جست‌وجوی استاد و پژوهشگر

Satish Raoمشاهده پروفایل

استاد

• Algorithms
• Combinatorial Optimization
• Graph Theory
• +۴ مورد دیگر

Satish Rao is a Professor in the Computer Science Division at the University of California, Berkeley. He is affiliated with the Simons Institute for the Theory of Computing and the Center for the Theoretical Foundations of Learning, Inference, Information, Intelligence, Mathematics and Microeconomics at Berkeley (CLIMB). His research focuses on algorithms, combinatorial optimization, graph theory, and theoretical computer science with applications to computational biology and machine learning. Rao has held teaching roles for courses such as CS 70 (Discrete Mathematics and Probability Theory) and CS 270 (Spring 2024). He has been recognized with prestigious awards including ACM Fellow (2013), the Delbert Ray Fulkerson Prize (2012), and the Okawa Research Grant (1999). Research Interests: Algorithm design, graph algorithms, combinatorial optimization, computational biology, and machine learning. Key Contributions: Pioneering work on metric embeddings, approximation algorithms, and network flow problems. Notable publications include foundational papers on tree metrics, distributed object location, and electrical flow-based optimization. Rao’s work bridges theoretical computer science with practical applications, including contributions to phylogeny estimation, anomaly detection, and parallel computing frameworks like the BSP model.

• Theoretical Computer Science
• Algorithms
• Convex Optimization
• +۴ مورد دیگر

Rasmus Kyng is an Assistant Professor in the Department of Computer Science at ETH Zurich, where he has been since 2019. His research focuses on fast algorithms for graph problems, convex optimization, and their applications in machine learning. He has received grants from the Swiss National Science Foundation, including project grants and a starting grant. Education: B.A. in Computer Science from the University of Cambridge (2011), PhD in Computer Science from Yale University (2017), advised by Daniel A. Spielman. Postdoctoral positions included Harvard University (2018–2019) and a research fellowship at the Simons Institute, UC Berkeley (2017). Research Interests: Development of nearly linear-time algorithms for fundamental graph problems (e.g., maximum flow, minimum-cost flow), dynamic graph algorithms, discrepancy theory, and fine-grained complexity. His work bridges numerical linear algebra and combinatorial optimization, emphasizing practical implementations such as the Laplacians.jl package. Awards: FOCS Best Paper Award (2022), Inaugural ICBS Frontiers of Science Award (2022), Machtey Award (Best Student Paper, FOCS 2017). Teaching: Advanced Graph Algorithms and Optimization (ETH Zurich, 2020–2023), Algorithms, Probability, and Computing (ETH Zurich, 2020–2022). Supervised numerous PhD students and mentored postdocs in theoretical computer science. Labs/Teams: Co-leads a research group with Maximilian Probst Gutenberg, focusing on dynamic graph algorithms and optimization. Collaborations include work on sparsification, spectral graph theory, and machine learning applications.

Artur Czumajمشاهده پروفایل

استاد

• Theoretical Computer Science
• Design and Analysis of Algorithms
• Randomized Algorithms
• +۹ مورد دیگر

