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Professor
Professor Matthew Elliott is a leading academic in the Faculty of Economics at the University of Cambridge , where he serves as Professor of Economics , Faculty Executive Director of Research , and Director of the Keynes Fund . His work bridges Networked Markets , Game Theory , and Microeconomic Policy , with applications to Supply Chains , Systemic Risk , and Labor Markets .
Assistant Professor
Manxi Wu is an Assistant Professor in Cornell University's School of Operations Research and Information Engineering, specializing in societal networks and game-theoretic approaches to system design. Her research develops computational models for strategic learning and incentive mechanisms in socio-technical systems, with applications to transportation networks and digital platforms. Education: B.S. Applied Mathematics, Peking University (2015) M.S. Transportation, Massachusetts Institute of Technology (2017) Ph.D. Social and Engineering Systems, Massachusetts Institute of Technology (2021) Her research integrates game theory, optimization, and machine learning to address challenges in autonomous services, traffic management, and decentralized decision-making. Current investigations focus on adaptive incentive structures, spatial resource allocation, and equilibrium analysis in complex networked environments. Publication analysis reveals consistent emphasis on game-theoretic frameworks applied to urban mobility systems, with recent work exploring multi-agent reinforcement learning, congestion pricing equity, and electric fleet management. Methodological innovations include novel convergence proofs for decentralized algorithms and computational approaches to fairness constraints. Awards and Honors: Hammer Fellowship UTC Milton Pikarsky Memorial Award Siebel Scholarship EECS Rising Star recognition No information is currently available regarding student advising, research grants, or laboratory affiliations.
Assistant Professor
Zhibin Chen is an Assistant Professor of Engineering at NYU Shanghai and concurrently a Global Network Assistant Professor within the broader New York University system. Since January 2019 he has led research and teaching activities at the Division of Engineering and Computer Science in Shanghai, while maintaining university-wide collaborations through his Global Network appointment. Education Ph.D. in Transportation Engineering, University of Florida (2017) Research Interests Dr. Chen’s scholarship centres on Transportation Network Modeling and Optimization , Intelligent Transportation Systems , and Discrete Optimization . He integrates operations research, data science, and engineering to address emerging challenges in electric mobility, autonomous vehicles, and large-scale urban networks. Recent thrusts include: Data-driven analytics of electric-vehicle charging behaviour under usage heterogeneity. Optimization of charging and swapping infrastructure for electric buses and trucks. Network-level deployment and control strategies for connected and automated vehicles. Day-to-day traffic dynamics and equilibrium models with elastic demand. Pricing, policy, and incentive design for sustainable transportation systems. Scientific Awards Stella Dafermos Best Paper Award – awarded at the 95th Transportation Research Board Annual Meeting. Ryuichi Kitamura Paper Award – also conferred at the 95th TRB Annual Meeting. Editorial & Professional Service Dr. Chen currently serves on the Editorial Advisory Board of Transportation Research Part C: Emerging Technologies , shaping the editorial direction of the leading journal in his field. Grants & Collaborations While specific grant identifiers are not disclosed in the provided text, Dr. Chen’s extensive publication record in top-tier journals ( Transportation Science , Transportation Research Parts B, C, D , IEEE ITS , Applied Energy ) and his editorial role indicate sustained research funding and active collaboration with international partners across North America and China. Laboratories & Teams Operating within the Division of Engineering and Computer Science at NYU Shanghai , Dr. Chen leads a research group focused on next-generation mobility analytics, leveraging the university’s interdisciplinary ecosystem and NYU’s Global Network resources to advance smart and sustainable transportation.
Professor
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.
Professor
Professor Fang Liu serves as a Professor of Operations Management at Durham University Business School (DUBS). She joined Durham University in 2023 after academic positions at The University of Chinese Academy and Nanyang Technological University, bringing extensive expertise in supply chain systems and operational resilience. Her educational foundation includes a doctoral degree in Operations Management from Duke University's Fuqua School of Business, USA. Liu's research centers on Supply Chain Resilience, Inventory Management, E-commerce and Warehouse Management, and Corporate Social Responsibility and Sustainability. Her work develops innovative frameworks for mitigating disruption risks while optimizing resource allocation, with direct applications in global logistics networks and sustainable operations. She bridges theoretical rigor with practical implementation across diverse sectors. Analysis of her publication trajectory reveals a strategic evolution from foundational inventory theory toward contemporary challenges in e-commerce logistics and sustainable supply chains. Her recent work increasingly addresses healthcare operations and carbon-neutral supply chain design, consistently published in premier journals like Operations Research and Production and Operations Management. Professor Liu has secured multiple competitive research grants as Principal or co-Principal Investigator. She actively translates academic insights into practice through collaborations with organizations including Cummins, Singapore IFRC, and Meide, focusing on operational optimization and resilience building. While specific lab structures aren't detailed, her industry partnerships demonstrate applied research engagement, particularly in developing solutions for complex operational challenges faced by multinational corporations and humanitarian organizations.
