جست‌وجوی استاد و پژوهشگر

Maria Chudnovskyمشاهده پروفایل

استاد

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

Maria Chudnovsky is a Professor in the Department of Mathematics at Princeton University. Her research focuses on structural graph theory, particularly in areas such as graph decomposition, induced subgraphs, and algorithmic applications of graph structure. She is renowned for her contributions to understanding perfect graphs, even-hole-free graphs, and the Erdős–Hajnal conjecture. Her work often explores the interplay between graph structure and algorithmic efficiency, with applications in combinatorial optimization and theoretical computer science. Notable contributions include foundational results on tree decompositions, chromatic number bounds, and the structure of metrizable graphs. Recent research trends include investigations into induced subgraph obstructions, tree independence numbers, and the properties of sparse graphs. She has published extensively on topics such as clique-stable set separation, rainbow matchings, and the complexity of graph coloring problems in restricted graph classes. Chudnovsky has been involved in significant collaborative projects, including work funded by the DMS-EPSRC grant 'The Power of Graph Structure' (2021). Her research frequently bridges theoretical insights with practical algorithm design, contributing to both fundamental and applied areas of discrete mathematics.

Vijay Vaziraniمشاهده پروفایل

استاد

• Algorithmic Game Theory
• Matching Markets
• Computational Complexity
• +۶ مورد دیگر

Vijay Vazirani is a Distinguished Professor in the Department of Computer Science at the University of California, Irvine , where he directs the ACO Center @ UCI . He earned his Ph.D. in Computer Science from UC Berkeley and a S.B. from MIT. Vazirani is a Guggenheim Fellow , ACM Fellow , and 2022 INFORMS John von Neumann Theory Prize recipient. Research Areas: Algorithmic Game Theory, Matching Markets, Computational Complexity, Approximation Algorithms His groundbreaking work includes co-founding algorithmic game theory and solving a 30-year-old problem with an NC algorithm for perfect matching in planar graphs . Recent research focuses on matching-based market design, with a $500K NSF grant for advancing algorithms in matching and market equilibria. His 15 most recent papers explore topics like core imputations, stable matching lattices, and Nash bargaining solutions. Scientific Awards: Guggenheim Fellowship ACM Fellow 2022 INFORMS John von Neumann Theory Prize Vazirani advises numerous Ph.D. students and postdocs, including Tung Mai , Thorben Trobst , and Rohith Reddy Gangam . He contributes to major workshops and co-edited foundational texts like Algorithmic Game Theory and Online and Matching-Based Market Design .

Jacob D. Leshnoمشاهده پروفایل

دانشیار

• Market Design
• Game Theory
• Microeconomic Theory
• +۳ مورد دیگر

Jacob D. Leshno is an Associate Professor of Economics and Robert H. Topel Faculty Scholar at the University of Chicago Booth School of Business. His research employs game theory, applied mathematics, and microeconomic theory to study allocation mechanisms and marketplace design, with applications spanning school choice systems, patient assignments to nursing homes, and decentralized cryptocurrency protocols. Professor Leshno's academic background includes: PhD in Economics from Harvard University, completed under Nobel laureate Alvin Roth M.Sc. in Pure Mathematics from Tel Aviv University B.Sc. in Pure Mathematics from Tel Aviv University His research program centers on market design theory with two primary strands. The first focuses on matching markets, where he developed tractable cutoff characterizations that clarify market structures for college admissions and medical residency matching (NRMP). His work demonstrates how price discovery mechanisms can streamline inefficient processes like college applications and subsidized housing allocation. The second strand examines cryptocurrencies and blockchain technology, investigating how open-source computer code functions as market rules in decentralized systems. This research explores both the economic security of permissionless consensus and fundamental limitations of proof-of-work protocols. Professor Leshno's publications reveal a cohesive research trajectory applying economic theory to increasingly complex market structures. His work consistently bridges theoretical rigor with practical implementation, evolving from traditional matching markets to the frontier of decentralized digital systems. Publications in top journals like American Economic Review and Journal of Political Economy demonstrate both analytical depth and real-world relevance across education, healthcare, and financial technology sectors. Professor Leshno has received significant recognition for his contributions: ACM SIGecom Test of Time Award for foundational work in matching markets INFORMS Frederick W. Lanchester Prize for outstanding contributions to operations research Prior to Chicago Booth, Professor Leshno served as Assistant Professor at Columbia Business School and completed a postdoctoral fellowship at Microsoft Research New England, following industry experience at Yahoo! and IBM. He teaches MBA courses in Competitive Strategy and Market Design, and developed a PhD seminar bridging computer science theory with economic principles for distributed systems. His research continues to influence both academic theory and practical implementations of market mechanisms across multiple sectors. Professor Leshno maintains active collaborations with leading researchers including Itai Ashlagi, Irene Lo, and Gur Huberman, advancing the theoretical foundations of market design while addressing contemporary challenges in digital marketplaces and allocation systems.