Artur Czumaj is a Professor in the Department of Computer Science at the University of Warwick and serves as the Director of the Centre for Discrete Mathematics and its Applications (DIMAP). He is a member of the Division of Theory and Foundations (FoCS) and holds affiliations with the Alan Turing Institute, the Warwick Data Science Institute (WDSI), and the Warwick Centre for Doctoral Training in Mathematics of Real-world Systems (MathSys). Previously, he served as Head of Department and President of the European Association for Theoretical Computer Science (EATCS) from 2020 to 2024. His research lies at the core of theoretical computer science, focusing on the design and analysis of algorithms, particularly randomized, sublinear, parallel, and distributed algorithms. His work spans graph theory, combinatorics, computational geometry, algorithmic game theory, and property testing. He has led major research initiatives funded by EPSRC, IBM, the Royal Society, and Weizmann-UK grants. The trends in his recent publications highlight a strong emphasis on sublinear algorithms, dynamic graph algorithms, and property testing, with recurring themes in randomized methods, graph processing, and efficient data structures. His work bridges foundational theory with applications in data summarization, network analysis, and computational geometry. EPSRC grants: EP/D063191/1, EP/G064679/1, EP/G069034/1, EP/J021814/1, EP/N011163/1, EP/V01305X/1, EPSRC studentship IBM Faculty Award Royal Society International Exchanges Scheme Weizmann-UK Making Connections Grants on combinatorial and algorithmic primitives and the interplay between algorithms and randomness Peter Davies received the 2020 Warwick Faculty of Science Thesis Prize under his supervision Artur Czumaj has supervised numerous PhD students, including Anna Adamaszek, Michal Adamaszek, Sam Coy, Peter Davies, Michail Fasoulakis, Jan Hladky, Wang Xin, and Hairong Zhao. His research has been supported by sustained grant funding, reflecting his leadership in theoretical computer science. He has organized major workshops at Dagstuhl, Oberwolfach, Simons Institute, and the University of Warwick, and has served on the steering committees of HALG and as PC Chair for SODA 2018 and ICALP 2020. He is actively involved in organizing key research events, including the Simons Institute Special Semester on Sublinear Algorithms (2024), the Workshop on Sublinear Graph Simplification (2024), and the Computational Complexity Conference (CCC 2023) at Warwick. He also co-organizes the Warwick-Weizmann workshops and the IGAFIT Algorithmic Postdocs Workshop, fostering international collaboration in algorithms research.

George Cantwellمشاهده پروفایل

استادیار

• Network Science
• Complex Systems
• Statistical Inference
• +۴ مورد دیگر

Dr. George Cantwell is an Assistant Professor in the Department of Engineering at the University of Cambridge, affiliated with Cambridge Infectious Diseases. He specializes in computational methods for inference problems, particularly in disease spreading across networks. Education: PhD in Physics from the University of Michigan; postdoctoral fellowship at the Santa Fe Institute His research focuses on network science , complex systems , and statistical inference , with an emphasis on computational approaches. His work spans theoretical and applied domains, including: Message passing algorithms for heterogeneous networks Bias correction in social network analysis (friendship paradox) Statistical inference of network structure from noisy data Modeling judicial voting behavior through network interactions Computational cognitive neuroscience of category learning Recent publications highlight interdisciplinary applications in epidemiology, physics, and cognitive science. He actively mentors students in networks, complex systems, and statistical inference.

• Machine Learning
• Distributed Machine Learning
• Spectral Analysis of Neural Networks
• +۴ مورد دیگر

Yaoqing Yang is an Assistant Professor at the Department of Computer Science, Dartmouth College. He earned his PhD in Electrical and Computer Engineering (ECE) from Carnegie Mellon University (CMU) and completed postdoctoral research at UC Berkeley's RISE Lab. His work focuses on robustness in machine learning systems, spectral analysis of neural networks, and algorithm design for structured data like graphs and point clouds. PhD in ECE, Carnegie Mellon University Postdoc, RISE Lab, UC Berkeley BS in Electrical Engineering, Tsinghua University Research interests include diagnosing and mitigating model failures through heavy-tailed spectral analysis, decision boundary studies, and loss landscape visualization. He develops methods such as AlphaPruning and SharpBalance to enhance large language models and ensemble learning. Recent work spans 2025 publications on spectral evolution of neural networks, agentic AI for science, and LLM safety. Key collaborations include Michael W. Mahoney and other researchers. Burke Research Initiation Award, Dartmouth (2024) DOE grant for scientific foundation models (2024) DARPA grant for AI robustness (2024) He serves as Area Chair at NeurIPS 2025 and ICLR 2026, and has presented at Google Research, Lawrence Berkeley National Laboratory, and leading universities worldwide. His lab at Dartmouth engages in theoretical and applied research, with connections to UC Berkeley's RISE Lab and collaborations across institutions like CMU and Tsinghua University.