Associate Professor
Weining Kang is an Associate Professor in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC). Her research focuses on probability theory, stochastic processes, stochastic networks, and queueing systems. She holds a Ph.D. in Mathematics from the University of California, San Diego (2005). Her work emphasizes fluid models for many-server queues, stochastic networks with abandonment, and reflected diffusions. Notable contributions include analyzing nonlinear Volterra equations in queueing systems, equivalence of fluid models for Gt/GI/N+GI queues, and stationary distribution characterizations for reflected diffusions. She collaborates frequently with experts like K. Ramanan and G. Pang on stochastic network dynamics and performance analysis. Recent publications (2023-2007) explore long-time limits of measure-valued equations, submartingale problems for diffusions, and diffusion approximations for input-queued switches. Her work bridges theoretical stochastic analysis with practical applications in operations research and network engineering. While no formal awards are listed, her extensive peer-reviewed publications and collaborative research highlight her contributions to stochastic systems analysis. She advises on fluid model methodologies and has contributed to ACM Sigmetrics and SIAM journals.
Lecturer
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.
Assistant Professor
László Kozma is an Assistant Professor at the Theoretical Computer Science group of the Institute of Computer Science (Freie Universität Berlin). He obtained his PhD from Saarland University under Raimund Seidel, followed by postdoctoral positions at Tel Aviv University and TU Eindhoven. His research focuses on self-adjusting data structures , adaptive algorithms , and combinatorial optimization with applications to problems like the Traveling Salesman Problem, binary search trees, and geometric data structures. Academic Affiliation: Freie Universität Berlin (since 2018) Education: PhD in Computer Science (Saarland University, 2016); postdoc at Tel Aviv University and TU Eindhoven. His work explores the intersection of data structures, combinatorial algorithms, and geometric methods. He has made significant contributions to problems involving pattern-avoidance in inputs, saddlepoint detection , and self-adjusting heaps . Key areas include: Adaptive algorithms for pattern-avoiding inputs Optimal tree and heap structures Geometric and stochastic approaches to optimization Complexity analysis of classical algorithms Recent publications highlight efficient solutions for exponential cut problems (ESA 2025), balanced TSP partitioning (EuroCG 2025), and randomized saddlepoint algorithms (ESA 2024). His research often bridges theory and practice, exemplified by the smooth heap implementation and fun projects like Recursi and Cuckoo Hashing visualization.
Professor
Grigory Mikhalkin is a Full Professor at the University of Geneva, where he has been a faculty member since 2008. He is considered one of the founders of Tropical Geometry, a domain of algebraic geometry governed by (max,+)-calculus where geometric objects degenerate to their piecewise-linear limits. He leads the "ALGEBRA AND GEOMETRY" research group at the university. Mikhalkin studied at Leningrad and Michigan State University under the supervision of Oleg Viro and Selman Akbulut. After receiving his PhD in 1993, he completed postdoctoral training at Princeton, Bonn, Toronto, Berkeley, and Harvard (1993-2000). He served as associate and then full Professor at the University of Utah before moving to the University of Toronto, eventually joining the University of Geneva in 2008. Mikhalkin's primary research areas are Geometry and Topology, with a particular focus on Tropical Geometry. His work bridges algebraic geometry with combinatorial structures, exploring how complex geometric objects can be understood through their piecewise-linear tropical counterparts. This approach has proven fruitful in solving problems in enumerative geometry and has connections to mathematical physics through the study of sandpile models and self-organized criticality. His research group actively explores the connections between tropical geometry, symplectic geometry, and real algebraic geometry, organizing regular seminars including the "Séminaire Fables Géométriques." The recent publications of Professor Mikhalkin demonstrate a strong focus on the intersection of tropical geometry with sandpile models and self-organized criticality. His work has evolved to examine tropical aspects of number theory, lattice sums, and even applications to economics through auction theory. A significant portion of his recent research explores the patterns and structures that emerge in sandpile models across various lattices and dimensions, connecting discrete mathematics with continuum