جست‌وجوی استاد و پژوهشگر

Aryeh Kontorovichمشاهده پروفایل

استاد

• Theoretical Machine Learning
• Probability
• Statistics
• +۴ مورد دیگر

Aryeh Kontorovich is a Professor in the Computer Science Department at Ben-Gurion University. His research primarily focuses on theoretical machine learning, with expertise in probability, statistics, Markov chains, and metric spaces. His research interests span theoretical machine learning, with particular emphasis on: Probability theory and concentration inequalities Statistical learning theory Markov chains and mixing time estimation Metric space learning Kernel methods Sample compression schemes Professor Kontorovich's recent publications (2021-2025) demonstrate a continued focus on theoretical foundations of machine learning. His work shows strong trends in statistical estimation for Markov processes, distribution learning, metric space analysis, and sample compression. Many papers explore the intersection of probability theory and machine learning, particularly examining concentration inequalities, minimax optimality, and theoretical guarantees for learning algorithms. His research consistently bridges abstract mathematical theory with practical machine learning applications. Scientific awards and recognitions: Distinguished contribution award at MLG 2007 for "A Universal Kernel for Learning Regular Languages" Professor Kontorovich has advised numerous students and collaborated extensively with researchers in theoretical machine learning. His work spans both theoretical foundations and practical applications, with significant contributions to understanding the mathematical limits of learning algorithms. While specific grant information isn't provided in the source material, his extensive publication record in top venues suggests successful funding for his research programs. He maintains active collaborations with researchers worldwide, including prominent names like L. Gottlieb, D. Berend, and S. Hanneke.

George C Tsengمشاهده پروفایل

استاد

• Genomics
• Bioinformatics
• Statistical Learning
• +۹ مورد دیگر

Dr. George C Tseng serves as Professor and Vice Chair for Research in the Department of Biostatistics at the University of Pittsburgh School of Public Health, with secondary appointments in Human Genetics and Computational and Systems Biology. His educational background includes a BS (1997) and MS (1999) in Mathematics from National Taiwan University and an ScD (2003) in Biostatistics from Harvard School of Public Health. Dr. Tseng's research focuses on developing statistical methodologies for genomic and bioinformatic applications to advance precision medicine. His work spans multiple high-impact areas including multi-omics data integration, machine learning for high-dimensional data, cluster analysis for disease subtyping, and statistical methods for experimental design in omics studies. His approach emphasizes close collaboration with biological and clinical researchers to ensure methodological relevance to real-world problems. His publication record demonstrates consistent contributions to top statistical and bioinformatics journals, with recent work focusing on congruence analysis between animal models and humans, outcome-guided clustering methods, and high-dimensional causal mediation analysis. Elected Fellow, American Statistical Association (2017) Statistician of the Year, ASA Pittsburgh Chapter (2017) Provost's Award for Excellence in PhD Mentoring, University of Pittsburgh (2019) Clinical Research Scholar (K12) Award, NIH (2007-2009) Elected Member, International Statistical Institute (2012) Dr. Tseng has successfully mentored over 25 PhD students who have secured positions in academia, industry, and government agencies. His laboratory has maintained continuous NIH funding as principal investigator since 2012, including current grants R01CA285337 (2025-2030) and R01LM014142 (2023-2026). The Tseng Lab operates as a collaborative research environment focused on translating statistical innovations into practical solutions for biological and medical challenges, with strong connections to multiple research centers and clinical departments at the University of Pittsburgh.