• Game Theory
• Bounded Rationality
• Economic Theory
• +۳ مورد دیگر

Ariel Rubinstein is a prominent Israeli economist and Professor of Economics at Tel Aviv University's School of Economics and the Department of Economics at New York University. Born on April 13, 1951, he has established himself as a leading figure in game theory and economic theory over his decades-long career. His research spans Game Theory, Bounded Rationality, Economic Theory, and Experimental Economics. Rubinstein is particularly renowned for developing the Rubinstein bargaining model, published in 1982, which describes two-person bargaining as an extensive game with perfect information. He also co-authored the highly influential A Course in Game Theory (1994) with Martin J. Osborne, which has been cited over 4,000 times. His recent scholarly output shows continued productivity across multiple subfields of economic theory, with a focus on behavioral aspects of decision-making, implementation theory, and the philosophical foundations of economic modeling. His work often challenges conventional approaches in economics while maintaining rigorous theoretical foundations. Honorary Fellow recognition Author of highly cited textbooks and scholarly articles Creator of educational resources including game theory experiments website Rubinstein maintains an active teaching role at NYU, where he has taught PhD microeconomics courses through 2024. His work extends beyond traditional academic boundaries through his 'Rubinstein's Atlas of Cafes where one can think,' his political commentary, and his engagement with public discourse on economic methodology and social issues. He has created protest materials and written extensively on contemporary political matters, particularly regarding the Israeli-Palestinian conflict. His laboratory focuses on Economic Theory, Bounded Rationality, Game Theory, and Experimental Economics, reflecting his interdisciplinary approach to understanding human decision-making within economic frameworks.

Peter G. Troyanمشاهده پروفایل

دانشیار

• Microeconomic theory
• Game theory
• Market design
• +۳ مورد دیگر

Peter G. Troyan is an Associate Professor of Economics and Director of Graduate Studies at the University of Virginia, where he has served on the faculty since 2014. His research bridges theoretical and experimental economics with practical applications in market design, focusing on strategic behavior in matching systems and auction mechanisms. Education: Ph.D. in Economics, Stanford University (2014) B.S. in Mathematics (with High Honors) and Physics, University of Michigan (2008) Troyan's research centers on microeconomic theory with emphasis on game-theoretic foundations of market design. His work develops novel frameworks for matching under constraints, analyzes strategic manipulation in allocation mechanisms, and pioneers experimental validations of theoretical predictions. Key contributions include formalizing 'obvious strategyproofness' as a solution concept and designing ranking methods that improve welfare in competitive matching processes. His interdisciplinary approach integrates experimental economics to test theoretical models in real-world settings like school choice and labor markets. His publication record reveals a consistent trajectory toward foundational contributions in mechanism design, with increasing focus on simplicity principles and behavioral realism. Recent work in Econometrica establishes theoretical limits of mechanism simplicity, while experimental studies in Games and Economic Behavior validate preference structures in matching markets. The recurring themes across his publications demonstrate how theoretical insights can be operationalized to solve allocation problems with distributional constraints. Scientific Awards: Best Paper Award and Exemplary Theory Paper Award at ACM Conference on Economics and Computation (EC19) UVA Quantitative Collaborative (2022) and Arts & Sciences Research Grant (2022) Roger Sherman Fellowship (2019-2020) and multiple university research grants Stanford and University of Michigan fellowships during graduate training Troyan directs the Economics Department's graduate program while securing continuous research funding, including five consecutive Bankard Fund grants (2017-2024) supporting his theoretical and experimental work. His service includes editorial roles at the American Economic Journal: Microeconomics and extensive peer review for top economics journals. As an active conference participant, he regularly presents at the ACM Conference on Economics and Computation and Econometric Society meetings, contributing to the market design research community through the University of Virginia Bankard Workshop in Economic Theory. His leadership extends to mentoring graduate students in economic theory research and collaborating with international scholars like Marek Pycia and Thayer Morrill. Current projects explore desirable ranking methodologies and the boundaries of strategyproof allocation mechanisms, positioning his work at the forefront of market design theory.

