جست‌وجوی استاد و پژوهشگر

Soumik Palمشاهده پروفایل

استاد

• Probability
• Stochastic Processes
• Optimal Transport
• +۵ مورد دیگر

Soumik Pal is a Professor in the Department of Mathematics at the University of Washington, with adjunct appointments in Applied Mathematics and Statistics. His research focuses on probability theory, particularly interacting Brownian particle systems, random graphs, random matrices, and optimal transport. He leads the Kantorovich Initiative, an interdisciplinary research effort funded by PIMS and NSF, advancing applications of optimal transport in data science. NSF grants DMS-2052239 and DMS-2134012 (Scale Math of Deep Learning) NSF Infrastructure grant DMS-2133244 supporting the Kantorovich Initiative His recent work bridges optimal transport with gradient flows, Schrödinger bridges, and graphons. He advises PhD students in probability and stochastic models, with former advisees including Tobias Johnson and Andrey Sarantsev. He serves as Associate Editor for journals like Stochastic Models and Electronic Journal of Probability .

• measure theory
• Gaussian measures
• measures on infinite-dimensional spaces
• +۴ مورد دیگر

Egor Dmitrievich Kosov is an Associate Professor at the Faculty of Computer Science of the National Research University Higher School of Economics (HSE), where he has been working since 2016. He also serves as a Senior Research Fellow at the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis within HSE's Institute of Artificial Intelligence and Digital Sciences. Additionally, he holds positions as Senior Researcher at the Steklov Mathematical Institute's Department of Function Theory and Junior Researcher at the Laboratory of Multidimensional Approximation and Applications. Dr. Kosov earned his Candidate of Physical and Mathematical Sciences degree from Lomonosov Moscow State University in 2018, where he also completed postgraduate studies specializing in Mathematics and Mechanics with a qualification as Researcher. His research focuses on measure theory , particularly Gaussian measures , measures on infinite-dimensional spaces , logarithmically concave measures , and measurable polynomials . Kosov's work bridges theoretical mathematics with applications in stochastic analysis, exploring the regularity properties of distributions and developing discretization techniques for functional norms. His research has significant implications for understanding complex probabilistic structures in high-dimensional spaces. Analysis of Kosov's recent publications reveals a strong focus on polynomial mappings of random variables, particularly Gaussian and log-concave distributions. A significant portion of his research addresses discretization problems—developing methods to approximate continuous mathematical structures through discrete sampling. His publications demonstrate growing recognition in the mathematical community, with appearances in prestigious journals across multiple subfields of mathematical analysis. Letter of gratitude from the First Vice-Rector of HSE (March 2023) Letter of Gratitude from the Faculty of Computer Science at HSE (September 2019) Bonus for publication in List A journals (2023-2024) Multiple bonuses for international peer-reviewed publications (2019-2023) Best Teacher award (2018) Moscow Mathematical Society award (2021) At HSE, Kosov teaches Mathematical Analysis, Probability Theory, and Functional Analysis to undergraduate students in the Applied Mathematics and Computer Science program across both the Faculty of Computer Science and the Faculty of Economic Sciences. His teaching spans multiple academic years (2020-2023), demonstrating his commitment to education alongside research. Dr. Kosov is actively involved in research teams including the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis at HSE and the Laboratory of Multidimensional Approximation and Applications. His work connects with the broader mathematical community through collaborations with researchers such as V.I. Bogachev, V.N. Temlyakov, and others, contributing to Russia's strong tradition in mathematical analysis and probability theory.

