جست‌وجوی استاد و پژوهشگر

Mohammadsaeed Masihaمشاهده پروفایل

پژوهشگر

• Nonconvex Optimization
• Applied Probability Theory
• Stochastic Analysis
• +۳ مورد دیگر

Mohammadsaeed Masiha is a Doctoral Assistant at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Chair of Business Analytics (BAN) under the College of Management of Technology (CDM) and the Information and Network Dynamics Laboratory 2 (INDY2) under the College of Engineering (IC) . His research spans Nonconvex Optimization, Applied Probability Theory, Stochastic Analysis, High-dimensional Probability, Learning Theory, and Information Theory , reflecting interdisciplinary expertise in mathematical and computational methods. He is also a Doctoral Student in Technology Management through EPFL's Doctoral Program in Technology Management (EDMT) . Key research themes include theoretical foundations of optimization, probabilistic modeling, and high-dimensional data analysis. His work intersects machine learning, information theory, and network dynamics, with a focus on mathematical rigor and algorithmic development. Contact details include the email address mohammadsaeed.masiha@epfl.ch , with office locations in both the Odyssea building (Station 5) and Bâtiment INF (Station 14), Lausanne, Switzerland.

Karl Abererمشاهده پروفایل

استاد

• Distributed Information Management
• Peer-to-Peer Systems
• Semantic Interoperability
• +۶ مورد دیگر

Karl Aberer is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the School of Computer and Communication Sciences (IC) and leading the Distributed Information Systems Laboratory (LSIR) . He also serves as Director of the Swiss National Research Center for Mobile Information and Communication Systems (NCCR-MICS) since 2005. Education : PhD in Mathematics (1991, ETH Zurich); Postdoctoral Fellow at ICSI, UC Berkeley (1991-1992). Research Interests : Focus on distributed information management , decentralized architectures , trust management , and participatory sensing . Key themes include self-organization in peer-to-peer systems, semantic interoperability, and fairness in resource sharing. Publications : Recent works explore truthful data collection , privacy in participatory sensing , and trust-aware P2P systems , with applications to environmental monitoring and semantic web services. Leadership : Member of EPFL's School of Computer and Communication Sciences Direction, Director of the Swiss Science and Technology Council (2003-2011), and participant in PhD program committees. Labs & Collaborations : LSIR Laboratory (EPFL); NCCR-MICS (Swiss national center).

Sacha Friedliمشاهده پروفایل

مدرس

• Mathematics
• Analysis
• Probability Theory
• +۲ مورد دیگر

Sacha Friedli serves as a Lecturer in the Section of Mathematics within the School of Basic Sciences at École Polytechnique Fédérale de Lausanne (EPFL), while also holding a Teaching Staff Member position for Preparatory Mathematics Courses under the Academic Vice Presidency for Education (VPA-AVP-E). His dual roles span both the central administration and academic school structures at EPFL. His educational trajectory includes a Diploma in Physics from Université de Lausanne (1999) followed by a PhD in Theoretical Physics from EPFL's Institut de Physique Théorique (1999-2004). Professional experience encompasses a post-doctoral fellowship at IMPA (Instituto Nacional de Matemática Pura e Aplicada) in Rio de Janeiro (2004-2006) and a decade as Adjunct Professor in the Department of Mathematics at Universidade Federal de Minas Gerais, Brazil (2006-2016). Dr. Friedli's academic focus centers on foundational mathematics education, with teaching expertise spanning Analysis, Linear Algebra, and Statistical Mechanics. His current instructional portfolio includes Analysis I (standard and flipped classroom formats), Statistical Mechanics and Gibbs Measures, and specialized courses for the MAN program. This reflects a deliberate shift from his physics research origins toward mathematics pedagogy, particularly in preparing students for engineering and scientific disciplines through rigorous quantitative training. His teaching methodology emphasizes conceptual clarity in core mathematical disciplines, evidenced by course descriptions highlighting differential/integral calculus, probabilistic modeling of lattice systems, and geometric visualization in 2D/3D spaces. The absence of research publications in the provided materials indicates a career dedicated to educational delivery rather than active research output.