Ulrik Brandesمشاهده پروفایل

استاد

• Social Network Analysis
• Graph Theory
• Network Science
• +۳ مورد دیگر

Ulrik Brandes serves as Full Professor and Head of the Department of Humanities, Social and Political Sciences at ETH Zurich, holding the Professorship for Social Networks. He actively teaches courses including Network Analysis and Applied Network Science: Sports Networks for Fall semester 2025, with office location at WEP J 14, Weinbergstr. 109, Zurich. His research spans Social Network Analysis, Graph Theory, and Network Science, with significant applications in sociology, sports analytics (particularly soccer and Australian Football League), and archaeological networks. As a longstanding member of the International Network for Social Network Analysis (since 2001) and the Academy of Sociology (since 2017), he bridges theoretical graph algorithms with practical interdisciplinary applications, recently expanding into sports analytics through the Football Scouting Association (2023). Analysis of his recent publications reveals strong thematic continuity in network centrality, temporal dynamics, and robustness, with increasing application diversity across sports analytics, archaeological trade networks, and decentralized social media platforms. His work consistently emphasizes efficient computational methods for real-world network problems. Honors: Simmel Award (2024) Prof. Brandes maintains active research leadership through departmental oversight and course development, though specific grant details and student advisement records are not publicly documented in available sources. His departmental role indicates substantial administrative responsibilities alongside research and teaching commitments.

Prosper Dovononمشاهده پروفایل

استاد

• Econometrics
• Time Series Analysis
• Financial Econometrics
• +۲ مورد دیگر

Prosper Dovonon serves as a Full Professor in the Department of Economics at Concordia University in Montreal, Canada, where he holds a prestigious Concordia University Research Chair, Tier 1, in Econometrics of Large Datasets. He previously held positions as Associate Professor (2015-2023) and Assistant Professor (2010-2015) at the same institution. Additionally, he maintains an adjunct professorship at the University of Adelaide's School of Economics since 2021 and previously served as a Visiting Professor at HEC Montreal's Department of Finance (2017-2018). His educational background includes a PhD in Economics from Universite de Montreal (2007), an MSc in Statistics and Economics from ENSEA, Abidjan, Cote d'Ivoire (2000), and an MSc in Mathematics from Universite Nationale du Benin, Abomey-Calavi, Benin (1996). Dovonon's research focuses on advanced econometric methodologies, particularly in time series analysis and financial econometrics. His work addresses complex identification issues, develops robust estimation techniques, and creates innovative testing procedures for economic models. He specializes in moment condition models, GMM estimation, volatility modeling, and handling identification failures in econometric frameworks. His publication record shows a consistent focus on theoretical econometrics with practical applications in finance. Recent work emphasizes mixed identification strength scenarios, instrument exogeneity testing, and specification testing under challenging identification conditions. His research demonstrates increasing sophistication in handling complex econometric problems with real-world financial data applications. His notable recognition includes the Concordia University Research Chair, Tier 1, in Econometrics of Large Datasets, highlighting his significant contributions to the field. Dovonon has supervised numerous graduate students and collaborated extensively with leading econometricians worldwide. His research has been supported by institutional funding through his Research Chair position, enabling significant contributions to econometric theory and methodology. He maintains active research collaborations across international institutions and continues to push the boundaries of econometric theory with applications to financial markets and economic modeling.

Isaac Goldbringمشاهده پروفایل

استاد

• Model Theory
• Continuous Model Theory
• Nonstandard Analysis
• +۱۱ مورد دیگر

Isaac Goldbring is a Professor in the Department of Mathematics at the University of California, Irvine (UCI), where he is a leading member of the Logic and Foundations group. He also holds a courtesy appointment in the Department of Logic and Philosophy of Science. His research lies at the intersection of model theory, operator algebras, and nonstandard analysis, with significant contributions to combinatorial number theory and quantum complexity theory. His research interests include: Model theory, particularly continuous and metric model theory Applications of nonstandard analysis to algebra, analysis, and combinatorics Tracial von Neumann algebras and the Connes Embedding Problem Operator algebras and their logical properties Combinatorial number theory and ultrafilters Lie theory and geometric group theory Goldbring’s recent publications reflect a strong trend in applying model-theoretic tools to deep problems in operator algebras and quantum information. His work on the Connes Embedding Problem, especially in connection with the MIP*=RE result, has provided simpler, more direct model-theoretic proofs and stronger refutations. He has also contributed to the logical undecidability of operator algebraic properties and the model theory of C*-algebras and W*-probability spaces. His notable scientific contributions include editing the volume Model Theory of Operator Algebras and authoring influential books such as Ultrafilters throughout Mathematics and Nonstandard Methods in Ramsey Theory and Combinatorial Number Theory . He is the Editor-in-Chief of the Journal of Logic and Analysis . Goldbring currently holds an NSF grant on model theory, quantum complexity, and embedding problems in operator algebras. He has mentored several graduate students, including Ryan Burkhart, Alec Fox, Michael Hehmann, Jennifer Pi, and Jessica Schirle. He has organized major conferences, such as the 2023 North American Annual Meeting of the Association for Symbolic Logic at UCI, and frequently gives invited lectures worldwide. He is actively involved in collaborative research with prominent mathematicians such as Bradd Hart, Ilijas Farah, and Lou van den Dries, and has delivered lecture series at institutions like Yonsei University and the National University of Singapore.