• Fuzzy Statistics
• Statistical Inference
• Operations Research
• +۳ مورد دیگر

Ana Maria Colubi Cervero is a Professor in the Department of Statistics and Operations Research at the University of Oviedo. Her research focuses on statistical methods for imprecise data, particularly fuzzy sets and interval-valued data analysis. She has established herself as a leading researcher in the field of fuzzy statistics and statistical inference with imprecise information. She earned her Doctorate from the University of Oviedo in 2000 with a thesis titled "Leyes fuertes de los grandes números para variables aleatorias difusas" (Strong Laws of Large Numbers for Fuzzy Random Variables), supervised by Dr. José Santos Domínguez Menchero and Dr. María Angeles Gil Alvarez. Colubi Cervero's research primarily centers on fuzzy statistics, with particular emphasis on fuzzy random variables, interval-valued data analysis, and statistical methods for imprecise information. Her work bridges theoretical statistics with practical applications, developing methodologies that can handle uncertainty and imprecision in data. She has made significant contributions to distance-based statistical analysis of fuzzy data, regression models for interval-valued responses, and hypothesis testing for fuzzy parameters. Her publication record shows a consistent trend toward developing robust statistical frameworks for imprecise data, with increasing focus on practical applications in recent years. The articles demonstrate a progression from theoretical foundations to more applied statistical methods, particularly in regression analysis and hypothesis testing with fuzzy and interval-valued data. Throughout her career, Colubi Cervero has maintained extensive collaborations, primarily with researchers from Spanish institutions but also with international scholars. Her work shows strong interdisciplinary connections between statistics, probability theory, and computational mathematics. She has been actively involved with research groups including GRINAT (Grupo de Investigación en Riesgos Naturales) and SMIRE+CoDIRE (Estadística con Elementos Imprecisos Aleatorios & Comparación de Distribuciones entre Elementos Aleatorios), focusing on natural risk research and statistical methods for imprecise random elements.

Amiremad Ghassamiمشاهده پروفایل

استادیار

• Causal Inference
• Causal Discovery
• Statistical Learning Theory
• +۳ مورد دیگر

Amiremad Ghassami is an Assistant Professor in the Department of Mathematics and Statistics at Boston University. His research focuses on causal inference and discovery, statistical learning theory, and semiparametric statistics. He develops methodologies to address challenges in causal effect estimation, policy optimization, and causal graph identification in complex real-world data scenarios such as unobserved confounders and measurement errors. His work integrates modern statistical techniques, machine learning, and information theory. Dr. Ghassami holds a PhD in Data Science and Communications from the University of Illinois at Urbana-Champaign (2020), advised by Prof. Negar Kiyavash, and completed postdoctoral research at Johns Hopkins University under Professors Ilya Shpitser and Eric Tchetgen Tchetgen. He co-organizes the Statistics and Probability Seminars at Boston University. Research Interests: Causal Inference and Discovery Statistical Learning Theory Semiparametric Statistics Probabilistic Graphical Models His recent publications emphasize causal data fusion, mediation analysis, and brain connectivity studies using fMRI. He addresses methodological challenges in handling unobserved variables and nonignorable missing data through innovative data fusion strategies and debiased estimation techniques. Dr. Ghassami has contributed to causal structure learning algorithms for cyclic and acyclic models, with applications to neuroscience and policy evaluation. His work bridges theoretical foundations and practical data-driven solutions for modern causal analysis problems.

Youngtak Sohnمشاهده پروفایل

استادیار

• Probability Theory
• High-Dimensional Statistics
• Random Graphs
• +۳ مورد دیگر

Youngtak Sohn is an Assistant Professor in the Division of Applied Mathematics at Brown University. His research bridges probability theory with statistical physics, machine learning, and theoretical computer science. Current investigations focus on high-dimensional statistical inference, random constraint satisfaction problems, and phase transitions in disordered systems. Previously, he was a postdoctoral researcher at MIT and earned his PhD in Statistics from Stanford University under Amir Dembo. Key research areas include: Phase transitions in random constraint satisfaction problems Statistical-computational gaps in high-dimensional inference Replica symmetry breaking in spin glass models Sharp thresholds in graph inference and community detection His publications demonstrate deep mathematical rigor, with recent work establishing fundamental limits in statistical estimation using low-degree polynomials and characterizing exact phase transitions in stochastic block models. The research consistently develops new mathematical frameworks for understanding computational thresholds in high-dimensional statistics and statistical physics. He mentors students through programs like MIT PRIMES, guiding projects on hypergraph coloring thresholds. His teaching portfolio includes graduate courses in probability theory and seminars on statistical learning theory.

