جست‌وجوی استاد و پژوهشگر

Sayan Banerjeeمشاهده پروفایل

دانشیار

• Probability Theory
• Stochastic Processes
• Interacting Particle Systems
• +۹ مورد دیگر

Sayan Banerjee is an Associate Professor in the Department of Statistics and Operations Research at the University of North Carolina at Chapel Hill, within the College of Arts and Sciences. His research lies at the intersection of probability theory, stochastic processes, and network science, with applications in operations research and statistical physics. Research Interests: His work focuses on interacting particle systems, ergodicity of diffusions, stochastic networks, probabilistic couplings, and dynamic random graphs. He also investigates random walks in random environments, random matrices, and network sampling and ranking algorithms. These areas reflect a deep engagement with both theoretical probability and its real-world applications in complex systems. Recent Publication Trends: His recent articles (2023–2025) reveal a strong focus on the analysis of infinite particle systems (e.g., Atlas models), dynamic network evolution under various delay regimes, convergence properties of machine learning algorithms like Stein Variational Gradient Descent, and structural properties of random graphs. The keywords span probability, operations research, network science, and computational statistics, indicating interdisciplinary impact. Subfields include rank-based diffusions, local weak convergence, load balancing, flocking dynamics, and network centrality. Scientific Awards: NSF CAREER Award (DMS-2141621) Grants and Advising: He is the principal investigator on the NSF CAREER award DMS-2141621 and a co-PI on the NSF RTG grant DMS-2134107, supporting research training in probability and stochastic processes. While no formal list of students is provided, his role as a Ph.D.-granting faculty member in a top statistics department implies active graduate student mentorship. His prior postdoctoral fellowship at the University of Warwick under Prof. Wilfrid Kendall highlights strong international collaboration. Laboratories and Research Teams: Though no named lab is mentioned, his extensive collaborative network—including researchers like A. Budhiraja, S. Bhamidi, P. Dey, and M. Olvera-Cravioto—indicates leadership in a vibrant research group focused on stochastic modeling and network analysis at UNC.

• Robotics
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Sebastian Thrun is a Professor at Stanford University, USA, with a prolific research career spanning robotics, artificial intelligence, and machine learning. His work has significantly impacted autonomous vehicle technology, computer vision, and medical AI applications. Thrun's research interests focus on robotics, particularly simultaneous localization and mapping (SLAM), autonomous driving systems, and probabilistic state estimation techniques. His work extends to deep learning applications in medical imaging, notably achieving dermatologist-level skin cancer classification. He has pioneered approaches in meta-learning, vector quantization, and efficient clustering algorithms using multi-armed bandits. His recent publications demonstrate a strong trend toward improving efficiency in machine learning algorithms, particularly in areas like decision trees, vector quantization, and nearest neighbor search. Thrun has also made significant contributions to medical AI, applying deep learning to skin cancer detection and molecular property prediction. Max Planck Research Award (2011) Thrun has advised numerous PhD students who have become prominent researchers in robotics and AI, including David Stavens, Anna Petrovskaya, and Jesse Levinson. His research has been supported by significant grants, particularly for autonomous vehicle development, including Stanford's entry in the DARPA Urban Challenge (Junior). His work bridges theoretical advances with practical applications across multiple domains. Thrun has led research teams focused on autonomous driving systems, 3D reconstruction, and medical AI applications. His work on the DARPA Urban Challenge demonstrated advanced capabilities in urban autonomous navigation, while his more recent research explores the intersection of deep learning and efficiency optimization in machine learning algorithms.

