جست‌وجوی استاد و پژوهشگر

• Data Science
• Computational Statistics
• Bayesian Models
• +۵ مورد دیگر

Antonietta Mira is a Full Professor of Statistics at the University of Italian Switzerland (USI), where she also serves as the founder and director of the Data Science Lab. She holds a part-time professorship at Università dell'Insubria and has held visiting roles at institutions such as the University of Cambridge and Université Paris-Dauphine. Her academic leadership includes a Vice-Dean role in the Faculty of Economics (2013-2015). She earned a PhD in Computational Statistics from the University of Minnesota (1998), a Master's in Statistics (1996), and a Doctorate in Methodological Statistics from the University of Trento (1995). Her research focuses on Bayesian statistical models, computational statistics, and interdisciplinary applications in healthcare, finance, and social sciences. Mira is a Fellow of the International Society for Bayesian Analysis (ISBA) and the Istituto Lombardo Accademia di Scienze e Lettere. Her work has been recognized with awards for both research and teaching excellence. She leads Swiss National Science Foundation-funded projects and serves on editorial boards of journals like Bayesian Analysis and Statistica Sinica . Her publications span topics such as approximate Bayesian computation (ABC), spatio-temporal modeling of health events, and data science applications in public policy. Mira also engages in public outreach, including scientific exhibitions like Numbed by Numbers! and lectures on data-driven societal issues.

Ehsan Rahmatizad Khajehpashaمشاهده پروفایل

پژوهشگر ارشد

• Computational Physics
• Materials Science
• Machine Learning
• +۳ مورد دیگر

Ehsan Rahmatizad Khajehpasha is a Research Fellow at the University of Basel's Department of Physics, part of the Computational Physics Group led by Prof. Stefan Goedecker. He holds a PhD in Computational Physics from the Institute for Advanced Studies in Basic Sciences (Zanjan, 2016–2021), a Master's in Solid-state Physics from the University of Zanjan (2014–2016), and a Bachelor's in Solid-state Physics from the Iran University of Science and Technology (2010–2014). His research focuses on advancing computational methods for atomistic simulations, including machine learning potentials, charge equilibration techniques, and structure prediction algorithms. Key projects include the development of the FLAME library for atomistic modeling environments and contributions to the Minima Hopping algorithm for materials discovery. Recent work emphasizes entropic contributions in phase transitions and improving the accuracy of density functional theory calculations using surface integral approaches. Publications highlight advancements in computational physics, such as optimizing geometry calculations, accelerating machine learning potentials, and analyzing free energy landscapes in molecular crystals. Collaborations span interdisciplinary topics like bioinformatics (e.g., predicting BRCA gene variants) and agricultural genomics (vernalization gene prediction). He is affiliated with the Philosophisch-Naturwissenschaftliche Fakultät and actively contributes to the Computational Physics Group's projects in materials science and quantum chemistry.

Oliver Yibing Chénمشاهده پروفایل

استادیار

• Statistics
• Machine Learning
• Neuroscience
• +۱۱ مورد دیگر

Oliver Yibing Chén is an Assistant Professor with conditional pre-tenure at the associate professor level at the University of Lausanne (UNIL), Faculty of Biology and Medicine, based at the Department of Immunology and Allergy of CHUV. His research bridges statistics, machine learning, and neuroscience, focusing on biomarker discovery for conditions such as Alzheimer's disease, Parkinson’s disease, multiple sclerosis, psychosis, and sleep disorders. He leads two teams: Beyond bio-X for methodological research and Alpha Analytika for translating innovations into affordable medical devices and free health applications. Education: PhD in Engineering, University of Oxford Master in Biostatistics, Johns Hopkins University Master in Mathematical Statistics, University of Washington Research Associate in Psychology, Yale University Research Assistant in Theoretical Statistics, Northwestern University Honorary Fellow in Neurobiology, University College London His research focuses on developing novel statistical and machine learning frameworks for high-dimensional biomedical data, particularly in neuroimaging and digital phenotyping. His work emphasizes explainability, data fusion, longitudinal modeling, and clinical translation. He has previously held an Assistant Professor position in Artificial Intelligence and Big Data Analytics at the University of Bristol, where he attained tenure in 2022 before moving to Switzerland in 2023. The most recent publications highlight a strong trend in multimodal learning, explainable AI for neurodegenerative diagnosis, brain connectivity analysis, and digital health applications. Key themes include Alzheimer’s disease prediction, sleep staging, Parkinson’s monitoring via smartphones, and statistical methodology for high-dimensional mediation and multiple testing. His work frequently involves collaboration with clinical and neuroscience research groups. Scientific Contributions: Development of methods for brain variability analysis Innovations in multimodal data fusion (e.g., VisTA, OPTIMUS) Advancements in sleep staging using deep learning (e.g., SleepTransformer, SeqSleepNet) Statistical frameworks for digital health and remote disease monitoring Foundational work on high-dimensional mediation and causal inference in neuroscience Dr. Chén mentors PhD students and emphasizes the growth and development of his advisees. He has collaborated extensively with researchers across institutions and disciplines. His teams work on both theoretical advancements and practical implementations, aiming to make healthcare more accessible through technology. He also engages in watercolor and oil painting as a personal pursuit. Research Teams: Beyond bio-X : Focuses on research in statistical and machine learning theory and applications. Alpha Analytika : Translates research into affordable medical devices and free health apps.