• Power Systems
• Smart Grids
• Renewable Energy Integration
• +۵ مورد دیگر

Bozorg Mokhtar is an Associate Professor at the School of Engineering and Management of the Canton of Vaud (HEIG-VD) , part of the University of Applied Sciences and Arts Western Switzerland (HES-SO). His research focuses on power systems, smart grids, and renewable energy integration. BSc and MA in Electrical Engineering from HES-SO Key projects: SCCER FURIES Phase 2 (WP3 on hybrid AC-DC grids), funded via CTI and HES-SO Research Interests include grid stability, power electronics, and energy storage optimization. He explores probabilistic flexibility indices , grid-forming inverters , and digital twin platforms for distribution networks. His work addresses challenges like peak shaving , carbon trading mechanisms , and dynamic state estimation . Scientific Contributions span 15 recent articles on DER aggregation, rail-to-EV energy recycling, and voltage control algorithms. His collaborations include researchers like Rachid Cherkaoui (EPFL) and Mauro Carpita (HEIG-VD).

Federica Sbergamiمشاهده پروفایل

مدرس ارشد

• International Economics
• Macroeconomics
• Microeconomics
• +۳ مورد دیگر

Federica Sbergami serves as a Senior Lecturer and Researcher at the Institute of Economics and Econometrics within the Geneva School of Economics and Management (GSEM) at the University of Geneva, where she maintains an active role in both teaching and scholarly research. Her academic foundation includes a Ph.D. in International Relations with specialization in International Economics from the Graduate Institute of International and Development Studies (IHEID) in Geneva. Her educational credentials are anchored by: Ph.D. in International Relations (specialization: International Economics) from the Graduate Institute of International and Development Studies (IHEID) Dr. Sbergami's research program demonstrates sustained engagement with International Economics , Macroeconomics , and Microeconomics , particularly examining agglomeration dynamics in economic growth, political economy influences on trade patterns, and socioeconomic determinants of educational and labor market outcomes. Her methodological approach combines theoretical modeling with empirical cross-country analysis, contributing to foundational debates in economic geography and policy-relevant labor market studies. Analysis of her publication trajectory reveals consistent exploration of spatial economic phenomena and institutional interactions, with notable contributions to understanding how market size effects shape firm location decisions and how educational tracking perpetuates intergenerational inequality. These investigations appear in high-impact venues including the Journal of Urban Economics and European Economic Review, reflecting rigorous interdisciplinary scholarship. Scientific recognition includes: No specific awards or fellowships documented in current institutional profiles Her academic mentorship and research funding activities remain unreported in available materials, though her executive-level teaching across Microeconomics and Macroeconomics courses indicates significant instructional responsibilities. Collaborative work occurs within the Institute of Economics and Econometrics framework, where she contributes to the department's research agenda on global economic systems and policy analysis. Dr. Sbergami maintains operational affiliation with the University of Geneva's Uni Mail facility (office 5267) and participates in the institution's broader research ecosystem through the Geneva School of Economics and Management's interdisciplinary initiatives.

Ravi Kiran Sarvadevabhatlaمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Human-Computer Interaction
• +۹ مورد دیگر

Ravi Kiran Sarvadevabhatla is an Associate Professor at the International Institute of Information Technology Hyderabad (IIIT-H), with additional roles as Lead (Applied Projects and Academic Programs) at iHub-Data Mobility. His research spans Computer Vision , Machine Learning , and Human-Robot Interaction , focusing on interdisciplinary challenges involving multimodal data (images, sketches, eye-tracking, audio). Research Themes : Computer vision projects in Document Image Understanding , Sketch Recognition , and Depiction-Invariant Object Recognition Cognitive modeling through Eye Tracking Analysis and Pictionary-inspired Models Human-robot interaction studies on Proxemics , Panoramic Attention , and Extended Interaction Analysis Technical Contributions : Pioneered Category-Epitome representations for sparse sketch recognition Developed SketchParse - a deep network for automated sketch parsing Created PPSS-12 benchmark for evaluating object recognition robustness

