Junior Faculty Award 2017 Silver Jubilee Award 2015 Ewald Wicke Prize The Computational Chemistry group maintains strong industrial collaborations, particularly with Novartis and Givaudan AG. Their research combines theoretical method development with practical applications in biological and chemical systems.

Francesco Audrinoمشاهده پروفایل

استاد

• Computational Statistics
• Financial Econometrics
• Machine Learning
• +۶ مورد دیگر

Francesco Audrino is a Professor of Statistics at the Department of Mathematics and Statistics, University of St. Gallen, affiliated with the School of Economics and Political Science (SEPS). He holds a Diploma in Mathematics from ETH Zurich (specializing in Financial and Insurance Mathematics) and a Ph.D. in Statistics/Finance from ETH Zurich, with a thesis on statistical methods for high-dimensional financial time series. Research Focus: Computational Statistics applied to Economics and Finance, Financial Econometrics, Sentiment Analysis, Volatility Estimation, and Regime-switching Models. His work emphasizes nonparametric methods (e.g., Functional Gradient Descent) and their applications in asset pricing, interest rate dynamics, and risk management. Recent projects include the SentiVol program for sentiment-driven volatility forecasting and causal machine learning analyses of post-earnings sentiment impacts. Selected Projects: Director of the SentiVol SNF Grant (2017–2020) on sentiment analysis for volatility prediction. Co-Director of the SNF Grant on Behavioral Asset Pricing (2010–2013). Leading research on cross-asset dependency structures and causal machine learning applications. Awards & Recognition: Swiss Society of Economics and Statistics Young Economist Award (2007). University of St. Gallen’s Best Researcher Award (2007). Teaching load reduction for outstanding publications (2009–2018). Teaching & Academic Service: Teaches Statistics, Financial Volatility, and Computational Statistics at bachelor’s, master’s, and Ph.D. levels. Served as Elected Member of the Board of Directors of the European Regional Section of the International Association for Statistical Computing (2016–2020). Active in academic networks like the Computational Financial Econometrics (CFE) Network. Labs & Resources: Developed the FGD Code package for nonparametric high-dimensional time series analysis, widely used in volatility and correlation modeling. Maintains real-time volatility forecasts for S&P 500 constituents via the SentiVol project.

Sebastian Bonhoefferمشاهده پروفایل

استاد

• Theoretical Biology
• Population Dynamics
• Evolutionary Ecology
• +۶ مورد دیگر

Sebastian Bonhoeffer is a Full Professor in Theoretical Biology at ETH Zurich, where he has been serving since July 1, 2005. He is affiliated with the Department of Environmental Systems Science and serves as Head of Collegium Helveticum. His research focuses on using population biological models to understand fundamental biological processes, with particular expertise in mathematical modeling of virus infections within infected individuals, providing important insights into the pathogenesis of viral infections, especially HIV. Prof. Bonhoeffer was born in Tübingen, Germany. His academic journey began with Music studies in Basel, followed by Physics studies in Munich and Vienna. He then moved to Oxford for his PhD with Martin Nowak and Robert May at the Department of Zoology. After completing his PhD, he remained in Oxford as a Research Fellow of Wolfson College and worked as a visiting scientist with David Ho at the Rockefeller University in New York. Prior to his appointment at ETH Zurich in 2001 as an SNF Research Professor, he was a junior group leader at the Friedrich Miescher Institut in Basel. His research interests span theoretical biology, population dynamics, evolutionary ecology of infectious diseases, mathematical modeling of viral infections, and antimicrobial resistance. He has made significant contributions to understanding the population dynamics of virus infections within infected individuals, particularly HIV. His work has provided important insights into the pathogenesis of viral infections and the evolutionary dynamics of pathogens. Prof. Bonhoeffer's recent publications demonstrate a diverse research portfolio spanning microbial ecology, epidemiology, evolutionary biology, and antimicrobial resistance. His work shows a strong focus on applying mathematical and computational approaches to understand complex biological systems, from viral dynamics to microbial communities and pandemic responses. His research often involves interdisciplinary collaborations addressing pressing public health challenges. Prof. Bonhoeffer has supervised numerous students and mentored early-career researchers in theoretical biology and mathematical modeling. His work has been supported by various research grants, though specific details are not provided in the available information. He is actively involved in teaching, offering courses such as "Seminar in Evolutionary Ecology of Infectious Diseases" and "Ecology and Evolution: Term Paper" at ETH Zurich.