Nicole Megowمشاهده پروفایل

استاد

• Combinatorial optimization
• Discrete optimization
• Mathematical optimization
• +۱۱ مورد دیگر

Nicole Megow is a Professor holding the chair for Combinatorial Optimization in the Faculty of Mathematics and Computer Science at the University of Bremen since 2016. She is affiliated with several research clusters including Humans on Mars Initiative, Minds, Media, Machines, and Dynamics in Logistics. Her academic journey includes positions at TU Berlin, Max Planck Institute for Informatics, TU Darmstadt, and TU Munich. Professor Megow's research focuses on mathematical optimization, algorithm design and analysis, and operations research. Her specific interests span combinatorial and discrete optimization, efficient algorithms, scheduling theory, resource allocation, packing problems, network design, routing, and uncertainty models including online, stochastic, robust, and explorable approaches. Her work bridges theoretical foundations with practical applications in logistics and decision-making systems. Her recent publications demonstrate a strong trend toward integrating prediction models with traditional optimization frameworks, particularly in scheduling and matching problems. She has made significant contributions to understanding the role of uncertainty in optimization problems, developing algorithms that work effectively with incomplete or uncertain information. Her work spans multiple prestigious venues including Mathematical Programming, Algorithmica, SODA, STACS, and NeurIPS. Dissertation Award by the German Operations Research Society (2007) Berlin Science Award for Young Researchers (2013) Heinz Maier-Leibnitz Prize (2013) Listed among Germany's top 40 researchers below 40 (Capital, 2014, 2015) Professor Megow actively supervises PhD students and postdocs, including Max Stahlberg, Joes Biburger, Sarah Morell, Bart Zondervan, Zhenwei Liu, and Alexander Lindermayr. She serves on numerous program committees for major conferences including SODA, IPCO, and STOC, and holds editorial positions for several prestigious journals. Her current research projects include Optimization under Explorable Uncertainty (DFG funded), How robots learn how to use structure (seed grant from MMM research cluster), and Scheduling Invasive Multicore Programs Under Uncertainty (within TCRC 89).

Anna R. Karlinمشاهده پروفایل

استاد

• Theoretical Computer Science
• Algorithm Design and Analysis
• Probabilistic Algorithms
• +۶ مورد دیگر

Anna R. Karlin is a Professor and the Bill & Melinda Gates Chair in Computer Science & Engineering at the University of Washington's Paul G. Allen School of Computer Science & Engineering. She serves as Associate Director of Graduate Studies and leads research in theoretical computer science within the Theory & Models of Computation focus area. Ph.D. from Stanford University (1987) Former researcher at Digital Equipment Corporation's Systems Research Center (5 years) Professor Karlin's research centers on theoretical computer science, with specific expertise in algorithm design and analysis, particularly probabilistic and online algorithms. Her work spans multiple interdisciplinary domains including algorithmic game theory, economics and computation, data mining, operating systems, networks, and distributed systems. Her research has evolved from foundational algorithmic work to impactful applications in market design, auction theory, and pricing mechanisms. Karlin's publication record demonstrates a consistent trajectory from classical theoretical computer science toward algorithmic game theory and mechanism design. Her recent work focuses on approximation algorithms for NP-hard problems, auction design, revenue maximization, and stable matching problems, with applications in online advertising, network economics, and resource allocation. She has developed influential algorithms for the Traveling Salesman Problem and made significant contributions to understanding interdependent valuations in combinatorial auctions. Bill & Melinda Gates Chair in Computer Science & Engineering Professor Karlin has advised numerous doctoral students throughout her career, with former students including prominent researchers like Jason Hartline, Frank McSherry, and Kira Goldner. Her collaborative research has been supported by various grants, including NSF funding (CCF-1813135 mentioned in her publications). She co-authored the influential textbook Game Theory, Alive with Yuval Peres, which serves as a rigorous introduction to game theory with applications across multiple disciplines. As a leader in theoretical computer science, Professor Karlin maintains active involvement in the Theory of Computation research group at the Allen School, fostering collaboration between theoretical foundations and practical applications in computer science.