Nathanaël Fijalkowمشاهده پروفایل

پژوهشگر

• Program Synthesis
• Automata Theory
• Game Theory
• +۸ مورد دیگر

Nathanaël Fijalkow is a senior researcher at CNRS in LaBRI, Bordeaux, where he leads the Synthesis team. His work bridges program synthesis, games on graphs, automata theory, and applications in machine learning and formal verification. Research interests include: Program synthesis and code generation Game theory for algorithmic verification Linear Temporal Logic learning Probabilistic automata and dynamical systems Boolean network synthesis for biological modeling Recent publications focus on GPU-accelerated program synthesis, decidable classes of POMDPs, and optimal transformations in automata theory. He received the AAAI 2025 Outstanding Paper Award. He supervises PhD students and collaborators in the Synthesis team, working on projects like ANR ZADyG, ANR Shannon meets Cray, and PEPR IA SAIF. The team develops tools such as Scarlet and BoNesis for LTL learning and Boolean network analysis.

Craig Innesمشاهده پروفایل

پژوهشگر ارشد

• Cyber-Physical Systems
• Formal Verification
• Machine Learning
• +۴ مورد دیگر

Craig Innes is a Research Fellow at the University of Edinburgh , affiliated with the Institute for Perception, Action and Behaviour (IPAB) . His research bridges symbolic logic and formal verification with black-box probabilistic models in cyber-physical systems , focusing on trustworthiness , risk assessment , and safety guarantees for autonomous vehicles and robotics . His work explores temporal logic specifications , adaptive experiment design , and probabilistic calibration to improve safety validation . Key trends in his publications include formal verification for machine learning systems , physics-informed simulation for soft robotics , and risk-driven perception system design . He also investigates unawareness in decision-making and scenario generation via large language models . Craig supervises PhD students in areas like hybrid AI for cyber-physical systems and autonomous robotics , with funding opportunities through EPSRC Doctoral Training Partnerships and Centre for Doctoral Training programs in Dependable Robotics and Machine Learning Systems . He emphasizes research proposal development and collaboration with industry in his supervision approach.

• Natural Language Processing
• Machine Learning
• Uncertainty Estimation
• +۴ مورد دیگر

Wilker Aziz is an assistant professor at the Institute for Logic, Language and Computation (ILLC) within the Faculty of Science at the University of Amsterdam, where he leads the Probabilistic Language Learning research group. His academic journey includes a PhD from the University of Wolverhampton (2014), followed by research positions at the University of Sheffield and ILLC before joining the UvA faculty in January 2019. His research focuses on the intersection of machine learning, natural language processing, and probabilistic modeling. Aziz investigates how to design models and algorithms that learn to represent, understand, and generate language data, with particular emphasis on uncertainty estimation in language models, probabilistic inference techniques, and developing novel decoding algorithms for text generation. His work addresses fundamental problems in language modeling, machine translation, syntactic parsing, textual entailment, text classification, and question answering. Aziz's recent publications (2022-2024) demonstrate a strong focus on uncertainty in natural language generation, with multiple papers accepted at top-tier conferences including EACL and EMNLP. His research shows how language models can better align with human uncertainty patterns and how to properly evaluate model confidence when humans themselves disagree on annotations. Best paper award at Coling 2020 for 'Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation' Outstanding Area Chair award at ICLR 2022 Active program committee member for major ML/NLP conferences including NeurIPS, ICML, ACL, EMNLP, and ICLR Aziz actively supervises PhD and MSc students, with a current cohort of five ongoing PhD candidates and numerous recent graduates. His Probabilistic Language Learning group develops theoretical frameworks and practical applications for uncertainty-aware natural language processing. He has contributed to open-source projects including PET (Post-Editing Tool), grasp (Randomised Semiring Parsing), and mosesdecoder, demonstrating his commitment to advancing the field through shared research tools.

