جستجوی استاد راهنمای دانشگاهی

• 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.

Laura Grigoriمشاهده پروفایل

استاد

• Numerical Linear Algebra
• High Performance Computing
• Deterministic and Randomized Algorithms
• +۴ مورد دیگر

Laura Grigori is a Full Professor and Chair of High Performance Numerical Algorithms and Simulations at EPFL's School of Basic Sciences (SB) Department of Mathematics (MATH). Her research focuses on numerical linear algebra, high performance computing, and tensor computations, with applications in astrophysics and molecular simulations. She leads the HPNalgs lab and teaches courses in numerical analysis and HPC. Her awards include the SIAM Supercomputing Career Prize (2024) and SIAM Fellow distinction (2020). She advises four PhD students and has authored numerous papers on communication-avoiding algorithms, randomized methods, and parallel linear algebra techniques. Her work addresses scalability challenges in scientific computing and large-scale data analysis. Labs/Teams: HPNalgs Lab (https://www.epfl.ch/labs/hpnalgs/) Grants: ERC Synergy Grant (2019) for Extreme-scale Computational Chemistry

Antti Knowlesمشاهده پروفایل

استاد

• Probability Theory
• Mathematical Physics
• Random Matrices
• +۶ مورد دیگر

Antti Knowles is a Full Professor at the University of Geneva , Section of Mathematics. His research lies at the intersection of probability theory, mathematical physics, and analysis, with a strong focus on random matrices, random graphs, and quantum dynamics. He leads the research group Analysis, Mathematical Physics and Probability and mentors postdocs and doctoral students. Research Interests: Prof. Knowles's work spans a wide range of topics including random matrices , random graphs , statistical mechanics , stochastic processes , high-dimensional statistics , quantum field theory , and quantum dynamics . His contributions are foundational in understanding spectral properties of complex systems and their physical implications. Publications: His recent publications demonstrate a consistent focus on spectral theory of random graphs and matrices, delocalization phenomena, and quantum statistical mechanics. These works often appear in top-tier journals such as Communications in Mathematical Physics , Annals of Probability , and Journal of the European Mathematical Society . Grants & Support: He has received significant funding from the European Research Council (ERC) and the Swiss State Secretariat for Education, Research and Innovation through projects RandMat (2017–2022) and ProbQuant (2022–2027). Editorial Work: Prof. Knowles serves on the editorial boards of Annales de l’Institut Fourier , Annals of Applied Probability , L'Enseignement Mathématique , and Journal of Statistical Physics .

Hong-Quan Tranمشاهده پروفایل

پژوهشگر ارشد

• Probability Theory
• Mixing times of Markov processes
• Random Matrix Theory

Hong-Quan Tran is a Postdoctoral Scholar at the University of Geneva, working under Professor Antti Knowles in mathematical research. He holds a PhD in Mathematics from Paris Dauphine University, supervised by Justin Salez. Education: PhD in Mathematics, Paris Dauphine University Research Interests: Dr. Tran specializes in Probability Theory with a focus on Mixing times of Markov processes. His recent work extends into Random Matrix Theory, exploring theoretical frameworks for stochastic processes and spectral properties of random matrices. This research bridges foundational probability with applications in statistical physics and data science. Scientific Awards: None mentioned. Advising and Grants: No information available regarding student supervision or research funding. Laboratory and Team: Dr. Tran is affiliated with Antti Knowles' research group at the University of Geneva, contributing to collaborative projects in probabilistic mathematics.

Olivier Lévêqueمشاهده پروفایل

پژوهشگر

• Information Theory
• Random Matrices
• Stochastic Calculus
• +۲ مورد دیگر

Olivier Lévêque is a Senior Scientist at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Computer and Communication Sciences. He conducts research at the Laboratory of Information Theory (LTHI) and holds teaching responsibilities in both Communication Systems (SSC) and Computer Science (SIN) sections. Additionally, he contributes to the Interface EPFL-Gymnases initiative. Lévêque obtained his Physics diploma (1995) and PhD in Mathematics (2001) from EPFL, with a visiting lectureship at Stanford University's Electrical Engineering Department in 2005-2006. His research explores fundamental aspects of information theory , random matrices , and stochastic calculus , with applications in wireless communications and network theory. Key interests include capacity scaling laws in ad hoc networks, diversity-multiplexing tradeoffs, and mathematical frameworks for communication systems. Recent publications demonstrate broad interdisciplinary engagement, spanning computational thinking assessment (2022), digital education frameworks (2019), satellite positioning systems (2018), and theoretical advances in probability (2018). His work consistently integrates mathematical rigor with practical communication challenges, particularly in wireless network optimization and information-theoretic security. He has supervised four doctoral students at EPFL and teaches courses including Information, Computation, Communication , Markov Chains and Algorithmic Applications , and Cryptography . Lévêque leads research activities within the Laboratory of Information Theory, focusing on theoretical foundations of modern communication systems.