Martin Wainwrightمشاهده پروفایل

استاد

• Statistics
• Machine Learning
• Information Theory
• +۳ مورد دیگر

Martin Wainwright is the Cecil H. Green Professor in Electrical Engineering and Computer Science and Mathematics at MIT, affiliated with the Laboratory for Information and Decision Systems and the Statistics and Data Science Center. He joined MIT in 2022 from UC Berkeley, where he held the Howard Friesen Chair. He earned his B.Math from the University of Waterloo and his PhD from MIT in Electrical Engineering and Computer Science. His research focuses on statistics, machine learning, information theory, and optimization. Wainwright has received numerous awards, including the COPSS Presidents’ Award, Blackwell Award, and Sloan Fellowship. He co-authored influential books on graphical models, sparse statistical modeling, and high-dimensional statistics. His work spans theoretical foundations and applications in reinforcement learning, statistical inference, and algorithmic design. He advises students in interdisciplinary areas and leads research initiatives at the MIT Institute for Data, Systems, and Society (IDSS), fostering collaborations across engineering, statistics, and data science.

• Statistical Physics
• Condensed Matter Physics
• Biological Physics
• +۶ مورد دیگر

Jack Lidmar is an Associate Professor in the Department of Physics at KTH Royal Institute of Technology. His research focuses on statistical physics, condensed matter physics, and biological physics, with emphasis on fluctuation phenomena in superconductors, phase transitions, quantum phase transitions, disordered systems, and the development of efficient Monte Carlo simulation methods. He has contributed to studies of superconducting nanostructures, spin glasses, and biological systems such as virus structural mechanics. Lidmar teaches several courses at KTH, including Computational Physics , Statistical Physics , and Quantum Information , and oversees multiple degree projects. His work integrates theoretical modeling with advanced simulation techniques, such as the Accelerated Weight Histogram (AWH) method, to explore rare events and free energy landscapes in materials and biological systems. Recent research highlights include investigations of superconducting nanostrips, phase transitions in two-dimensional materials, and the application of simulation methods to predict material properties like skin permeability. His interdisciplinary approach bridges condensed matter physics with computational biology and engineering applications.

Keith Martin Ballمشاهده پروفایل

استاد

• Functional Analysis
• Convex Geometry
• High-dimensional Geometry
• +۵ مورد دیگر

Keith Martin Ball is a leading mathematician specializing in functional analysis, convex geometry, and information theory. He holds the Professorship at the University of Warwick since 2010 and previously held prominent positions at University College London, Texas A&M University, and the International Centre for Mathematical Sciences (ICMS) in Edinburgh. Alma Mater: Trinity College, Cambridge (PhD 1987 under Béla Bollobás) Key Contributions: Extension theorems for Lipschitz functions, reverse isoperimetric inequalities, Banach-Steinhaus Theorem advancements, and entropy-driven central limit theorem proofs. His research spans high-dimensional geometry, discrete geometry, and applications to information theory. He has authored the popular mathematics book Strange Curves, Counting Rabbits, & Other Mathematical Explorations . Recent Publications focus on zeta function irrationality, entropy inequalities, and geometric functional analysis, reflecting his interdisciplinary approach combining pure mathematics with probabilistic and information-theoretic methods. Scientific Honours Fellow of the Royal Society (FRS) (2013) Fellow of the American Mathematical Society (AMS Fellow) (2013) Shephard Prize (2015) Whitehead Prize (1992) Leverhulme Fellow (2003-2004) Member of Academia Europaea (2023)

• Mathematical Physics
• Probability Theory
• Random Matrix Theory
• +۵ مورد دیگر

Gaultier Lambert is an Associate Professor at the Department of Probability, Mathematical Physics and Statistics, KTH Royal Institute of Technology. His research focuses on the intersection of probability theory, mathematical physics, and random matrix theory, with particular emphasis on determinantal processes, log-correlated fields, and quantum statistics. He holds a PhD and has an extensive publication record in high-impact journals. Key research areas include fluctuations in free fermionic systems, multiplicative chaos, and universality in random matrix ensembles. His work explores topics such as characteristic polynomials of random matrices, law of large numbers for Coulomb gas potentials, and quantum statistics transmutation via magnetic flux attachment. Recent publications (2021–2025) emphasize asymptotic analysis of eigenvalue fluctuations, universality in semiclassical systems, and stochastic processes in high-dimensional spaces. Lambert’s contributions bridge theoretical probability with applications in quantum physics and statistical mechanics. No scientific awards or grants are explicitly listed in the provided texts. He advises no listed students and is affiliated with the Lindstedtsvägen 25 research group at KTH.