Carsten Wittمشاهده پروفایل

استاد

• Evolutionary Algorithms
• Runtime Analysis
• Genetic Algorithms
• +۷ مورد دیگر

Carsten Witt is a Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), working within the Algorithms, Logic and Graphs section. His research is centered on theoretical aspects of evolutionary computation, with a strong emphasis on runtime analysis, genetic algorithms, and randomized search heuristics. He is actively involved in guiding PhD research and has a substantial publication record in top-tier conferences and journals. PhD in Computer Science, Technical University of Dortmund, Germany Postdoctoral research at Max Planck Institute for Informatics Professor at DTU since appointment His primary research interests lie in evolutionary algorithms , runtime analysis , and probability theory in algorithmics . He investigates how bio-inspired optimization techniques such as genetic and compact genetic algorithms perform on benchmark problems like OneMax and LeadingOnes. His work often involves rigorous mathematical analysis to derive bounds on expected runtime and convergence behavior. The recent articles show a consistent trend in the theoretical foundations of evolutionary computation , particularly focusing on multi-valued representations, dynamic mutation strategies, neuroevolution models, and self-adjusting mechanisms. These works span subfields such as stochastic optimization, adaptive parameter control, and algorithmic analysis under probabilistic models. The dominant keywords include Computer Science, Theoretical Computer Science, Optimization, and Artificial Intelligence. Carsten Witt has not been explicitly listed with any scientific awards in the provided text. He has supervised several PhD students, including Adak, Rajabi, and Gießen, in projects related to nature-inspired algorithms and theoretical analysis. While specific grant names are not listed, his involvement in multiple funded PhD projects indicates active participation in research funding and academic leadership. Supervision roles include both main supervisor and examiner positions across various DTU research initiatives. Carsten Witt is affiliated with the Algorithms, Logic and Graphs group at DTU, which functions as a research lab focusing on foundational aspects of computing. This team conducts high-level theoretical research in algorithm design, discrete mathematics, and computational complexity, particularly in the context of heuristic and evolutionary methods.

Benjamin Dadounمشاهده پروفایل

دانشیار

• Probability Theory
• Asymptotic Convex Geometry
• Random Matrices
• +۳ مورد دیگر

Benjamin Dadoun is an Associate Professor in the field of probability theory at Le Mans University, affiliated with the Laboratoire Manceau des Mathématiques (LMM). His research focuses on asymptotic convex geometry, random matrices, high-dimensional phenomena, and growth-fragmentation processes. His research interests include: Asymptotic behavior of random structures in high dimensions Growth-fragmentation processes and their scaling limits Random convex polytopes and their geometric properties Random matrix theory and associated energy functionals Dadoun's recent publications demonstrate a strong focus on the intersection of probability theory, convex geometry, and high-dimensional analysis. His work often involves establishing precise asymptotic behaviors and phase transitions in high-dimensional settings. He has made significant contributions to understanding the properties of Schatten balls, Poisson polytopes, and growth-fragmentation processes, with publications in top journals like Journal of Functional Analysis and Random Matrices: Theory and Applications. His doctoral thesis completed at the University of Zurich under Jean Bertoin established foundational work on growth-fragmentation processes, showing how these continuous processes emerge as scaling limits of discrete Markov branching structures.

Tom Albertsمشاهده پروفایل

دانشیار

• Probability Theory
• Stochastic Analysis
• 2D Conformally Invariant Systems
• +۴ مورد دیگر

Tom Alberts is an Associate Professor in the Department of Mathematics at the University of Utah, where he has been a faculty member since 2013, achieving the rank of Associate Professor in 2020. His research focuses on probability theory, particularly two-dimensional conformally invariant systems and Schramm-Loewner Evolution (SLE). His educational background includes: PhD in Mathematics (2008) from the Courant Institute of Mathematical Sciences at New York University BS in Mathematics (2002) from the University of Alberta Professor Alberts' research spans multiple areas within probability theory and statistical mechanics. His primary focus is on two-dimensional conformally invariant systems, which can be understood as the natural extension of one-dimensional random paths to two-dimensional random surfaces. This work has potential applications across various fields including physics, finance, and artificial intelligence. He also maintains active research in statistical mechanics, random walks in random environments, directed polymer models, last passage percolation, and random matrix theory. His work often bridges theoretical mathematics with physical applications, demonstrating how abstract probability concepts can illuminate real-world phenomena. His recent publications (2022-2025) demonstrate a sophisticated evolution of his research program, moving from foundational work on SLE boundary behavior to more complex explorations of conformal field theory in multiply connected domains. The trajectory shows increasing mathematical sophistication while maintaining connections to physical systems. His work on directed polymers and last passage percolation reveals deep connections to the Kardar-Parisi-Zhang universality class, which describes interface growth in diverse physical systems. Professor Alberts has presented his research at prestigious venues including the Fields Institute, Mathematical Sciences Research Institute, Institut Mittag-Leffler, and Korean Institute of Advanced Study. His recent talks have focused on conformal field theory in multiply connected domains and the interplay between random geometry and conformal field theory, indicating where his research program is heading. His ORCID identifier is 0000-0002-1696-853X, reflecting his established scholarly presence.