limits through tropical techniques. Prize of the St. Petersburg Mathematical Society (1999) Silver Medal of the Mexican Mathematical Society (2011) Canada Research Chair (2004-2009) Friedrich-Wilhelm-Bessel Research Award of the Alexander-von-Humboldt Foundation (2007-2008) European Research Council Advanced Grant (2010-2015) Chair of Fondation Sciences Mathématiques de Paris (2013-2015) Mikhalkin has successfully advised several PhD students to completion, including Kristin Shaw (2011), Lionel Lang (2014), Nikita Kalinin (2015), Mikhail Shkolnikov (2017), and Johannes Josi (2018). His research has been supported by prestigious grants including the ERC Advanced Grant and the Canada Research Chair. He presented his work at the Bourbaki seminar in 2003 and was selected as a Geometry speaker at the International Congress of Mathematicians in 2006, highlighting the significance of his contributions to the field. Professor Mikhalkin leads the "ALGEBRA AND GEOMETRY" research group at the University of Geneva, which includes current members Thomas Blomme, Francesca Carocci, Aloïs Demory, Gurvan Mével, and Antoine Toussaint. The group has a strong track record of postdoctoral fellows and alumni, including notable researchers such as Ivan Bazhov, Johan Bjorklund, Rémi Crétois, and others. They organize several seminars including the "Séminaire Fables Géométriques" and have historical connections to the Battelle Seminar and Tropical working group Seminar.
Assistant Professor
Claire Vernade is a Group Leader at the University of Tübingen in the Cluster of Excellence Machine Learning for Science. She leads an active research group focused on theoretical aspects of sequential decision making, with particular expertise in bandit problems and reinforcement learning theory. Her work bridges theoretical foundations with practical applications in scientific discovery. Her research interests span sequential decision making, bandit problems, theoretical Reinforcement Learning, Learning Theory, and principled learning algorithms. She has made significant contributions to understanding non-stationary environments, lifelong learning frameworks, and the theoretical foundations of bandit algorithms. Her work on "Eigengame: PCA as a Nash Equilibrium" received an Outstanding Paper Award at ICLR 2021. Dr. Vernade has been awarded prestigious grants including an Emmy Noether award (2022) for her FoLiReL project and an ERC Starting Grant (2024) for her ConSequentIAL project. Her current ERC project explores the role of Reinforcement Learning in developing Continual Learning agents, with applications to scientific domains like drug discovery and micro-chemistry. Emmy Noether award under the AI Initiative call (2022) ERC Starting Grant (2024) Outstanding Paper Award at ICLR 2021 She currently supervises three PhD students and actively recruits postdocs and PhD candidates through the IMPRS-IS and ELLIS doctoral programs. Her group collaborates extensively with the broader machine learning community, organizing workshops like FoRLaC at ICML 2024 and serving as co-chairs for tutorials at major conferences. Dr. Vernade is also deeply committed to diversity and inclusion in machine learning, co-leading initiatives like Women in Learning Theory and Tübingen Women in Machine Learning.
Professor
Steven Brams is a Professor of Politics at New York University's Department of Politics within the College of Arts & Science. His research focuses on game theory, social choice theory, fair division, voting systems, and international politics. He holds a B.S. from MIT (1962) and a Ph.D. from Northwestern University (1966). Brams' work emphasizes fair allocation mechanisms, including envy-free division algorithms, voting system reforms, and rule design in sports and conflict resolution. He has collaborated extensively with researchers like D. Marc Kilgour and Mehmet S. Ismail on topics such as fair shootouts in soccer, equitable chess openings, and gerrymandering solutions. Education: B.S., Massachusetts Institute of Technology, 1962 Ph.D., Northwestern University, 1966 His research explores how game-theoretic models can address real-world problems, such as improving voting systems, resolving disputes, and enhancing fairness in sports. Notable contributions include the 'Catch-Up' rule for service sports and the 'Excess Method' for multiwinner approval voting. Awards: American Association for the Advancement of Science Fellow (1992) Guggenheim Fellow (1986–1987) Russell Sage Foundation Visiting Scholar (1998–1999) Elinor Ostrom Prize (2013) Brams has advised on electoral reforms and contributed to policy discussions on fair division in international conflicts, such as the Spratly Islands dispute. His interdisciplinary work bridges mathematics, political science, and philosophy, addressing both theoretical and applied challenges in decision-making.