Luiz Chamonمشاهده پروفایل

استادیار

• Machine Learning
• Reinforcement Learning
• Constrained Optimization
• +۳ مورد دیگر

Luiz Chamon is an Assistant Professor (tenure-track) and Hi!PARIS Chair holder at the Center for Applied Mathematics (CMAP) , École polytechnique, Paris, France. His research bridges machine learning with control theory and signal processing, focusing on intelligent systems that extract, process, and act on information under constraints. Research Interests: He specializes in constrained learning frameworks for machine learning, with applications to reinforcement learning, statistical signal processing, and optimization. His work includes adversarial robustness, safe policy design, and duality analysis in constrained settings. Article Trends: Recent publications span differential equation solving (ICLR 2025), primal-dual sampling (NeurIPS 2024), and resilient reinforcement learning (IEEE Trans. Autom. Control 2024). Common themes include constrained optimization, robustness, and bridging theory with practical applications in wireless systems and graph signal processing. Scientific Awards: Hi!PARIS Chair (2025) ELLIS Scholar (2025) Best Student Paper Award (ICASSP 2020) Best Paper Award (ICASSP 2020) Top 50 Most Accessed Articles in IEEE TSP (2019) Grants & Projects: Currently leads a 4-year ANR JCJC project on "Beyond (Robust) Learning: Striking Compromises in Machine Learning" (2024). Previously involved in technical reports on aircraft vibroacoustics and wireless power systems. Labs & Teams: Heads a research group at École polytechnique developing tools for constrained learning, collaborating with institutions like University of Pennsylvania and IMPRS-IS. Organizes tutorials and panels on constrained learning and career development.

Manfred Opperمشاهده پروفایل

استاد

• Statistical inference
• Bayesian nonparametric models
• Statistical learning theory
• +۵ مورد دیگر

Manfred Opper serves as Professor of Computer Science at the School of Electrical Engineering and Computer Science (School IV EECS) of Technische Universität Berlin and holds a Fellowship at the Berlin Institute for the Foundations of Learning and Data (BIFOLD), a joint research institute with the Hasso Plattner Institute. His research program integrates statistical physics with machine learning, focusing on Bayesian nonparametric models, statistical learning theory, and stochastic dynamics on complex networks. Key themes include data assimilation for nonlinear systems, optimal stochastic control applications, and information-theoretic approaches to inference. This interdisciplinary framework bridges theoretical physics and computational intelligence. His recent publication analyzes stochastic control techniques for Bayesian neural network training, reflecting his signature approach of applying statistical physics principles to modern machine learning challenges. This work demonstrates convergence between control theory and probabilistic deep learning. Opper's recognition includes the German Physical Society's Physics Award for early-career contributions. Physics Award of German Physical Society (1992) As a BIFOLD Fellow, he contributes to foundational research in learning systems and data science. The available text does not specify advising activities, grant funding, or laboratory infrastructure details. His professional engagement extends to the German Physical Society (DPG), where he maintains active membership in theoretical physics and statistical mechanics communities.

David Ginsbourgerمشاهده پروفایل

استاد

• Statistics
• Machine Learning
• Uncertainty Quantification
• +۷ مورد دیگر

David Ginsbourger is a Professor and Head of Research Group at the Institute of Mathematical Statistics and Actuarial Science (IMSV) within the University of Bern, Switzerland. He maintains dual affiliations through his role at IMSV and as a member of the Multidisciplinary Center for Infectious Diseases (MCID), reflecting interdisciplinary engagement across statistical methodology and applied domains. His research program centers on advanced statistical methodologies with emphases on Gaussian process modeling, uncertainty quantification, and experimental design for computer experiments. Key contributions include novel kernel constructions for equivariant systems, sequential design strategies for excursion set estimation, and efficient computational frameworks for spatial distributional modeling. His work bridges theoretical statistics with practical applications in agriculture, chemoinformatics, environmental science, and risk assessment, demonstrating consistent innovation in handling complex prediction problems under uncertainty. Analysis of his 15 most recent publications (2024-2025) reveals persistent methodological development in Gaussian process theory alongside expanding application domains. Recurring themes include integration-free kernel design for structured data, rare event probability estimation, and multivariate forecast calibration. His research exhibits strong continuity in addressing computational challenges for large-scale inverse problems while increasingly incorporating domain-specific constraints from fields like molecular chemistry and agricultural science. Ginsbourger leads a dedicated research group at IMSV focused on advancing statistical frameworks for computer experiments and uncertainty quantification. The group maintains active collaborations across disciplines, particularly evident in recent work connecting statistical methodology to infectious disease modeling through MCID affiliations and agricultural optimization projects.