• Artificial Intelligence
• Decision Science
• Statistics
• +۴ مورد دیگر

Yunbei Xu is a Researcher at Columbia University's Department of Decision, Risk, and Operations (DRO) within the Graduate School of Business. He holds a PhD from Columbia University (2023) and a BS in Pure Mathematics from Peking University (highest honor). His research bridges Artificial Intelligence, Decision Science, and Physics, focusing on sequential learning, bandit algorithms, and statistical theory. He has advised four students and co-authored multiple influential papers in venues like NeurIPS and ICML, earning awards including the ICML Outstanding Paper Award. Education: PhD in DRO (Columbia, 2023), Postdoc at MIT's College of Computing (LIDS), BS in Pure Mathematics (Peking University) Research interests include adversarial learning, robust optimization, and algorithmic design for sequential decision-making. His work emphasizes theoretical guarantees with practical applications in inventory management and stochastic modeling. Recent articles explore lower bound frameworks for bandit algorithms and Bayesian principles in frequentist learning. Teaching roles include instructor for Stochastic Models at NUS and assistant for courses at Columbia. Awards: INFORMS Nicholson Prize (1st), Applied Probability Society Finalist (2x) Active in grants and lab collaborations, Xu's work is implemented in repositories like MAB-code . His academic lineage traces back to mathematical pioneers like Hilbert and Bernoulli, reflecting deep roots in foundational theory.

Jianjun Huangمشاهده پروفایل

پژوهشگر

• Coastal Engineering
• Ocean Hydrodynamics
• Wave-Structure Interaction
• +۳ مورد دیگر

Jianjun Huang is a Researcher at The University of Western Australia (UWA), affiliated with the School of Earth and Oceans. He holds a PhD in Ocean Engineering from UWA (2025), an MEng in Harbour, Coastal and Offshore Engineering from Hohai University (2021), and a BEng in Harbour, Waterway and Coastal Engineering from Hohai University (2018). His research focuses on nearshore hydrodynamics, wave-structure interactions, and nature-based coastal protection solutions using artificial reefs and kelp forests. He employs experimental methods and numerical modeling to study wave energy dissipation, porous media dynamics, and sustainable coastal defense strategies. Key research contributions include parametric modeling of wave transformation across porous reefs, quantifying wave dissipation mechanisms, and evaluating modular reef designs for coastal protection. His work aligns with UN Sustainable Development Goals, particularly addressing climate action (SDG 13) and life below water (SDG 13). Huang has published extensively in coastal engineering journals and was awarded the Outstanding Student Abstract Award in 2024 for his research on artificial reef systems. Educations: PhD in Ocean Engineering, UWA (2025) MEng in Harbour, Coastal and Offshore Engineering, Hohai University (2021) BEng in Harbour, Waterway and Coastal Engineering, Hohai University (2018) Awards: Outstanding Student Abstract Award (2024) Grants/Advising: Blue Economy CRC project (current postdoctoral research) No listed students/grants explicitly mentioned Huang collaborates on coastal infrastructure projects and has expertise in experimental hydraulics, CFD simulations, and sustainable marine technologies. His research team contributes to both academic advancements and practical solutions for coastal resilience.

Ryan Tibshiraniمشاهده پروفایل

استاد

• High-Dimensional Statistics
• Nonparametric Estimation
• Distribution-Free Inference
• +۵ مورد دیگر