Erhan Bayraktarمشاهده پروفایل

استاد

• Mathematical finance
• applied probability
• machine learning
• +۴ مورد دیگر

Erhan Bayraktar is a Professor of Mathematics at the University of Michigan, holding the Susan Smith Chair. He serves as Director of the Quantitative Finance and Risk Management Masters Program, which he established in 2015. His academic career at the University of Michigan spans since 2004, progressing from T. H. Hildebrandt Research Assistant Professor to his current full professorship. Professor Bayraktar earned his Ph.D. from Princeton University in 2004, following dual Bachelor's degrees in Electrical Engineering and Mathematics from Middle East Technical University in Turkey. His academic journey reflects a strong foundation in both theoretical and applied mathematical disciplines. Bayraktar's research focuses on mathematical finance, applied probability, machine learning, mean field games, stochastic analysis, stochastic control, and optimal stopping. His work bridges theoretical mathematics with practical applications in finance and risk management. He has developed sophisticated mathematical frameworks for analyzing complex financial systems, market behaviors, and optimal decision-making under uncertainty. His contributions to mean field games have provided new insights into large-scale interacting systems, while his work on stochastic control has advanced methodologies for optimal decision processes. His publication record demonstrates a consistent trajectory of high-impact research, with recent work focusing on Wasserstein space analysis, graphon particle systems, and applications of machine learning to financial mathematics. His research shows increasing interdisciplinary connections between traditional mathematical finance and modern computational approaches. Susan M. Smith Professorship (2010-present) National Science Foundation CAREER Grant (2010-2016) SIAM Activity Group on Financial Mathematics and Engineering Early Career Prize (2010) Professor Bayraktar has mentored 14 Ph.D. students (13 graduated) and approximately 40 post-doctoral researchers. His students hold prestigious positions in academia and industry, including tenure-track positions at Boston University, University of Colorado, University of Sydney, and University of Toronto. He has secured continuous funding from the National Science Foundation, including the current grant DMS-2507940 (2025-2028) and previous grants totaling over 15 years of continuous NSF support. As Director of the Quantitative Finance and Risk Management Masters Program, Bayraktar has built a robust academic community through the Financial/Actuarial Math seminar series, which hosts about 10 outside speakers annually, and by organizing international workshops in stochastic analysis for finance and insurance in Ann Arbor.

Caterina Ida Zeppieriمشاهده پروفایل

استاد

• Calculus of Variations
• Elliptic PDEs
• Gamma-convergence
• +۵ مورد دیگر

Professor Caterina Ida Zeppieri is a distinguished mathematician at the Westfälische Wilhelms-University Münster (University of Münster) in Germany, where she leads the Research Group 'Analysis and Modelling' within the Institute for Analysis and Numerics. She has maintained a continuous academic presence since at least the Winter semester 2012/13 through to upcoming semesters in 2025/26, consistently teaching advanced mathematics courses and supervising research activities. Her research focuses on fundamental aspects of mathematical analysis with significant applications to materials science. She specializes in Calculus of Variations, Elliptic PDEs, Gamma-convergence, Homogenization theory, Free-discontinuity problems, Nonlinear elasticity, and Plasticity. Her work bridges theoretical mathematics with practical applications in understanding material behavior, particularly fracture mechanics and composite materials. Professor Zeppieri's publication record demonstrates a consistent and impactful research trajectory from 2007 through forthcoming publications in 2025. Her recent work shows a strong emphasis on stochastic homogenization techniques applied to free-discontinuity problems and singularly-perturbed functionals, revealing sophisticated mathematical approaches to modeling complex material behaviors across multiple scales. She regularly collaborates with leading researchers including Filippo Cagnetti, Gianni Dal Maso, and Lucia Scardia, contributing to significant advances in the mathematical understanding of material science phenomena. Her research has been published in top-tier mathematics journals including Calculus of Variations and Partial Differential Equations, Archive for Rational Mechanics and Analysis, and SIAM Journal on Mathematical Analysis. Within the department, Professor Zeppieri plays an active role in teaching advanced courses such as Partial Differential Equations, Calculus of Variations, and Advanced Topics in the Calculus of Variation, while participating in the department's Advanced Seminar in Applied Mathematics and Colloquium on Applied Mathematics.