Taiji Suzukiمشاهده پروفایل

دانشیار

• Machine Learning
• Statistical Learning Theory
• Deep Learning
• +۶ مورد دیگر

Prof. Taiji Suzuki is an Associate Professor at the University of Tokyo in the Department of Mathematical Informatics . He also serves as Team Leader of the "Deep Learning Theory" team at AIP-RIKEN , Japan. With a PhD in Information Science and Technology from the University of Tokyo (2009), he has held academic positions at the University of Tokyo (2009-2013) and Tokyo Institute of Technology (2013-2017) before returning to the University of Tokyo in 2017. University of Tokyo (2004: BEng in Mathematical Engineering) University of Tokyo (2006: MSc in Information Science and Technology) University of Tokyo (2009: PhD in Information Science and Technology) His research interests span statistical learning theory , deep learning , kernel methods , sparse estimation , and stochastic optimization . He investigates how neural networks adapt to function smoothness, avoid the curse of dimensionality, and achieve global optimization through mean-field dynamics. His work bridges theoretical guarantees (minimax optimality, convergence analysis) with practical implementations (transformers, graph neural networks, diffusion models). The 15 most recent articles focus on transformers' representation power, graph neural networks' limitations, diffusion models' convergence, and optimization theories for deep learning. Key themes include information-theoretic bounds , feature learning dynamics , and mean-field analysis . He explores applications in AI for medicine, federated learning, and biomedical modeling. Awards & Recognition: Outstanding Paper Award, ICLR 2021 MEXT Young Scientists’ Prize Outstanding Achievement Award, Japan Statistical Society 2017 Outstanding Achievement Award, Japan Society for Industrial and Applied Mathematics 2016 Best Paper Award, IBISML 2012 Best Paper Candidate, ICDM 2019 He has served as Area Chair for NeurIPS, ICML, ICLR, AISTATS, and as Program Chair for ACML. His scientific contributions include convergence theories for stochastic gradient methods, minimax analysis of deep learning vs kernel methods, and novel frameworks for distributional optimization in diffusion models.

Frédéric Milaمشاهده پروفایل

استاد

• Condensed Matter Theory
• Quantum Magnetism
• Frustrated Spin Systems
• +۵ مورد دیگر

Frédéric Mila is a full Professor and Chair of Theoretical Condensed Matter Physics at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Basic Sciences (SB), Institute of Physics (IPHYS), and the Condensed Matter Theory Group (CTMC). He also holds roles in the Doctoral Program in Physics (EDPY) and the School of Physics and Engineering (SPH-ENS). His research centers on quantum magnetism, frustrated systems, and strongly correlated electrons. His research interests include quantum magnetism, frustrated spin systems, quantum phase transitions, tensor network methods, and quantum spin liquids . He employs both analytical approaches and advanced numerical techniques to study complex quantum phenomena in low-dimensional and geometrically frustrated materials. His recent publications reveal a strong focus on magnetization plateaus, quantum dimer models, spin liquids, and high-field quantum phases , particularly in compounds like SrCu2(BO3)2 and theoretical models such as the Shastry-Sutherland and triangular lattice antiferromagnets. His work often bridges theory with experimental findings from neutron scattering and high-field measurements. Scientific Awards: Charpak-Ritz Prize (2024) – Awarded by the French and Swiss Physical Societies. Advising and Grants: Prof. Mila actively mentors a large group of doctoral students and has supervised numerous PhD theses at EPFL. His students have worked on diverse topics including SU(N) spin chains, tensor networks, frustrated magnetism, and quantum Monte Carlo methods. His sustained publication record in top journals indicates continuous funding and active research leadership. Labs and Teams: He leads the Condensed Matter Theory Group (CTMC) at EPFL, which focuses on theoretical and computational aspects of quantum materials. The group collaborates closely with experimentalists and uses state-of-the-art numerical tools to investigate quantum phases of matter.