• Combinatorics
• Discrete Mathematics
• Graph Theory
• +۳ مورد دیگر

Professor Daniel Kleitman is a faculty member in the Massachusetts Institute of Technology (MIT) Department of Mathematics , where he has taught courses such as 18.310 Principles of Discrete Applied Mathematics , 18.01 Calculus , and 18.022 Calculus . His academic rank is Professor, with a career spanning decades in combinatorics, discrete mathematics, and interdisciplinary applications. Teaches 18.310 (Discrete Applied Mathematics) Teaches 18.01 (Calculus) Teaches 18.022 (Multivariable Calculus) Research Interests Professor Kleitman’s work lies at the intersection of combinatorics , graph theory , and discrete applied mathematics . His research explores: Theoretical foundations of discrete mathematics Error-correcting codes and cryptographic algorithms Applications to computer science, biology, and physics Geometric and probabilistic methods in combinatorics Publications Trends His publications since 1980 emphasize combinatorics , graph theory , and algorithm design . Key areas include: Combinatorial geometry (convex sets, planarity) Network optimization (spanning trees, linear programming) Coding theory (Shannon bounds, BCH codes) Interdisciplinary applications (gene recognition, urban homicide analysis)

• Invasion Ecology
• Forest Ecology
• Computational Modelling
• +۴ مورد دیگر

Samuel M. Fischer is a Research Fellow at the Helmholtz Centre for Environmental Research (UFZ) in the Department of Ecological Modelling, where he has been conducting postdoctoral research since 2023. He holds a PhD in Applied Mathematics from the University of Alberta and a Bachelor of Science in Applied Systems Science from Osnabrück University. His research integrates mathematical modeling, algorithm design, and statistics to address ecological challenges. Primary interests include: Modeling invasive species spread using mobile app data and network analysis Mechanistic forest modeling for productivity assessment and stress detection via remote sensing Developing computational methods for ecological epidemiology and carbon sequestration Advancing standardization in ecological modeling through the Open Modelling Foundation Fischer's publications demonstrate a strong focus on integrating computational methods with ecological applications. Recent work emphasizes forest carbon dynamics, species persistence modeling, and optimization algorithms for environmental management. Methodological innovations in statistical computing and cross-disciplinary model integration are recurring themes across his research portfolio. He actively mentors students in computational ecology and promotes open science standards. Current projects involve linking hyperspectral satellite data with forest models to monitor ecosystem health in German forests.

David Wartonمشاهده پروفایل

استاد

• Ecological Statistics
• Multivariate Analysis
• High-Dimensional Data Analysis
• +۴ مورد دیگر

Professor David Warton, based at the School of Mathematics & Statistics, University of New South Wales, is a leading ecological statistician advancing data analysis methods in ecology. He leads the Eco-Stats group, which has secured over $9M in ARC funding, and founded Stats Central, the UNSW statistical consulting unit. His cross-disciplinary work spans methodological development for multivariate analysis, species distribution modeling, and ecological theory. Key research areas include: Multivariate abundance data analysis Species distribution modeling with point processes High-dimensional statistical methods in community ecology Machine learning applications in ecological contexts Model selection and error-in-variables regression Recent publications (2023-2025) focus on spatiotemporal modeling, computational innovations for ecological data, and clinical applications of statistical methods. His work emphasizes bridging statistical theory with real-world ecological challenges, particularly in biodiversity and climate change impacts. Scientific awards include the Christopher Heyde Medal (2014), Fellow of the Royal Society of NSW (2020), and recognition as a Highly Cited Researcher (2019-22). He actively supervises students in ecological statistics and machine learning applications.

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

Alexander Fribergh is an Associate Professor at the Department of Mathematics and Statistics under the Faculty of Arts and Sciences at the University of Montreal. His research focuses on Probability Theory , Stochastic Processes , and Statistical Physics , with applications in Random Walks and Mathematical Analysis . Research Areas : Probability Theory, Random Walks in Random Environments, High-dimensional Lattice Models, Percolation Theory, Scaling Limits in Stochastic Systems Key Projects : Lead Researcher: Random Walks in Random Environments and Traps (2022–2029) Lead Researcher: Étude des marches aléatoires biaisées sur des clusters de percolations (2015–2019) Co-researcher: Centre de recherches mathématiques (CRM) (2015–2023) Grants : FRQNT - Programme de regroupements stratégiques (2022–2029) NSERC - Discovery Grant (2020–2027) FRQNT - Programme de subvention à la découverte (2015–2022) Teaching : Courses include MAT-2719 – Marches et graphes aléatoires , MAT-6701 – Probabilités , and MAT-6717 – Probabilités . Student Supervision : Supervised 8 theses (2017–2024) across Master's and Doctoral levels, including Chanel-Agouès Emile (2024, Master's), Davignon Élise (2024, Doctoral), and Ouimet Frédéric (2019, Doctoral).