• Neural Networks
• Genetic Algorithms
• System Identification and Control
• +۷ مورد دیگر

FURUZUKI, Takayuki is a Professor at Waseda University's Faculty of Science and Engineering, Graduate School of Information, Production, and Systems. He holds a PhD from Kyushu Institute of Technology and maintains the NCLab research group (http://nclab.w.waseda.jp/nclab/). His professional memberships include the Institute of Electronics, Information and Communication Engineers, Institute of Electrical Engineers of Japan, Society of Instrument and Control Engineers, and IEEE. His educational background includes a PhD in Information Science from Kyushu Institute of Technology (1994-1997), a Master's degree in Electronics Engineering from Sun Yat-Sen University (1983-1986), and a Bachelor's degree in Electrical & Electronic Systems Engineering from Sun Yat-Sen University (1979-1983). Professor FURUZUKI's research spans neural networks, genetic algorithms, system identification and control, complex systems, bioinformatics, and combinatorial optimization. His work integrates soft computing techniques with medical informatics and control systems engineering, developing innovative approaches for problems ranging from brain tumor segmentation to music information retrieval. His research methodology often combines theoretical foundations with practical applications in healthcare, energy systems, and intelligent transportation. His recent publication record shows a strong focus on sequential recommendation systems, graph neural networks for medical imaging, music information retrieval, and power systems analysis. The consistent thread across his work is the application of advanced machine learning techniques to solve complex, real-world problems across diverse domains, with particular emphasis on handling missing data, improving model efficiency, and enhancing prediction accuracy. His scientific achievements have been recognized with several prestigious awards: ISCIIA2008 Excellent Paper Award (2008) Institute of Electrical Engineers of Japan Excellent Paper Award (2001) Guangdong Province Higher Education Science and Technology Progress Second Prize (1991) Guangdong Province Higher Education Science and Technology Progress Second Prize (1989) Chinese Ministry of Education Scientific and Technological Achievements Second Prize (1986) Professor FURUZUKI has maintained continuous research activity since joining Waseda University in 2003, with significant contributions to multiple research projects spanning neural computation, soft computing, and system engineering. His work demonstrates strong international collaboration, with publications involving researchers from various institutions worldwide. His research output includes over 410 papers with 3,623 citations and an h-index of 23 according to Scopus data. The NCLab research group, which he leads, focuses on neural computation and related areas, providing a platform for interdisciplinary research that bridges theoretical foundations with practical applications in healthcare, energy systems, and intelligent transportation. His laboratory environment fosters innovation in machine learning algorithms and their application to real-world problems, with particular emphasis on developing efficient, robust solutions for complex data analysis tasks.

• Algorithms
• Computational Complexity
• Convex Optimization
• +۴ مورد دیگر

James R. Lee is a Professor in the Department of Computer Science at the University of Washington. His research spans theoretical computer science, probability, and geometry. He has held visiting scientist roles at Microsoft Research (2023, 2018, 2017) and participated in programs at the Simons Institute (2023, 2020, 2018, 2017, 2014). Research Interests: Algorithms, complexity theory, convex optimization, metric embeddings, spectral graph theory, probability, stochastic processes, and the interplay between discrete and continuous analysis. Teaching: Courses on modern algorithms, quantum computing, optimization theory, and spectral methods in theoretical computer science. Scientific Contributions: Developed sparsification algorithms for generalized linear models and norms with near-linear size guarantees (STOC'24, FOCS'23). Extended Cheeger-type inequalities to higher eigenvalues (STOC'12, STOC'18). Proved super-polynomial lower bounds for LP/SDP relaxations in constraint satisfaction (STOC'15, FOCS'13). Disproved Benjamini-Papasoglou conjectures on annular separators (Discrete Comp. Geom. 2024). Advanced understanding of random walks in geometric and unimodular graphs (Israel J. Math. 2023, GAFA 2023). Scientific Awards: Best Paper Award, STOC 2015