João Miguel Penedonesمشاهده پروفایل

دانشیار

• Theoretical Physics
• Quantum Field Theory
• String Theory
• +۷ مورد دیگر

João Miguel Penedones is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Basic Sciences, Institute of Physics, and leading the Fields and Strings Laboratory (FSL). He also holds a role in the School of Physics and Chemistry (SPH) and serves on the executive committee of the Center for Imaging (CIB). His research focuses on foundational aspects of theoretical physics, particularly in quantum field theory, string theory, and the AdS/CFT correspondence. His work emphasizes nonperturbative methods, including the S-matrix bootstrap, scattering amplitudes, and constraints from physical principles such as unitarity and analyticity. The article titled Nonperturbative aspects of scattering amplitudes (2023) reflects current research trends in his group, exploring deep analytical structures in quantum field theories, Landau singularities, anomalous thresholds, and connections to conformal field theories. This highlights a strong focus on rigorous mathematical frameworks for understanding quantum scattering beyond perturbation theory. He has supervised several PhD students, including Alves Da Silva João Pedro, Hebbar Aditya, Häring Kelian Philippe, Marucha Jan Krzysztof, Ribeiro Correia Miguel Alexandre, and Salehi Vaziri Kamran. His current doctoral candidates are Armanini Elisabetta, Loparco Manuel, Martina Adrien Philippe, and Vuignier Antoine. There is no mention of external grants, but his leadership of a dedicated research lab suggests active research funding. He is involved in academic leadership through his membership in the executive committee of the Center for Imaging (CIB), contributing to institutional governance at EPFL.

Nicolas Macrisمشاهده پروفایل

استاد مدعو

• Statistical Mechanics
• Information Theory
• Error Correcting Codes
• +۳ مورد دیگر

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 .

• Condensed Matter Physics
• Computational Physics
• Quantum Many-Body Systems
• +۲ مورد دیگر

Philipp Werner is a Full Professor at the Department of Physics, Faculty of Mathematics, Natural Sciences and Medicine, University of Fribourg. His research focuses on quantum many-body systems, ultrafast phenomena, and strongly correlated materials. Affiliation: University of Fribourg Academic Rank: Professor Contact: philipp.werner@unifr.ch Research Interests: Werner's work explores the interplay of electron correlations, spin-orbit interactions, and ultrafast dynamics in quantum materials. He develops advanced computational methods like tensor cross interpolation and dynamical mean-field theory to study systems such as Mott insulators, Hubbard models, and transition metal compounds. Recent Publications: His recent studies investigate topics including high-harmonic generation in quantum systems, spin-orbit driven nonlinear dynamics, and hidden Kondo lattice physics. These works often involve nonequilibrium simulations and time-resolved spectroscopic techniques.

• Graph Machine Learning
• Signal Processing
• Higher-Order Networks
• +۵ مورد دیگر

Elvin Isufi is an Associate Professor at the Delft University of Technology (TU Delft) , where he co-founded and co-directs AIdroLab , one of the 24 TU Delft AI Labs. His research focuses on fundamental and applied graph-based data processing with applications to water networks, flood modeling, infrastructure systems, and recommender systems. Elvin obtained his Ph.D. from TU Delft in graph signal processing and completed his master's and bachelor's studies at the University of Perugia in Italy. He has been mentored by renowned academics including Prof. Alejandro Ribeiro (postdoc), Prof. Geert Leus (Ph.D.), and Prof. Paolo Banelli (master's/bachelor's). His research integrates signal processing , machine learning , and mathematical modeling to develop techniques for graph signal processing, graph neural networks (GNNs), and higher-order network analysis. Key application domains include water distribution networks , flood modeling , and recommender systems . His work addresses critical challenges such as stability analysis of GNNs, dynamic graph processing , and physics-informed machine learning . Recent publications highlight advancements in multi-scale hydraulic GNNs for flood prediction, carbon footprint-aware recommender systems , and simplicial vector autoregressive models for edge flow analysis. His research group includes Ph.D. students like Bishwadeep Das and M.Sc. students exploring topics such as online edge flow prediction and topological signal processing . 2021: Audience Choice Award, IEEE Data Science and Learning Workshop 2022: Best Student Paper Award, IEEE DSLW 2023: Top 3% Recognition Award, ICASSP Elvin actively supervises students in graph machine learning projects, emphasizing requirements like Python and PyTorch/TensorFlow expertise . He provides structured thesis project themes covering dynamic graph analysis , physics-informed GNNs , and self-supervised learning for networked data.