Clément Honglerمشاهده پروفایل

دانشیار

• Statistical mechanics
• Quantum field theory
• Deep learning theory
• +۴ مورد دیگر

Clément Hongler is an Associate Professor in the Chair of Statistical Field Theory at the School of Basic Sciences , École Polytechnique Fédérale de Lausanne (EPFL). He leads research at the intersection of statistical mechanics, quantum field theory, and deep learning theory. Education : B.Sc. (2006), M.Sc. (2008) in Mathematics from EPFL, and Ph.D. (2010) in Mathematics from Université de Genève. Career : Ritt Assistant Professor at Columbia University (2010-2014), Tenure-Track Assistant Professor at EPFL (2014-2018), and Associate Professor at EPFL since 2019. His research focuses on: Connections between lattice models and conformal field theories Dynamics of learning in neural networks Artificial life via continuous cellular automata Decentralized systems He actively teaches courses such as Probabilistic models of modern AI and participates in EPFL's doctoral programs, including Mathematics and EDMA (Environmental Data Science and Engineering). His lab ( CSFT ) collaborates on interdisciplinary projects bridging theoretical physics and machine learning.

Florian Brueckمشاهده پروفایل

پژوهشگر

• Statistical Model Comparison
• Non-Parametric Bayesian Analysis
• Lévy Processes
• +۳ مورد دیگر

Florian Brueck is a Postdoctoral Researcher at the Research Center for Statistics at the University of Geneva, under Prof. Sebastian Engelke. He earned his PhD in Mathematics (Dr. rer. nat.) from the Technical University of Munich (TUM) in 2023, with distinction, under Prof. Matthias Scherer. His research focuses on statistical model comparison, Bayesian non-parametric survival analysis, Lévy processes, extreme value theory, and the integration of machine learning into classical statistics. Education: PhD in Mathematics, TUM (2023) MSc in Financial Mathematics and Actuarial Sciences, TUM (2019) BSc in Business Mathematics, Ludwig Maximilians University Munich (2017) Research Interests: Statistical model selection via Maximum Mean Discrepancy (MMD) Exchangeability and non-parametric Bayesian methods in survival analysis Inference for Lévy and stable processes Extreme value theory applications in risk modeling Machine learning techniques for statistical inference Key Contributions: Developed distribution-free MMD tests for model selection with estimated parameters Advanced infinitely divisible priors for multivariate survival functions Applied generative neural networks to characteristic function estimation Awards & Recognition: None explicitly listed. Advising & Grants: Supervised 5 Master’s theses on topics including MMD-based model selection and clustering-based portfolio optimization Co-led industry project with WWK Versicherungen on risk assessment tools Labs/Teams: Research Center for Statistics at University of Geneva.

Sergey Litvinovمشاهده پروفایل

پژوهشگر

• Computational Mechanics
• Biomedical Engineering
• Industrial Applications
• +۶ مورد دیگر

Sergey Litvinov is a Researcher at ETH Zürich, affiliated with the Department of Structural Mechanics and Monitoring within the College of Civil, Environmental and Geomatic Engineering. His work focuses on computational and statistical models for biomedical and industrial applications, leveraging tools like C++, PyTorch, and cloud computing. He contributes to hierarchical Bayesian analysis on big data and probabilistic programming, emphasizing data-driven methodologies. Current projects include the DCoMEX initiative (Data-Driven Computational Mechanics at Exascale), aiming to advance large-scale computational frameworks for structural mechanics and monitoring. His research interests span computational mechanics, fluid dynamics, and machine learning applications, with a strong emphasis on hybrid numerical methods and high-performance computing for multiphase systems. He has no listed scientific awards or advisees. His contributions are centered on software development (e.g., Aphros and Mirheo ) for multiphase flow simulations and novel optimization techniques for inverse problems, particularly in biomedical and engineering contexts. Recent articles highlight his work on discrete loss optimization, reinforcement learning for flow control, and data-driven methods for tumor growth modeling and drug delivery systems. He is part of interdisciplinary teams addressing challenges in microfluidics, turbulence modeling, and biomedical applications, with a focus on scalable solutions for complex systems. His projects often involve collaboration with industrial and academic partners on computational models for real-world applications.