• Reinforcement Learning
• Bandit Algorithms
• Learning Theory
• +۴ مورد دیگر

Claire Vernade is a Group Leader at the University of Tübingen within the Cluster of Excellence Machine Learning for Science. She holds an Emmy Noether award (2022) and an ERC Starting Grant (2024) for her projects FoLiReL and ConSequentIAL , focusing on theoretical reinforcement learning and non-stationary environments. Education: PhD from Telecom ParisTech (2017) Post-doctoral researcher at University of Magdeburg (2018) Her research bridges sequential decision making, bandit problems, and reinforcement learning theory. Recent work explores lifelong learning, distributional RL, and game-theoretic approaches to PCA, emphasizing mathematical rigor and algorithmic innovation. Recent publications highlight trends in non-stationary RL , continual learning , and bandit algorithms with complex feedback structures. Key subfields include meta-learning, adaptive control, and theoretical guarantees in dynamic programming. Scientific Awards: Emmy Noether Award (2022) ERC Starting Grant (2024) Outstanding Paper Award & Oral Presentation, ICLR (2021) She mentors PhD and master's students in theoretical machine learning, with current advisees including Nicolas Nguyen, Onno Eberhard, and Ziyad Sheebaelhamd. Her lab actively recruits candidates in bandit algorithms and RL theory through the IMPRS-IS and ELLIS doctoral programs. Claire co-leads diversity initiatives like Tübingen Women in Machine Learning and Women in Learning Theory, advocating for inclusivity in AI research. She has organized workshops at ICML and EWRL, and contributed to union activism in the tech industry.

• symplectic geometry
• algebraic geometry
• Lie theory
• +۴ مورد دیگر

Chris Woodward is a Distinguished Professor and Chair of the Department of Mathematics at Rutgers University (New Brunswick). He maintains an active research program in symplectic and algebraic geometry, Lie theory, gauge theory, and mathematical physics. Woodward serves as an associate editor for Selecta Mathematica and organizes the Rutgers symplectic seminar, which focuses on current developments in symplectic geometry. Woodward's research primarily centers on symplectic geometry with particular emphasis on Floer theory, mirror symmetry, and connections to mathematical physics. His work bridges geometric analysis with algebraic structures, exploring deep connections between symplectic topology and quantum field theory. He has made significant contributions to understanding the mathematical structures underlying string theory through rigorous geometric frameworks, with recent work focusing on immersed Lagrangians, tropical geometry applications, and quantum cohomology. Analysis of Woodward's recent publications reveals a cohesive research trajectory with increasing sophistication in handling complex geometric structures. His work consistently explores the connections between symplectic geometry, algebraic geometry, and mathematical physics, demonstrating how techniques from one field can solve problems in another. Recent papers show particular innovation in extending Floer theory to immersed settings and developing connections between tropical geometry and symplectic topology. Woodward has advised numerous graduate students who have secured positions at Stanford University, Pennsylvania State University, Yale University, and Columbia University His undergraduate research mentorship program has produced students who continued to top graduate programs He has mentored junior faculty members including Matt Leingang (now at NYU), Eduardo Gonzalez (now at University of Massachusetts Boston), and Yuhan Sun (now at Imperial College) Woodward leads the Rutgers symplectic geometry research group and has organized significant workshops including the Recent Developments in Lagrangian Floer theory workshop at the Simons Center for Geometry and Physics (held in March 2022 after pandemic delay) and is co-organizing a workshop at Harvard's CMSA in Fall 2026 with Denis Auroux and Jonny Evans. His research group maintains strong connections with international symplectic geometry communities.

