جستجوی استاد راهنمای دانشگاهی

• Information Acquisition
• Mechanism Design
• Stochastic Orders
• +۳ مورد دیگر

Xiaosheng Mu is an Associate Professor in the Department of Economics at Princeton University. He previously held positions as an Assistant Professor of Economics at Columbia University and a post-doctoral researcher at the Cowles Foundation at Yale University. He earned his Ph.D. in Economics from Harvard University, advised by Drew Fudenberg, Eric Maskin, and Tomasz Strzalecki. His research focuses on information acquisition, mechanism design, stochastic orders, and their applications to decision making. Key contributions include work on pricing strategies under informational robustness, privacy-preserving auctions, and market dynamics on global e-commerce platforms. His work bridges theoretical economics with practical applications in algorithm design, privacy mechanisms, and behavioral analysis. Xiaosheng has published in top journals such as Econometrica, the Quarterly Journal of Economics, and the Journal of Political Economy. Notable publications include 'Monotone Additive Statistics' (Econometrica, 2024) and 'Algorithmic Design: A Fairness-Accuracy Frontier' (Journal of Political Economy, 2024). His research often employs advanced mathematical techniques from probability theory, game theory, and optimization. He has collaborated extensively with scholars such as Annie Liang, Vasilis Syrgkanis, and Luciano Pomatto. Current research projects explore information friction in digital markets and the design of privacy-preserving economic mechanisms. His work consistently addresses foundational questions in economics while maintaining relevance to modern technological challenges.

Lampros Gavalakisمشاهده پروفایل

پژوهشگر

• Information Theory
• Probability
• Mathematical Statistics
• +۷ مورد دیگر

Lampros Gavalakis is a Postdoctoral Research Associate at the Department of Pure Mathematics and Mathematical Statistics (DPMMS) at the University of Cambridge, affiliated with the INFORMED AI Hub. Previously, he was a postdoctoral fellow of the MathInGreaterParis programme (2022–2024), funded by the Marie Sklodowska-Curie Actions at Gustave Eiffel University. He holds a Ph.D. from the University of Cambridge’s Engineering Department, where he worked in the Signal Processing and Communications Laboratory, and a MEng in Computer Science from the Athens University of Economics and Business. His research focuses on information theory and probability, with emphasis on entropy power inequalities, entropic limit theorems, and their applications to data compression, communications, and convex analysis. Key interests include discrete/continuous entropy monotonicity, finite blocklength analysis, and non-asymptotic bounds. He has contributed to foundational work on de Finetti theorems, Gaussian mixture analysis, and entropy-convexity relationships. Lampros has published extensively in top venues like IEEE Transactions on Information Theory and the International Symposium on Information Theory (ISIT), earning the 2023 IEEE Jack Keil Wolf Student Paper Award. His academic collaborations span institutions including the Signal Processing and Communications Lab (Cambridge) and the INFORMED AI Hub. Education : PhD in Engineering (University of Cambridge, 2022) MEng in Computer Science (Athens University of Economics and Business, 2017) Awards : IEEE Jack Keil Wolf ISIT Student Paper Award (2023) Teaching : Lecturer for Concentration Inequalities (Part III Mathematics, Cambridge, 2025) His research bridges probability theory and information science, with applications to coding, stochastic processes, and high-dimensional analysis.

Gersende Fortمشاهده پروفایل

پژوهشگر

• Stochastic Approximation
• Bayesian Statistics
• Machine Learning
• +۳ مورد دیگر

Gersende Fort is a CNRS Senior Researcher affiliated with the Institut de Mathématiques de Toulouse (IMT) at the University of Toulouse. Her research focuses on stochastic approximation methods, Bayesian statistics, optimization algorithms, and computational statistics. She has presented at major conferences such as ICASSP 2025 (Suzhou & Hyderabad) and the French-German-Spanish conference on Optimization (Gijon, 2024), often collaborating with researchers like Eric Moulines and Hoi To Wai. Recent work includes developing sampling techniques for nonsmooth log-concave densities, hierarchical Bayesian models for epidemiological analysis (e.g., COVID-19 reproduction number estimation), and federated learning algorithms. She leads the MAD project funded by the French National Research Agency (ANR), advancing scalable optimization methods. Her publications span technical reports on stochastic proximal-gradient algorithms, fluid-limit-based MCMC tuning, and PLS classification in microarray data analysis. Fort actively engages in academic outreach, including the 'AI and Society' Summit in Paris (2025) and a workshop on mathematics of machine learning. She also contributed to educational initiatives like the Women and Mathematics event in Lavelanet (2024). Her research bridges theoretical foundations with applications in health, optimization, and machine learning.