Michel Goemansمشاهده پروفایل

استاد

• Combinatorial Optimization
• Discrete Algorithms
• Approximation Algorithms
• +۳ مورد دیگر

Michel Goemans is the RSA Professor and Head of the Department of Mathematics at the Massachusetts Institute of Technology (MIT), with additional affiliations at MIT CSAIL and MIT ORC. Previously, he held the Leighton Family Professorship (2007-2017) and adjunct/visiting positions at the University of Waterloo, University of Louvain, and RIMS Kyoto. His research focuses on combinatorial optimization , discrete algorithms , and approximation methods , with applications spanning network design, stochastic optimization, and algorithmic game theory. His publications consistently explore fundamental problems in mathematical programming and theoretical computer science. Notable awards include: Leroy P. Steele Prize (2022) George B. Dantzig Prize (2021) Farkas Prize (2012) Fellowships: AMS, ACM, SIAM, Guggenheim, Sloan Foundation Doctor Honoris Causa (Université catholique de Louvain) He advises doctoral candidates and postdocs, with prominent former students including David Williamson (Cornell), Jon Kleinberg (Cornell), and Aleksander Madry (MIT). Research is primarily funded by NSF and ONR grants.

Marco Molinaroمشاهده پروفایل

استاد

• Online Algorithms
• Convex Optimization
• Machine Learning
• +۵ مورد دیگر

Marco Molinaro is a Professor in the Computer Science Department at PUC-Rio . He previously held Assistant Professor positions at TU Delft (EWI), Georgia Tech (ISyE, ACO), and was a PhD student in the ACO program at Carnegie Mellon University . Education: PhD in Algorithms, Combinatorics, and Optimization (ACO), Carnegie Mellon University Research Interests: Marco's work lies at the intersection of online algorithms , convex optimization , machine learning , and operations research . His recent publications analyze: Online scheduling and VM allocation in data centers Complexity of branch-and-bound trees Information-theoretic bounds for optimization problems Explainable decision trees and their theoretical limits Stochastic and adversarial bandit algorithms Curvature properties in optimization sets His research often bridges abstract mathematical frameworks (e.g., Lipschitz functions, supermodular norms) with practical applications in cloud computing, machine learning, and game theory. Publication Trends: Marco's recent work focuses on online convex optimization and its extensions to data center resource allocation (Kamino: latency-driven VM scheduling), dynamic bin packing , and information complexity in mixed-integer models. He frequently collaborates with researchers at institutions like Carnegie Mellon University and TU Delft, with recurring co-authors Santanu Dey, Amitabh Basu, and Thomas Kesselheim. Teaching: Marco teaches advanced courses in algorithm design and analysis at PUC-Rio, including: Analise de Algoritmos (2020–2025) Projeto e Analise de Algoritmos (postgraduate, 2020–2025) Algoritmos e Incerteza (postgraduate, 2020) Estruturas Discretas (2021) His curriculum emphasizes online learning , random-order models , and PAC learning , with connections to game theory and practical implementations.

Anupam Guptaمشاهده پروفایل

استاد

• Theoretical Computer Science
• Algorithms
• Approximation Algorithms
• +۴ مورد دیگر

Anupam Gupta is a Silver Professor at the Department of Computer Science, Courant Institute of Mathematical Sciences, New York University. Previously, he was affiliated with Carnegie Mellon University. His academic journey includes a Ph.D. from the University of California, Berkeley (2000) and a B.Tech. from the Indian Institute of Technology, Kanpur (1996). Education : Ph.D., University of California, Berkeley (2000) B.Tech., Indian Institute of Technology, Kanpur (1996) Research Interests : Anupam's research lies at the intersection of theoretical computer science and algorithm design. He focuses on approximation algorithms for stochastic and online environments, metric embeddings, and algorithmic problems in network design, clustering, and optimization. His work bridges theoretical insights with practical applications in uncertain and dynamic settings. Publications & Trends : His recent publications highlight advancements in online algorithms with adversarial corruptions, stochastic optimization, and approximation techniques for problems like k-server, set cover, and graph searching. Key trends include leveraging predictions, balancing adaptivity and robustness, and exploring the interplay between machine learning and classical algorithm design. Scientific Awards : ACM Fellow (2021) Alfred P. Sloan Research Fellowship NSF CAREER Award Herb Simon Award for Teaching Excellence (Carnegie Mellon University) Advising & Grants : He has mentored numerous Ph.D. students and postdoctoral fellows. His research is supported by grants from the National Science Foundation (NSF), including a CAREER Award, and collaborative funding for algorithmic initiatives like the Simons Institute program and the Joint Indo-US Networked Center for Algorithms under Uncertainty.