• Probability Theory
• Stochastic Processes
• Lévy Processes
• +۵ مورد دیگر

Víctor Rivero is a Professor in the Department of Probability and Statistics at the Center of Research in Mathematics (CIMAT) in Guanajuato, Mexico. He previously served as Director of CIMAT from January 2017 to December 2022 and spent a sabbatical year (August 2023-July 2024) at the Probability Group of the Department of Statistics at the University of Warwick, UK. Starting in 2025, he will serve as Editor of ALEA - Revista Latinoamericana de Probabilidad y Estadistica and is part of the Scientific Committee for the 11th International Conference on Lévy Processes to be held in Sofia, Bulgaria in 2025. He also serves on the steering committee of the CIMAT-UNAM-Warwick-Bath research platform for UK-Latin America engagement in probability. Rivero's research focuses on probability theory and stochastic processes, with particular expertise in Lévy processes, Markov processes, and self-similar processes. His work spans fluctuation theory of real-valued Lévy processes and Markov additive processes, stable processes, excursion theory, local times as stochastic processes, branching processes, and regenerative sets. He has developed deep connections between these areas, often using the Lamperti transform to relate self-similar Markov processes to Lévy processes. An analysis of his recent publications reveals a continued focus on foundational aspects of probability theory with applications to various mathematical models, particularly examining how scaling properties and self-similarity manifest in complex systems with increased attention to multidimensional settings and connections to biological models. Rivero actively mentors graduate students at institutions including CIMAT, University of Warwick, and University of Bath, frequently collaborating with other researchers like Andreas Kyprianou on student supervision. He has been a frequent visitor to leading probability research centers worldwide, including institutions in France (Université Sorbonne Paris Nord, Angers) and the UK (Bath, Warwick, Manchester). His teaching includes courses on Lévy processes and self-similar Markov processes, with lecture notes available for students. He is also part of the Network of Creative Teaching in Mathematics, which aims to promote mathematics as an activity that stimulates creative, conceptual, and emotional development.

Florent Krzakalaمشاهده پروفایل

استاد

• Statistical Physics
• Machine Learning
• Probability and Statistics
• +۶ مورد دیگر

Florent Krzakala is a Full Professor at École polytechnique fédérale de Lausanne (EPFL) in Switzerland, holding positions across multiple departments including the School of Basic Sciences (SB), School of Engineering (STI), and specifically within the Department of Physics (IPHYS) and Department of Electrical Engineering (IEM). He leads the Information, Learning and Physics Laboratory (IdePHICS) and maintains an office at ELD 239, Station 11, 1015 Lausanne. His research bridges statistical physics and computational disciplines, with significant contributions to understanding the theoretical foundations of machine learning and optimization problems. Dr. Krzakala received his MSc in Physics from Orsay, France in 1999, followed by a PhD in Statistical Physics from Orsay, Paris XI, France in 2002, and completed a postdoctoral position at Roma La Sapienza in 2004. This strong foundation in physics has informed his interdisciplinary approach to computational problems. His research interests span Statistical Physics, Machine Learning, Probability and Statistics, Computer Science, Information Theory, Inference on Graphs, Random Constraint Optimization, and Computational Optics. Krzakala's work focuses on applying methods from statistical physics to problems in theoretical computer science, probability, and machine learning. He investigates how concepts from disordered systems and phase transitions can illuminate computational barriers in optimization and inference tasks. His research has particular relevance for understanding the behavior of neural networks, compressed sensing, and high-dimensional statistical models. Analysis of his recent publications reveals a strong trend toward understanding the fundamental limits of learning in high-dimensional settings, with particular emphasis on phase transitions, statistical-to-computational gaps, and the theoretical properties of deep learning architectures. His work frequently bridges rigorous mathematical analysis with practical machine learning applications, demonstrating how insights from statistical physics can inform algorithm design and theoretical understanding in AI. Krzakala actively mentors the next generation of researchers, supervising numerous PhD students whose work continues to advance these interdisciplinary fields. His laboratory serves as a hub for researchers exploring the intersection of physics and computation, fostering collaborations across traditional disciplinary boundaries. He teaches advanced courses including Fundamentals of Inference and Learning, Statistical Physics, and Statistical Physics for Optimization & Learning, which examine the connections between physical principles and computational methods. His educational materials, including lecture notes on statistical physics methods in optimization and machine learning, have become valuable resources for students and researchers worldwide. As founder and scientific advisor of the startup Lighton, Krzakala has also demonstrated a commitment to translating theoretical insights into practical applications, particularly in the realm of optical computing for machine learning tasks.