Arnab Senمشاهده پروفایل

دانشیار

• Probability Theory
• Discrete Harmonic Analysis
• Statistical Physics
• +۳ مورد دیگر

Arnab Sen is an Associate Professor at the School of Mathematics, University of Minnesota. His research focuses on probability theory and discrete harmonic analysis, with emphasis on models from statistical physics such as spin glasses, random graphs, random matrices, and random polynomials. PhD in Statistics, UC Berkeley (2010), advised by Steven N. Evans and Elchanan Mossel Postdoctoral Fellow, Statistical Laboratory, University of Cambridge His research spans discrete probability , statistical physics , and random matrix theory , addressing topics like disorder chaos in spin glasses, eigenvalue distributions, and quantum percolation. He has taught graduate and undergraduate courses including Random Matrix Theory , Introduction to Stochastic Processes , and Multivariable Calculus . His recent publications analyze spin glass models, random matrices, and combinatorial systems.

David Alan Goldbergمشاهده پروفایل

دانشیار

• Applied Probability
• Operations Research
• Stochastic Processes
• +۷ مورد دیگر

David Alan Goldberg is an Associate Professor in the School of Operations Research and Information Engineering (ORIE) at Cornell University, part of Cornell Engineering. He joined Cornell in 2017 and previously held the A. Russel Chandler III Associate Professorship at Georgia Tech’s Industrial and Systems Engineering department. Goldberg earned his Ph.D. in Operations Research from MIT (2011) and a B.S. in Computer Science from Columbia University (2006). Education: B.S. in Computer Science, Columbia University (2006) Ph.D. in Operations Research, MIT (2011) Research Interests: Goldberg’s work focuses on applied probability and stochastic processes, including optimal stopping, inventory and queueing models, combinatorial optimization, and robust optimization. He develops algorithms and insights for complex systems, addressing challenges like the curse of dimensionality. His research spans applications in data science, operations research, and stochastic modeling. Notable contributions include distributionally robust inventory control and high-dimensional decision-making frameworks. Awards and Honors: 2025 Community-Engaged Practice and Innovation Award (David M. Einhorn Center) 2023 Sunny Yau ’72 Teaching Award (Cornell) 2019 INFORMS Applied Probability Society Best Publication Award 2015 NSF CAREER Award Multiple INFORMS Nicholson Student Paper Competitions (First Place, 2019 & 2015) Teaching and Service: Goldberg leads Cornell ORIE’s undergraduate research program, connecting students to real-world applications of OR and data science. He teaches courses in probability modeling, stochastic models, and academic skills for PhD students. He chairs the INFORMS Applied Probability Society and serves on editorial boards for Operations Research and Stochastic Systems . At Cornell, he advises the Undergraduate ORIE Society and directs undergraduate studies in ORIE. Labs & Collaborations: Goldberg’s research integrates theoretical rigor with practical applications, often involving collaborations across disciplines. His work bridges operations research, statistics, and computer science to address modern challenges in inventory systems, queueing networks, and decision-making under uncertainty.

Samuel Johnstonمشاهده پروفایل

مدرس

• Probability
• Stochastic Processes
• Free Probability
• +۷ مورد دیگر

Samuel Johnston is a Lecturer in Probability Theory at the Department of Mathematics, King's College London, affiliated with the Faculty of Natural, Mathematical & Engineering Sciences. He joined King's in 2022 after postdoctoral roles at the University of Bath, University of Graz, and University College Dublin. MMath, University of Oxford (2014) PhD in Probability, University of Bath (2017) Johnston's research spans probability theory, with a focus on stochastic processes involving branching, coalescence, and fragmentation. He actively explores free probability, random matrices, integrable combinatorics, and combinatorial approaches to the Jacobian conjecture. His work intersects with statistical physics and asymptotic geometric analysis. Recent publications highlight coalescent structures in heavy-tailed branching processes, integrable probability models, free probability via entropic transport, and convexity in high dimensions. Keywords include universality classes, Berry-Esseen bounds, and fragmentation-scaling limits. Samuel has not been mentioned to have received specific scientific awards or honors. He advises PhD students Rohan Shiatis (2023-) and Neil Mukerji (2024-). Collaborations span institutions in the UK, USA, Mexico, Austria, and Poland, with invited talks at global conferences including Xiangtan University, Imperial College London, and UCLA.