Nima Anariمشاهده پروفایل

استادیار

• Sampling Algorithms
• Markov Chains
• Parallel Algorithms
• +۲ مورد دیگر

Nima Anari is an Assistant Professor of Computer Science at Stanford University and member of the Theory Group. He holds a Ph.D. in Computer Science from UC Berkeley (2015) and dual B.Sc. degrees in Computer Engineering and Mathematics from Sharif University of Technology (2010). His research focuses on theoretical computer science with emphasis on sampling algorithms, Markov chains, and combinatorial optimization. Research explores parallel sampling methods, log-concave polynomials, high-dimensional expanders, and applications in machine learning. Recent publications develop efficient algorithms for determinantal point processes, diffusion models, and optimization on discrete domains. Honors include the Michael and Sheila Held Prize (2025), Frontiers of Science Award (2023), Sloan Research Fellowship (2021), NSF CAREER Award (2021), and Best Paper Award at STOC 2019. He mentors 13 graduate and undergraduate students in theoretical computer science.

• Non-uniform Random Number Generation
• Graph Theory
• Nodal Sets
• +۲ مورد دیگر

Josef Leydold is Associate Professor of Statistics and Mathematics at Vienna University of Economics and Business. His research develops computational methods for random variate generation and graph theoretical applications. Research Areas: Design of black-box algorithms for non-uniform random numbers; geometric properties of graph Laplacians; spherical harmonics applications; and optimization techniques. Publications: Focus on practical algorithms for statistical computing, including monographs on automatic random variate generation and mathematical foundations for economists. No awards or student supervision details are documented.

Wuchen Liمشاهده پروفایل

استادیار

• Transport information geometry
• Complex dynamical systems
• Partial differential equations (PDEs)
• +۸ مورد دیگر

Wuchen Li is an Assistant Professor in Mathematics at the University of South Carolina , specializing in Transport information geometry and its applications across Complex Dynamical systems, PDEs, Statistics, Optimization, Control and Games, Mathematical Data science, Graphs and Neural networks , and Scientific Computations . His work bridges theoretical mathematics with practical algorithms for machine learning, Bayesian inference, and optimal transport problems. His research explores geometric frameworks for probability spaces, including Wasserstein-2 metrics , Onsager gradient flows , and primal-dual hybrid gradient algorithms . Recent publications focus on accelerated sampling methods, mean field control systems, and novel applications of optimal transport in high-dimensional settings. 2022 : Air Force Office of Scientific Research YIP award for Transport Information Geometric Computations Key article trends include stochastic differential equations (37%), Wasserstein gradient flows (42%), Markov chain Monte Carlo (28%), and Hamilton-Jacobi-Bellman equations (33%). Subfields span accelerated optimization , nonlinear mobility metrics , generative modeling , and reaction-diffusion systems .

• Combinatorics
• Computer Science
• Log-concave Polynomials
• +۳ مورد دیگر

Jonathan Leake is an Assistant Professor in the Department of Combinatorics and Optimization at the University of Waterloo . His research focuses on log-concave polynomials and their applications in combinatorics and computer science. Education: PhD in Mathematics, UC Berkeley (2014-2019) MS in Mathematics, Texas A&M University (2010-2012) BS in Computer Engineering and Applied Math, Texas A&M University (2006-2010) Research Interests: His work centers around log-concave polynomials and their connections to combinatorics, optimization, and computer science. Key areas include: Lorentzian polynomials and their applications Polynomial capacity and optimization Sampling algorithms and combinatorial structures Representation theory and algebraic combinatorics Scientific Awards: Dirichlet Postdoctoral Fellowship (TU Berlin, 2020-2022) James H. Simons Fellowship (Simons Institute, UC Berkeley, Spring 2019) Previous Positions: Postdoc Fellowship, Institut Mittag-Leffler, Stockholm (Spring 2020) Postdoc, KTH, Stockholm (Fall 2019) Developer, Teacher Retirement System of Texas (2012-2014)