Indranil Chowdhuryمشاهده پروفایل

استادیار

• Theory and Numerical Analysis of PDEs
• Nonlocal/Fractional order Problems
• Fully Nonlinear Equations

Indranil Chowdhury is an Assistant Professor in the Department of Mathematics and Statistics at the Indian Institute of Technology Kanpur. He holds a Ph.D. from Tata Institute of Fundamental Research, Centre for Applicable Mathematics in Bengaluru (2017) and has previously served as a Postdoctoral Researcher at University of Zagreb, Croatia (2020-2022) and Norwegian University of Science and Technology, Trondheim, Norway (2018-2020). Ph.D: Tata Institute of Fundamental Research, Centre for Applicable Mathematics, Bengaluru, India (2017) PG: Tata Institute of Fundamental Research, Centre for Applicable Mathematics, Bengaluru, India (2012) UG: St. Xavier's College, Kolkata, India (2010) Dr. Chowdhury's research focuses on the theory and numerical analysis of partial differential equations, with particular expertise in nonlocal and fractional order problems and fully nonlinear equations. His work bridges theoretical mathematics with practical applications in areas such as mean field games, optimal control, and mathematical modeling. His research program demonstrates a consistent trajectory of advancing the mathematical understanding of complex nonlocal phenomena through rigorous analytical techniques and innovative numerical methods. His publication record reveals a strong focus on fractional calculus, nonlocal diffusion processes, and mean field games. The research shows progression from foundational work on fractional Poincaré inequalities to increasingly sophisticated studies of fully nonlinear mean field games with both local and nonlocal diffusions. His recent work (2023-2025) demonstrates continued innovation in the field, particularly in addressing strongly degenerate cases and establishing precise error bounds for numerical approximations. Dr. Chowdhury maintains an active research program with consistent publication output in high-impact journals such as Foundations of Computational Mathematics, SIAM Journal on Numerical Analysis, and Discrete and Continuous Dynamical Systems. His collaborative work with researchers across international institutions reflects the global significance of his contributions to the field of nonlocal partial differential equations.

Roman Kremsمشاهده پروفایل

استاد

• Quantum Physics
• Machine Learning
• Quantum Chemistry
• +۳ مورد دیگر

Roman Krems is a Professor and Distinguished University Scholar at the University of British Columbia (UBC) in the Department of Chemistry, with affiliations to the Stewart Blusson Quantum Matter Institute. His research focuses on the intersection of quantum physics, machine learning, and chemistry, particularly in quantum materials and quantum technologies such as quantum computing and sensing. Key Roles: Professor at UBC (2013–present), Distinguished University Scholar (2017–present) Education: Ph.D. from Göteborg University (2002), Postdoctoral Fellow at Harvard-MIT Center for Ultracold Atoms (2003–05) Research Interests include: Quantum machine learning (QML) for solving complex physics problems Quantum scattering theory in electromagnetic fields Applications of quantum computing to chemistry Developing machine learning algorithms for quantum dynamics Recent publications highlight advancements in extrapolating quantum observables, Gaussian process models for collision dynamics, and quantum walks in disordered systems. His work bridges theoretical physics, computational methods, and experimental applications in cold molecule research. Scientific Awards include the UBC Killam Teaching Prize (2017), election as Fellow of the American Physical Society (2015), and the Keith Laidler Award (2013). He has held editorial board positions for journals such as Machine Learning: Science & Technology and New Journal of Physics . Research Group members include graduate students and postdocs working on quantum technologies, machine learning, and molecular scattering. He also contributes to outreach through invited talks and seminars at institutions like MIT and Lawrence Berkeley National Laboratory.