• Branching processes
• Random trees
• Reinforcement processes
• +۹ مورد دیگر

Cécile Mailler is a Reader in Probability at the University of Bath, where she is a member of the probability group Prob-L@B. She has held significant research positions including an EPSRC postdoctoral fellowship (2018-2021) titled "Random trees: analysis and applications" and previously worked as a postdoc at Prob-L@B (2013-2016) as part of Peter Mörters' EPSRC project "Emergence of Condensation in Stochastic Networks". She earned her PhD under the supervision of Brigitte Chauvin and Danièle Gardy at the Laboratoire de Mathématiques de Versailles. Her research focuses on probability theory with emphasis on branching processes, random trees, reinforcement mechanisms, Pólya urns, stochastic approximation, random networks, and statistical physics. She has made significant contributions to understanding preferential attachment models, zero-range processes, and random Boolean trees. Her work bridges theoretical probability with applications in statistical physics and combinatorics. Analysis of her recent publications shows a strong focus on random tree structures, branching processes, and reinforcement learning algorithms, with applications spanning from network theory to statistical mechanics. Her research demonstrates sophisticated mathematical techniques applied to complex stochastic systems, particularly those with reinforcement mechanisms and memory effects. Associate Editor of the Applied Probability Trust (since October 2020) Associate Editor of Stochastic Processes and Their Applications (since March 2022) Author of a general introduction to Pólya urns for the LMS Newsletter (November 2020) Co-organizer of the "Random Walks: Applications and Interactions" conference at CIRM (January 2026) She actively supervises PhD students working on topics including the multi-city ants process, Pólya urns with growing initial composition, large deviations for the Monkey walk, and competing growth processes. She has secured research funding through EPSRC fellowships and has been involved in multiple collaborative projects with prominent researchers in probability theory. Mailler regularly teaches mini-courses on advanced probability topics at international summer schools and workshops, demonstrating her commitment to knowledge dissemination in the field.

Miklos Z. Raczمشاهده پروفایل

استادیار

• Probability
• Statistics
• Computer Science
• +۱۰ مورد دیگر

Miklos Z. Racz is an Assistant Professor at Northwestern University with a joint appointment in the Department of Computer Science and the Department of Statistics and Data Science. He is affiliated with the IDEAL Institute. Previously, he was an Assistant Professor at Princeton University (ORFE Department) and a postdoc at Microsoft Research. His research focuses on probability, statistics, computer science, and information theory, with emphasis on combinatorial statistics, discrete probability, and applied probability. Key interests include statistical inference on random discrete structures like random graphs, community detection, latent geometry inference, and DNA data storage. He has advised numerous PhD and undergraduate students. Education: PhD in Statistics (UC Berkeley, 2015), MS in Computer Science (UC Berkeley), MS in Mathematics (Budapest University of Technology and Economics). Research interests span random graph theory, network analysis, information cascades, and computational biology. He teaches courses like Mathematical Foundations of Computer Science and Probability for Statistical Inference. His work has been published in top venues like Annals of Applied Probability, NeurIPS, and IEEE journals. Notable contributions include breakthroughs in graph matching algorithms for stochastic block models, community recovery, and DNA synthesis optimization. His research has practical applications in data storage and network science.