• Machine Learning
• Economics/Blockchain
• Mathematical Physics
• +۸ مورد دیگر

Franck Gabriel is an Associate Professor at University Claude Bernard Lyon 1 , affiliated with the Institut de Science Financière et d'Assurances (ISFA) . His research bridges Machine Learning , Economics/Blockchain , Mathematical Physics , and Random Matrices , with notable work on neural tangent kernels, DeFi protocols, and asymptotic matrix theory. Research Focus : Machine Learning: Theoretical analysis of neural networks, kernel methods, and generalization bounds. Blockchain Economics: Decentralized finance, staking mechanisms, and smart contract design. Mathematical Physics: Holonomy fields, Yang-Mills theory, and random matrix asymptotics. Recent Publications highlight trends in denoising diffusion models, free probability in matrix theory, and DeFi credit systems. His work often integrates cross-disciplinary approaches, merging deep learning with financial technology and quantum field theory. Scientific Awards : 2025 AI 2000 Most Influential Scholar Award in Theory 2024 AI 2000 Most Influential Scholar Award in Theory 2023 AI 2000 Most Influential Scholar Award in Theory As an organizer of the ISFA Seminar , he fosters interdisciplinary discussions in insurance, economics, and machine learning. His collaborations span institutions like Ecole Polytechnique Fédérale de Lausanne, Courant Institute, and EPFL.

Titus Mangham-Neupertمشاهده پروفایل

استاد

• Condensed Matter Physics
• Topological Quantum Matter
• Strongly Correlated Electron Systems
• +۷ مورد دیگر

Prof. Dr. Titus Mangham-Neupert is Full Professor of Theoretical Physics at the University of Zurich and co-director of its Digital Society Initiative . He leads the Theory of Quantum Matter group within the Department of Physics, Faculty of Science, and sits on several strategic boards including the Digital Strategy Board, Graduate Campus advisory board, and the Pauli Center board. Education & Career 2025–present: Full Professor, Department of Physics, University of Zurich 2021–present: Co-director, Digital Society Initiative, University of Zurich 2018–2025: Associate Professor, Department of Physics, University of Zurich 2016–2018: Assistant Professor, Department of Physics, University of Zurich 2013–2016: Postdoctoral Fellow, Princeton Center for Theoretical Science, Princeton University 2010–2013: PhD in Physics, ETH Zurich (advisors: Prof. Manfred Sigrist, Dr. Christopher Mudry) 2009–2010: Visiting Scientist, RIKEN, Japan 2007–2009: MSc in Physics, University of Zurich 2005–2007: Undergraduate studies, Dresden University of Technology Research Interests Neupert’s work straddles condensed-matter physics, quantum materials, topology, and machine learning . His group develops variational and tensor-network approaches to strongly-correlated electrons, explores fractional topological phases , and investigates kagome metals, moiré graphene, and unconventional superconductors . A growing focus is the deployment of neural-network quantum states and AI-assisted data analysis to uncover emergent quantum phenomena. Grants & Funding ERC Starting Grant “PARATOP” Swiss National Science Foundation project grants Member of the Swiss MaNEP network Scientific Awards & Honors Clarivate Highly Cited Researcher (2020-2024) Outstanding Referee, American Physical Society (2019) Klung-Wilhelmy Science Award (2019) ETH Medal for PhD thesis (2013) Swiss Physical Society Dissertation Prize (2013) Professional Service & Memberships Member, Editorial Board, Physical Review B (2020-2026) Member, Digital Strategy Board, University of Zurich Advisory boards: Graduate Campus UZH, Science Lab UZH & Science Pavilion UZH Board member, Pauli Center (UZH & ETH Zurich) Member, American Physical Society