James Martinمشاهده پروفایل

مدرس

• Probability Theory
• Statistical Physics
• Random Graphs
• +۹ مورد دیگر

James Martin is a Lecturer at the Department of Statistics, University of Oxford . He is affiliated with St Hugh's College and has been actively involved in organizing probability seminars since 2018. Research Interests Probability theory Random graphs and percolation Interacting particle systems Models of random growth and coagulation-fragmentation Queueing networks Combinatorial games Teaching Courses: Prelims Probability , Part A Probability , Part B Statistical Lifetime Models , Part C Probabilistic Combinatorics His publications focus on probability theory , statistical physics , and combinatorial structures . Recent work includes studies on last-passage percolation, multispecies exclusion processes, and integrable probability models. James Martin collaborates with researchers from institutions such as Uppsala University, University of Cambridge, Imperial College London, and Kyoto University. He has been a key organizer for the Oxford Probability Seminar since 2018.

• Economic Theory
• Game Theory
• Mechanism Design
• +۴ مورد دیگر

Seungjin Han is a Professor in the Department of Economics at McMaster University, specializing in economic theory with applications in game theory, mechanism design, and public policy. His work bridges theoretical rigor with practical economic questions in taxation, matching markets, and corporate finance. His academic foundation includes a Bachelor of Economics from Korea University, Seoul; Master of Arts in Economics from McGill University, Montreal; and PhD in Economics from the University of Toronto. Research interests center on competing mechanism games, signaling theory, and market design. Key contributions address equilibrium analysis in strategic environments, tax incidence in differentiated markets, and incentive structures in matching frameworks. His theoretical work frequently appears in top journals like the Journal of Economic Theory and Journal of Mathematical Economics. Recent publications (2021-2025) reveal consistent focus on competing mechanisms and signaling dynamics, with growing emphasis on policy-relevant applications in public finance and labor markets. Over half his work involves collaborative research networks spanning international institutions. Teaching encompasses graduate-level Microeconomic Theory (ECON 721), Mathematical Methods (ECON 765), and Advanced Economic Theory (ECON 4T03) since 2017, reflecting deep engagement with core economic methodology.

• Operations Research
• Market Design
• Algorithmic Game Theory
• +۳ مورد دیگر

Pengyu Qian serves as an Assistant Professor in the Department of Operations and Technology Management at Boston University's Questrom School of Business, where he conducts research at the intersection of operations research, economics, and algorithmic systems design. Education PhD, Graduate School of Business, Columbia University, 2021 B.Sc., Peking University, 2015 Research Interests Dr. Qian specializes in theoretical and applied problems within matching markets, dynamic resource allocation, and queueing network optimization. His work develops algorithmic solutions for market inefficiencies using tools from game theory, stochastic modeling, and mechanism design. Current investigations focus on partner competition dynamics in two-sided markets, incentive structures for resource pooling, and blind control policies in closed networks where system states remain partially observable. Publication Trends His 2018-2024 publications demonstrate consistent output in top-tier venues including Management Science and ACM Economics & Computation conferences, with increasing emphasis on real-world market applications. The research trajectory shows progression from foundational queueing network control to sophisticated matching market analyses, maintaining strong theoretical rigor while addressing practical constraints like information asymmetry and dynamic partner competition. Scientific Awards No scientific awards, fellowships, or major honors are documented in the available profile information. Advising and Grants While specific student advisees and grant funding details are not disclosed in the current materials, Dr. Qian's publication record suggests active collaboration with leading researchers including Yash Kanoria and Itai Ashlagi, indicating participation in significant research initiatives within operations management and market design. Labs and Teams Though no dedicated laboratory is specified, his research profile aligns with Boston University's operations management research groups at Questrom, particularly those focused on algorithmic market design and stochastic optimization within business contexts.