• Theoretical Computer Science
• Graph Algorithms
• Lower Bounds
• +۷ مورد دیگر

Nicole Wein is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan, where she is a member of the Theory of Computation Lab within the Computer Science and Engineering Division. Her research focuses on theoretical computer science, particularly graph algorithms and lower bounds across various domains including distance-estimation, dynamic, parameterized, distributed, and online algorithms. Education: PhD in Computer Science from MIT, advised by Virginia Vassilevska Williams Master's in Computer Science from Stanford University B.S. in Computer Science/Mathematics from Harvey Mudd College Nicole's research centers on theoretical aspects of graph algorithms and computational complexity. She investigates fundamental questions about how algorithms can efficiently handle changing data, extract information from graphs in linear time, and understand the structure of shortest paths, especially in directed graphs. Her work spans multiple algorithmic paradigms including dynamic algorithms that adapt to changing inputs, parameterized approaches for hard problems, and fine-grained complexity that establishes precise relationships between problem difficulty. Analysis of Nicole's recent publications reveals a strong focus on graph algorithms, particularly shortest path problems, spanners, and hardness results. Her work often bridges theoretical insights with practical implications, developing novel techniques for distance estimation, dynamic graph processing, and approximation algorithms. A significant portion of her research examines the structural properties of graphs that enable or constrain efficient computation, with applications across computer science. Nicole actively mentors students at various levels. She currently advises PhD student Jubayer Nirjhor and has worked with undergraduate researchers including Sam Hiken (now a pre-doc at MIT), Michael Wang, and Tony Zhang. Her teaching includes foundational courses like EECS 376: Foundations of Computer Science and specialized courses such as EECS 598: Graph Algorithms. Nicole contributes to the academic community through service as a program committee member for major conferences including SOSA 2025, FOCS 2025, SODA 2025, and others. She co-organized the June 2023 DIMACS workshop on Modern Techniques in Graph Algorithms and previously organized Algorithms Office Hours at MIT to improve communication between theory and applications of algorithms.

• Explainable AI
• Computational Argumentation
• Quantitative Bipolar Argumentation Frameworks
• +۲ مورد دیگر

Xiang Yin is a Research Associate at the Department of Computing in Imperial College London , affiliated with the Computational Logic and Argumentation group (CLArg) . His work bridges Explainable AI (XAI) and Computational Argumentation (CA) , focusing on the explainability of Quantitative Bipolar Argumentation Frameworks (QBAFs) through attribution and counterfactual explanations. Research Interests: Explainable AI (XAI) Computational Argumentation Quantitative Bipolar Argumentation Frameworks Model Interpretability Human-AI Interaction Logical Reasoning for AI Publication Trends reveal a focus on argumentation-based explainability, with 2025-2024 works addressing large language models for claim verification, truth-discovery frameworks, and counterfactual explanations. Earlier works (2023-2022) explore random forest explanations, faithfulness criteria, and QBAF analysis. His 2018 publications on aircraft prediction systems demonstrate applied machine learning expertise. Education PhD in Artificial Intelligence under Prof. Francesca Toni and Dr. Nico Potyka Pre-PhD: Machine Learning R&D Engineer at Baidu Labs & Teams Xiang is part of the CLArg group at Imperial College London, focusing on integrating computational argumentation with AI explainability and contestability.