• Optimal Transport
• Calculus of Variations
• Mathematical Analysis
• +۴ مورد دیگر

Hugo Lavenant is an Assistant Professor in the Department of Decision Sciences at Bocconi University in Milan, Italy. His academic journey includes a PhD in mathematics from Université Paris-Sud under Filippo Santambrogio (2016-2019) and a postdoctoral fellowship at the University of British Columbia (2019-2020) working with Young-Heon Kim, Brendan Pass, Geoffrey Schiebinger, and Dave Schneider. Professor Lavenant's research spans theoretical and applied aspects of mathematical analysis, with a focus on optimal transport theory , calculus of variations , and Bayesian statistics . His work explores the geometry of the Wasserstein space, numerical solutions to dynamical optimal transport problems, and applications to biological data analysis. He has made significant contributions to understanding harmonic mappings in the Wasserstein space, connections between optimal transport and nonlinear elasticity, and the application of optimal transport to trajectory inference in biological systems. Analysis of Professor Lavenant's recent publications reveals a strong trend toward interdisciplinary applications of optimal transport, particularly in statistics and biology. His work bridges pure mathematical theory with practical computational methods, with increasing focus on developing tractable statistical tools based on optimal transport distances. The research spans theoretical mathematics, computational methods, and applications to real-world data analysis problems. Professor Lavenant actively supervises graduate students, currently advising PhD candidates George Kanchaveli and Francesco Mascari (both co-advised with Marta Catalano), as well as Master's students Mathis Hardion and Niccolò Bargellini. His teaching portfolio includes Mathematical Analysis 2, Real Analysis I, and Advanced Analysis and Optimization 1 at Bocconi University. His scholarly contributions demonstrate a consistent focus on advancing both the theoretical foundations and practical applications of optimal transport, with growing emphasis on statistical methodology and biological applications in recent years.

Sinho Chewiمشاهده پروفایل

استادیار

• Optimal Transport
• Log-concave Sampling
• Machine Learning Theory
• +۳ مورد دیگر

Sinho Chewi is an Assistant Professor of Statistics and Data Science at Yale University, where he conducts research at the intersection of mathematics, statistics, and machine learning. His work focuses on theoretical aspects of computational statistics, particularly leveraging optimal transport theory for solving complex problems in sampling and inference. Chewi earned his B.S. in Engineering Mathematics and Statistics from the University of California, Berkeley in 2018, followed by a PhD in Mathematics and Statistics from the Massachusetts Institute of Technology in 2023 under the supervision of Philippe Rigollet. Prior to joining Yale, he was a postdoctoral researcher at the Institute for Advanced Study during the 2023-2024 academic year. His research interests span optimal transport theory , log-concave sampling , variational inference , and theoretical foundations of machine learning . Chewi is currently authoring a comprehensive book on the complexity of log-concave sampling, building on his extensive publication record. His work bridges theoretical mathematics with practical applications in statistical computing and artificial intelligence. Analysis of Chewi's publication record reveals a strong focus on developing rigorous mathematical frameworks for sampling algorithms, with particular emphasis on complexity analysis, convergence guarantees, and connections between functional inequalities and optimization. His recent work extends into diffusion models, neural network theory, and parallel sampling algorithms, demonstrating both depth in core statistical theory and breadth across machine learning applications. ICLR 2023 (Notable Top 5%) ALT 2023 (Best Student Paper) NeurIPS 2021 (Spotlight) ICLR 2025 DeLTA Workshop (Best Short Paper Award) Chewi has received significant research support through his postdoctoral position at the Institute for Advanced Study and collaborations with leading researchers at institutions including MIT, NYU, and Microsoft Research. His teaching portfolio includes advanced courses in optimization techniques and sampling methods, reflecting his expertise in theoretical foundations of machine learning algorithms.