• Transportation Modeling
• Applied Operations Research
• Sustainable Transportation
• +۳ مورد دیگر

Jee Eun (Jamie) Kang is an Associate Professor in the Department of Industrial and Systems Engineering at the University at Buffalo, located within the School of Engineering and Applied Sciences. Her research focuses on transportation modeling, applied operations research, and sustainable mobility systems. She has contributed extensively to studies on autonomous vehicles, emergency evacuation planning, and electric vehicle infrastructure. Her work bridges theoretical optimization frameworks with real-world applications, addressing challenges in multi-modal travel environments, disaster logistics, and shared mobility systems. Dr. Kang's research often involves data-driven methodologies, leveraging automated fare collection systems and smart card transactions to infer user behavior and optimize transportation networks. Her publications span topics such as strategic parking location models for autonomous vehicles, pricing mechanisms in bike-sharing systems, and scenario-based analyses for battery electric vehicle feasibility. She actively advises students on projects related to urban mobility, evacuation planning, and transportation sustainability. Scientific Awards: None explicitly mentioned in the provided texts. Dr. Kang's advising and collaborative work include projects on household activity patterns, hydrogen refueling station optimization, and evacuation warning systems. Her research has been applied to real-world scenarios like the Nepal earthquake relief efforts and California's hydrogen infrastructure planning.

Steven O. Kimbroughمشاهده پروفایل

استاد

• Artificial Intelligence
• Computational Rationality
• Decision Support Systems
• +۶ مورد دیگر

Steven O. Kimbrough is a Professor of Operations, Information and Decisions at the Wharton School, University of Pennsylvania. His research spans artificial intelligence, computational rationality, and strategic optimization with applications to political science, economics, and service innovation. He teaches courses like Agents, Games, and Evolution and Thinking With Models , focusing on experimental approaches to bounded rationality and uncertainty in decision-making. Primary Email: kimbrough@wharton.upenn.edu Office: 3730 Walnut Street, 565 Jon M. Huntsman Hall, Philadelphia, PA 19104 His research interests include: Artificial intelligence and metaheuristics for constrained optimization Evolutionary computation in electoral redistricting Agent-based modeling of market dynamics Logic modeling for normative reasoning Text mining applications in event analysis Publications demonstrate expertise in computational economics, political modeling, and service analytics. Recent work focuses on: Empirical validation of electoral compactness Strategic learning in oligopolies Multi-objective matching algorithms Feasible-infeasible solution spaces Service network optimization Teaching emphasizes: Game-theoretic approaches to strategic behavior Modeling life-cycle for energy sustainability Computational experiments in social science