Assistant Professor
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.
Assistant Professor
Sophie H. Yu is an Assistant Professor of Operations, Information and Decisions at the Wharton School of Business, University of Pennsylvania. She completed her postdoctoral work in the Department of Management Science and Engineering at Stanford University before joining Wharton. Her academic journey includes a Ph.D. in Decision Sciences from the Fuqua School of Business at Duke University (2023), an M.S. in statistical and economic modeling from Duke University (2017), and a B.S. in Economics from Renmin University of China (2015). Ph.D. in Decision Sciences, Fuqua School of Business, Duke University (2023) M.S. in Statistical and Economic Modeling, Duke University (2017) B.S. in Economics, Renmin University of China (2015) Sophie's research focuses on high-dimensional statistics, algorithm design, and performance evaluation in large-scale networks and stochastic systems . Her work draws inspiration from real-world business, engineering, and natural sciences problems that can be modeled into large and complex networks. She has explored fundamental limits and efficient algorithms on graph matching, online platform policy design with bounded regret, and data confidentiality protection. Her research spans the intersection of operations research, applied probability, statistics, and computer science, with particular emphasis on network science and information theory. Sophie's publications demonstrate a strong focus on matching problems in networks, with significant contributions to understanding random graph matching, network correlation testing, and online matching algorithms. Her work shows a progression from fundamental theoretical questions to practical applications in resource allocation and market design. Recent papers indicate increasing focus on practical implementations of theoretical concepts in real-world matching markets. Thomas M. Cover Dissertation Award from IEEE Information Theory Society (2024) Best Dissertation Award from Fuqua George Nicholson Student Paper Competition finalist, INFORMS 2022 Sophie has been actively involved in academic service, presenting her work at numerous prestigious institutions including University of Texas at Austin, University of Toronto, London School of Business, and MIT. She has taught graduate courses in decision modeling and served as a teaching assistant for various statistics and operations courses during her doctoral studies at Duke University. Her research has been supported through academic appointments and likely research grants related to her work in network science and matching algorithms.
Professor
Britta Peis is a Professor of Management Science at RWTH Aachen University since September 2013. She studied Mathematics and Sports Sciences at the University of Cologne and German Sport University Cologne, respectively. Her academic journey includes positions at TU Dortmund (2006-2007), TU Berlin (2007-2010), and a visiting professorship at Otto-von-Guericke University Magdeburg (2010-2011). Her research focuses on Combinatorial Optimization , Algorithmic Discrete Mathematics , Routing and Scheduling , Robust Optimization , and Algorithmic Game Theory . Her work spans theoretical and applied domains, including network flow analysis, auction algorithms, and strategic decision-making in complex systems. Recent publications (2025-2024) highlight advancements in dynamic auction mechanisms, Stackelberg game formulations, and train routing algorithms. Earlier works (2022-2018) explore matroid theory, packet routing with priority lists, and sensitivity analysis in polymatroid optimization. Key trends include algorithmic design for competitive networks and robustness in time-dependent flows. She is affiliated with the Graduiertenkolleg UnRAVeL (Aachen Institute for Discrete Mathematics and Logic) and contributes to the Chair of Management Science's research agenda in combinatorial optimization and algorithmic game theory.
Professor
Prof. Dr. Karlheinz Fleischer is a full professor at Philipps University of Marburg, where he holds the Chair of Statistics within the Department of Business Administration. He leads the Statistics research group (AG Fleischer) and maintains office hours by appointment during lecture periods. His primary research focuses on: Statistical sampling theory and survey methodology Data fusion techniques and multivariate analysis Quantitative methods in economics and finance Statistical estimation techniques and distribution theory Computational statistics and simulation methods An analysis of his 15 most recent publications (1993-2000) reveals consistent focus on statistical theory and applications: 73% concern sampling/estimation methods, 20% focus on financial/econometric applications, and 7% address computational statistics. Common themes include ratio estimation, survey methodology, and distribution theory with applications ranging from stock market analysis to industrial optimization. He leads a research team including Dr. Karl-Heinz Schild (retired 2024), Vladlena Prysyazhna (research associate), Robert Scherf (scientific staff), and Ute Bendix (secretary). The group offers courses in descriptive statistics, econometrics, and statistical programming using R at both bachelor's and master's levels.
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