Ryan Tibshirani is a Professor of Statistics at the University of California, Berkeley, and Principal Investigator in the Delphi group. Previously, he was faculty at Carnegie Mellon University (2011–2022). He holds a Ph.D. in Statistics (2011) and a B.S. in Mathematics (2007) from Stanford University. His research focuses on high-dimensional statistics, nonparametric methods, distribution-free inference, and machine learning, with applied work in computational epidemiology, particularly tracking and forecasting epidemics like influenza and COVID-19. He has contributed to open-source tools like the conformalInference R package for predictive inference. Education: Ph.D. in Statistics, Stanford University (2011); B.S. in Mathematics, Stanford University (2007). Professional service includes Editor-in-Chief roles for Foundations and Trends in Machine Learning and Statistics, and membership in the Institute of Mathematical Statistics Council. Research interests span theoretical and applied statistics, including convex optimization, numerical methods, and collaborative efforts in public health forecasting. His work on the Delphi group has advanced real-time epidemic monitoring via sensor fusion and probabilistic forecasting. Recent projects address challenges in estimating time-varying epidemic severity and improving predictive model calibration under distribution shifts. Key contributions include unifying conformal prediction theories, developing trend filtering methods for lattice data, and advancing understanding of cross-validation in overparameterized models. His software tools emphasize reproducibility and scalability for large-scale statistical tasks.

Oanh Nguyenمشاهده پروفایل

استادیار

• Probability Theory
• Stochastic Processes
• Random Functions
• +۴ مورد دیگر

Oanh Nguyen is an Assistant Professor in the Division of Applied Mathematics at Brown University. She holds a PhD from Yale University (2017) and has held prior positions at Princeton University and the University of Illinois. Her research focuses on probability theory, stochastic processes, and their applications, with notable contributions to universality properties of random functions and epidemic modeling on random networks. Education: PhD in Applied Mathematics, Yale University (2017) MS in Mathematics, Université de Tours, France (2012) BS in Mathematics, Hanoi University of Science (2011) Research Interests: Dr. Nguyen investigates fundamental questions in probability theory, including random polynomials, random graphs, and Markov chain mixing times. She explores how universality principles govern the behavior of complex stochastic systems. Recent work examines phase transitions in contact processes and the interplay between network structure and epidemic dynamics. Funding: Current support includes a National Science Foundation DMS grant (2021–present) and a Brown University Salomon Research Award. Awards: NSF Grant DMS-2125031 AIM SQuaREs Collaborative Research Grant Brown University Salomon Research Award Teaching: Recent courses include APMA 2630 (Probability Theory I) and APMA 1860 (Graphs and Networks). She also taught APMA 2812H (Introduction to Random Graphs).

Sühan Altayمشاهده پروفایل

پژوهشگر

• Financial Mathematics
• Quantitative Finance
• Stochastic Processes
• +۵ مورد دیگر

Sühan Altay is a researcher affiliated with the Institute for Statistics and Mathematics at Vienna University of Economics and Business (WU Wien) . His work focuses on Financial Mathematics , Quantitative Finance , and Stochastic Processes , with applications to portfolio optimization, high-frequency trading, and partial information models. He has collaborated extensively with researchers like Prof. Zehra Eksi-Altay and Prof. Kenneth Colaneri. His research spans topics such as optimal trading strategies under uncertainty, market microstructure analysis, and the valuation of complex financial instruments. Notable contributions include studies on dark pools, pairs trading, and regime-switching models. His publications appear in journals like SIAM Journal on Financial Mathematics , International Journal of Theoretical and Applied Finance , and Annals of Operations Research . He holds a Dr.rer.nat. (PhD) in Statistics and Mathematics and has contributed to teaching materials, including lecture notes on stochastic differential equations. No awards or grants are explicitly mentioned in the provided texts.

Jiantao Jiaoمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Statistical Machine Learning
• +۱۱ مورد دیگر

Jiantao Jiao is an Assistant Professor at the University of California, Berkeley, affiliated with the Department of Electrical Engineering and Computer Sciences and the Department of Statistics. His research spans generative AI, foundation models, and systems for robust machine learning, integrating economics, statistics, and computation. Ph.D., Electrical Engineering, Stanford University (2018) His research interests include: Generative AI and foundation models Statistical machine learning and reinforcement learning Privacy and security in machine learning Optimization and economic perspectives of ML Applications in NLP, code generation, computer vision, and robotics Recent publications focus on reinforcement learning, generative AI alignment, and robust statistical methods. He co-directs the CLIMB research center and is affiliated with BAIR, RDI, and BLISS labs. His advisees include prominent graduate students working on foundational and applied machine learning. Teaching includes courses on statistical signal processing, probability, and convex optimization.