Yosuke Morishimaمشاهده پروفایل

مدرس ارشد

• Neuropsychiatry
• Neuroimaging
• Neuromodulation Techniques
• +۵ مورد دیگر

PD Dr. Yosuke Morishima is a Senior Lecturer and Group Leader at the University Psychiatric Clinics Basel (UPK Basel) under the University of Basel. His research focuses on neuropsychiatric disorders, neuroimaging techniques, and neurophysiological interventions. He specializes in studying schizophrenia, psychosis risk, self-experience disorders, and neurobiological mechanisms of mental health conditions through advanced electrophysiological (ERP/EEG) and neuroimaging (fMRI, DTI) methods. Key research interests include: Neural correlates of self-consciousness and pre-reflective experience Clinical applications of transcranial stimulation (tACS, TMS, tDCS) Neuroimaging of brain connectivity in psychosis and depression Neurobiological basis of social emotions like embarrassment Pharmacological and immunological factors in mental disorders His work integrates computational neuroscience, clinical psychiatry, and neurotechnology to develop novel diagnostic markers and therapeutic interventions. Recent studies explore sleep modulation for depression and phase-synchronized neurostimulation techniques. As a Group Leader, he oversees projects on: Multimodal neuroimaging in psychiatric populations Neural network dysfunction in high-risk psychosis Closed-loop sleep interventions His team collaborates across disciplines, combining electrophysiology, MRI, and behavioral modeling to advance understanding of mental health disorders.

David Bommesمشاهده پروفایل

استاد

• Computer Graphics
• Geometry Processing
• Hexahedral Meshing
• +۴ مورد دیگر

Prof. David Bommes is a Professor and Head of the Computer Graphics Group (CGG) at the University of Bern's Institute of Computer Science. He specializes in geometry processing, with a focus on hexahedral and quad mesh generation, volumetric mapping, and parametrization quantization. His work bridges theoretical computer science and practical applications in computational geometry, geology, and engineering. Research Interests: His key areas include hex meshing, frame field synthesis, topology optimization, and robust mesh construction. He explores methods to generate high-quality meshes for simulations and modeling, often addressing challenges in non-manifold structures and boundary layer representations. Recent advancements include progressive embedding for tetrahedral maps and quantization techniques for hexahedral meshes. Publications: Over 20 publications since 2004 cover topics like parametrization quantization, subdivision surfaces for geological modeling, and automatic differentiation tools. His 2023 work on expansion cones and 2022 survey on hex-mesh processing highlight his leadership in this domain. Labs/Teams: Directs the CGG, a research group advancing computational geometry and graphics. Collaborates on projects involving mesh generation, geometric algorithms, and interdisciplinary applications in geology and engineering.

• Structural Biology
• Computational Imaging
• Microtubule Dynamics
• +۲ مورد دیگر

Dr. Michel Steinmetz is Associate Professor at EPFL and heads the Computational X-ray Imaging group at PSI. His research develops advanced imaging algorithms for synchrotron applications, pioneering 3D nanoscale techniques including magnetization vector nanotomography and scattering tensor tomography. Steinmetz's work reveals microtubule organization principles and creates new anti-infective therapeutics. He combines structural biology with computational methods to investigate microtubule dynamics and protein interactions. Recent innovations include photoswitchable microtubule inhibitors for optical cytoskeleton control and methods for high-resolution imaging of integrated circuits.

Maria Giulia Pretiمشاهده پروفایل

مدرس

• Neuroimaging
• Brain Connectivity
• Alzheimer's Disease
• +۳ مورد دیگر

Maria Giulia Preti is a Senior Scientist at MIPLAB, Research Staff Scientist at the CIBM Center for Biomedical Imaging (Switzerland), and a Lecturer (Maître-Assistante) at the University of Geneva. She holds a PhD in Bioengineering from Politecnico di Milano (2013) and completed postdoctoral research in Dimitri Van De Ville’s group at EPFL. Her work focuses on integrating neuroimaging techniques (fMRI, DTI, EEG, fNIRS) with graph signal processing to study brain function-structure relationships in health and disease. Key clinical applications include Alzheimer’s disease, epilepsy, multiple sclerosis, and stroke. She has pioneered methods like groupwise fMRI-guided tractography to study neurodegenerative pathologies. Education: PhD in Bioengineering (2013), Politecnico di Milano MSc in Biomedical Engineering (2009), Politecnico di Milano BSc in Biomedical Engineering (2007), Politecnico di Milano Research emphasizes multimodal neuroimaging integration and computational neuroscience. Notable achievements include developing connectome embedding techniques and demonstrating brain fingerprinting via spectral signatures. She received the Progetto Rocca fellowship (2011) supporting her work at MIT/Harvard Medical School. Ongoing projects investigate structure-function coupling dynamics, drowsiness markers, and DBS mechanisms in depression. Labs/Teams: MIP:Lab (part of the Van De Ville group at EPFL and University of Geneva collaborations).