Madhu Sudanمشاهده پروفایل

استاد

• Theories of Computation and Communication
• Coding Theory
• Property Testing
• +۵ مورد دیگر

Madhu Sudan is the Gordon McKay Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS), where he also serves as Director of Graduate Studies. He holds a B.Tech. from IIT Delhi (1987) and a Ph.D. from UC Berkeley (1992). Prior to Harvard, he was at MIT (1997–2015), IBM Research (1992–1997), and Microsoft Research (2009–2015). His research focuses on the theories of computation and communication, with notable contributions to probabilistically checkable proofs (PCP theorem), list-decoding algorithms for error-correcting codes, and sublinear-time algorithms. Education: B.Tech., Indian Institute of Technology Delhi, 1987 Ph.D., University of California, Berkeley, 1992 Research Interests: Communication and computing under errors Coding theory and its applications Property testing and sublinear algorithms Algebraic methods in computation Awards and Honors: Nevanlinna Prize (2002) IEEE Hamming Medal (2016) Member, National Academy of Sciences (2017) Infosys Prize in Mathematical Sciences (2018) Fellow of ACM, IEEE, and AMS Teaching and Service: Current courses: Algebra and Computation , Coding Theory Directed Graduate Studies in Computer Science at Harvard Editorial roles: Foundations and Trends in Theoretical Computer Science , Theory of Computing Research Groups: Member of Harvard's Theory of Computation group, collaborating on projects in coding theory, complexity theory, and distributed computing.

Yi-Chun Akchenمشاهده پروفایل

استادیار

• Machine Learning
• Operations Research
• Optimization
• +۳ مورد دیگر

Yi-Chun Akchen is an Assistant Professor at the UCL School of Management, University College London. His research focuses on customer choice modeling, leveraging machine learning and optimization techniques. He holds a PhD from UCLA Anderson School of Management, an M.S. from Stanford University, and a B.S. from National Taiwan University. Education: PhD in Management, UCLA Anderson School of Management M.S. in Computational & Applied Math, Stanford University B.S. in Physics, National Taiwan University His research explores data-driven decision-making, with a focus on irrational choice modeling, assortment optimization, and consideration set dynamics. Key contributions include the Decision Forest model and applications in retail analytics. Publications span operations research, machine learning, and marketing, addressing topics like POMDPs, Bayesian networks, and multi-agent systems. Notable awards include the Nicholson Student Paper Prize and Decision Analysis Award. Awards: Finalist, Service Science Best Cluster Paper Competition (2024) Honorable Mention, George Nicholson Student Paper Prize (2020) 1st Place, Decision Analysis Student Paper Award (2019) His work integrates theoretical rigor with practical applications, evidenced by contributions to open-source code for the Decision Forest model on GitHub.

Gianluca Baioمشاهده پروفایل

استاد

• Health Economics
• Bayesian Statistics
• Causal Inference
• +۵ مورد دیگر

Gianluca Baio is a Professor of Statistics and Health Economics at University College London (UCL), Department of Statistical Science. He holds a PhD in Applied Statistics from the University of Florence and has worked extensively in Bayesian statistical modeling for healthcare applications, including cost-effectiveness analysis and causal inference. His research focuses on hierarchical models, missing data methods, and health economic evaluation, with notable contributions to HPV vaccination cost-effectiveness and policy impact assessments. Education: PhD in Applied Statistics, University of Florence (Italy) Master's in Statistics and Economics, University of Florence Visiting Researcher, MIT Sloan School of Management (USA) Research Interests: Bayesian methods in health economics Missing data imputation in clinical trials Cost-effectiveness analysis Interrupted time series for policy evaluation Value of Information analysis Healthcare resource allocation Key Grants: Wellcome Trust-funded study on Bayesian hierarchical frameworks for policy evaluation (2021–2025) MRC grant on regression discontinuity design for drug evaluation (2013–2015) NIHR grants on stepped-wedge trial methodologies Awards & Recognition: None explicitly listed, but recognized for contributions to Bayesian HTA methodologies. Professional Activities: Lead of UCL's Statistics for Health Economic Evaluation Group Author of R packages BCEA and survHE Editorial board member of Medical Decision Making