• Probability Theory
• Random Discrete Structures
• Random Trees
• +۶ مورد دیگر

Igor Kortchemski is a CNRS researcher at the Department of Mathematics and Applications (DMA) at École Normale Supérieure, Paris, and a lecturer in the Department of Applied Mathematics at École Polytechnique. His primary research focuses on the continuous limits of random discrete models, particularly examining how discrete combinatorial structures converge to continuous objects under appropriate scaling. His educational background includes a PhD in Mathematics (2012) under Jean-François Le Gall at École Normale Supérieure and a Habilitation à diriger des recherches (HDR) in Mathematics (2016). Kortchemski's research spans several interconnected areas: Random trees and Galton-Watson processes with heavy-tailed distributions Random planar maps and their geometric properties Growth-fragmentation processes and their connections to Lévy processes Scaling limits of combinatorial structures and their continuous counterparts His publication record shows a consistent focus on the geometric properties of random discrete structures, with recent work (2023-2025) exploring uniform attachment processes with freezing, critical tree phenomena, and the mesoscopic geometry of sparse random maps. His research often involves sophisticated probabilistic analysis combined with combinatorial insights. Scientific recognition includes: prix de thèse solennel Perrissin-Pirasset / Schneider de la chancellerie des Universités de Paris (2012) Kortchemski actively contributes to academic service: Examiner for the minor math exam at École Polytechnique (FUF) since 2023 Member of the mathematics jury for ENS International Selection (2023) Member of the jury for the external mathematics competitive examination (Agrégation) since 2021 Member of the jury for the Arts and Economic and Social Sciences Bank (B/L) mathematics exams (2015-2018) He mentors the next generation of researchers as co-director of Antoine Aurillard's and Vanessa Dan's theses (both since 2023), and previously directed Etienne Bellin's (2020-2023) and Paul Thevenin's (2017-2020) theses.

Michael Krivelevichمشاهده پروفایل

استاد

• Probabilistic Methods in Combinatorics
• Random Structures
• Hypergraphs
• +۷ مورد دیگر

Prof. Michael Krivelevich holds the Baumritter Chair in Combinatorics at the School of Mathematical Sciences, Tel Aviv University. His research focuses on probabilistic methods in combinatorics, random graphs, and positional games. He has authored influential books such as Positional Games and contributed to foundational work in random graph theory. Currently teaching Introduction to Combinatorics and Graph Theory (Spring 2025), he has extensive experience in courses like Graph Theory and Hypergraph Coloring. His work bridges theoretical computer science, coding theory, and combinatorics, with over 150 publications. Recent research explores game-theoretic thresholds, random graph evolution, and equitable coloring algorithms. Education: Ph.D. in Mathematics, Tel Aviv University (not explicitly stated, inferred from career trajectory). Research Interests Krivelevich's work emphasizes random structures , extremal graph theory , and probabilistic combinatorics . He investigates phase transitions in random graphs, positional game strategies, and algorithmic challenges in graph coloring. His contributions include proving sharp thresholds for Hamilton cycle games and analyzing WalkSAT performance on smoothed k-CNF formulas. Collaborations span theoretical computer science and discrete mathematics. Publications Recent articles address Hamiltonicity in Maker-Breaker games, equitable coloring of random graphs, and smoothed analysis of satisfiability processes. His work often combines rigorous proofs with algorithmic insights. Teaching & Mentorship Guides students through advanced combinatorial topics and has taught foundational courses since 2002. No explicit student listings available in provided texts.

Antal Jaraiمشاهده پروفایل

مدرس ارشد

• Probability Theory
• Statistical Physics
• Random Processes
• +۳ مورد دیگر

Dr. Antal Jarai is a Senior Lecturer in the Department of Mathematical Sciences at the University of Bath, where he also contributes to the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa) and the Probability Laboratory at Bath. His work bridges probability theory and statistical physics, focusing on random processes with spatial and/or temporal structure. PhD in Mathematics from Cornell University (2000) BSc from Eötvös Loránd University (1996) Dr. Jarai's research explores problems motivated by statistical physics, including percolation, random walks, branching random walks, uniform spanning trees, and Abelian sandpiles. His recent publications address interlacement limits, asymptotics of optimal policies, resistance scaling, and wireless network proximity. He actively collaborates on interdisciplinary projects in network mathematics and wireless technology. Key trends in his publications include asymptotic analysis (5/5 papers), random walk theory (4/5), and probabilistic methods in statistical physics (4/5). Subfields span interlacement theory, self-organized criticality, stochastic geometry, and disordered systems. Royal Society Grant for 'Zero Dissipation Limit in Abelian Sandpiles' London Mathematical Society Grant for 'Critical Exponents in Sandpiles via Exact Sampling' EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa) Dr. Jarai serves as Principal Investigator on multiple research grants and supervises students in probability and applied mathematics. He has contributed datasets on sandpile simulations and collaborates internationally on network mathematics projects.