Vijay K Gargمشاهده پروفایل

استاد

• Distributed Algorithms
• Fault Tolerance
• Multicore Computing
• +۷ مورد دیگر

Vijay K Garg is a Professor at the University of Texas at Austin , holding positions in both the Department of Electrical and Computer Engineering and the Department of Computer Sciences . He serves as the Director of the Parallel and Distributed Systems Laboratory and has made significant contributions to distributed computing. Ph.D. in Electrical Engineering and Computer Science from University of California at Berkeley (1988) M.S. in Electrical Engineering and Computer Science from University of California at Berkeley (1985) B.Tech. in Computer Science & Engineering from Indian Institute of Technology, Kanpur (1984) His research focuses on Distributed Algorithms , Fault Tolerance , Multicore Computing , and Lattice Theory applied to computing problems. He has also contributed to areas like Distributed Debugging , Simulation , and Supervisory Control of Discrete Event Systems . His publications show a consistent focus on applying Lattice Theory to Distributed Systems , with recent work on Predicate Detection algorithms, Lattice Agreement , and applications to problems like Stable Marriage and Matching . His work bridges theoretical computer science with practical implementations in distributed systems. Among his scientific awards are: IEEE Fellow (2004) UT Outstanding Inventor (2011) Best Paper Award at 17th International Conference on Runtime Verification (RV'2017) Best Paper Award at 12th International Symposium on Stabilization, Safety, and Security of Distributed Systems (2010) Dean’s Fellowship at UT Austin (2002) Faculty Research Award at UT Austin (2000) Vijay has served on numerous program committees including ICDCS 2022 , SRDS 2021 , ICDCN 2020 , and DISC 2019 . He has also delivered keynote and invited talks at major conferences.

Sherrilyn Roushمشاهده پروفایل

استاد

• Epistemology
• Formal Epistemology
• Knowledge
• +۷ مورد دیگر

Sherrilyn Roush is a Professor in the Department of Philosophy at University of California, Los Angeles (UCLA) , with a PhD in Philosophy from Harvard University (1999). She holds dual B.S. and B.A. degrees with highest distinction from Pennsylvania State University (1988). Her work spans Epistemology , Formal Epistemology , Scientific Method , and Bayesian Reasoning . Harvard University, PhD in Philosophy (1999) University of California, Los Angeles, Department of Philosophy Research interests include the epistemic value of knowledge, probabilistic reasoning in legal contexts, and the distinction between knowledge and understanding. She has extensively analyzed the role of statistical evidence in judicial verdicts, simulation methods in science, and epistemic self-doubt through a Bayesian framework. Her publications cover topics like the epistemic superiority of experiments over simulations, epistemic injustice in testimonial contexts, and evolutionary stable strategies for knowledge. While she has not been explicitly linked to scientific awards or formal student mentoring in the provided texts, her work intersects with medical epistemology through collaborations on self-knowledge in illness .

Fang Kongمشاهده پروفایل

استادیار

• Online Learning
• Reinforcement Learning
• Machine Learning
• +۴ مورد دیگر

Fang Kong is an Assistant Professor in the Department of Statistics and Data Science at the Southern University of Science and Technology (SUSTech). He earned his PhD in Computer Science from Shanghai Jiao Tong University under the supervision of Prof. Shuai Li and received his Bachelor's degree in Software Engineering from Shandong University. Education: PhD in Computer Science, Shanghai Jiao Tong University (2020-2024) Bachelor's Degree in Software Engineering, Shandong University (2016-2020) Dr. Kong is broadly interested in developing theoretically guaranteed algorithms for sequential decision-making problems, with particular focus on multi-armed bandits and reinforcement learning, as well as their applications in online experimentation and recommendation systems. His research spans theoretical foundations of bandit algorithms, matching markets, influence maximization, and online learning under various feedback structures. He has made significant contributions to the understanding of best-of-both-worlds algorithms that perform well in both stochastic and adversarial environments. His publication record shows a strong trajectory of high-impact work in top-tier conferences including NeurIPS, ICML, ICLR, AAAI, WWW, and AAMAS. His research demonstrates expertise in theoretical machine learning with a focus on bandit algorithms, particularly in matching markets and sequential decision-making problems. His work often bridges theoretical guarantees with practical applications in recommendation systems and online experimentation. Scientific Awards: CCF Doctoral Dissertation Award in Agent and Multi-Agent Systems (2025) Baidu Scholarship (2024) National Scholarship for PhD students (2023, 2022) AAMAS Student Scholarship (2023) Microsoft Research Asia Excellence Award (2022) Dr. Kong actively mentors students at various levels, including PhD and Master's students at SUSTech, visiting students from other institutions, and undergraduate researchers. He serves as a reviewer for top machine learning conferences (ICLR, NeurIPS, ICML, WWW) and journals (IEEE PAMI, TMLR). His teaching includes graduate Machine Learning and undergraduate Artificial Intelligence courses at SUSTech.