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Marie BrandenburgView profile

Professor

• Combinatorics
• Discrete Geometry
• Tropical Geometry
• +6 more

Jun.-Prof. Dr. Marie Brandenburg is a Junior Professor for Combinatorics at Ruhr-Universität Bochum's Faculty of Mathematics, leading the Research Team Stump within the Algebra division. Her work focuses on polyhedral geometry and its applications in machine learning, optimization, economics, and game theory. She holds an office at IB 2/155 in the Department of Mathematics. Research Interests : Combinatorics & Discrete Geometry, Tropical/Algebraic Geometry, Polyhedral Geometry, Machine Learning applications, Optimization, Game Theory, and Economic Modeling. Her interdisciplinary approach bridges abstract geometry with practical fields like AI and market design. Teaching : Recently taught a seminar on Discrete Geometry (Summer 2025). Supervises graduate students in algebraic combinatorics and related areas. Labs/Teams : Member of the Research Team Stump, part of Ruhr University's Algebra group. Collaborates with the Floer Center of Geometry and the HDM@RUB student laboratory.

Anupam GuptaView profile

Adjunct Professor

• Algorithms
• Complexity
• Artificial Intelligence
• +4 more

Anupam Gupta is an Adjunct Professor in the Computer Science Department at New York University's Courant Institute of Mathematical Sciences. Previously, he held a faculty position at Carnegie Mellon University. His research focuses on algorithms, complexity, and theoretical computer science, with an emphasis on network design, metric embeddings, and approximation algorithms. He has received notable awards, including the ACM Fellowship (2021) and the Herbert A. Simon Award for Teaching Excellence (2019). Education: Ph.D., University of California, Berkeley (2000); B.Tech., Indian Institute of Technology Kanpur (1996) Teaching: Taught courses on algorithms, theoretical computer science, and real-world applications at both undergraduate and graduate levels. Research: Explores algorithms for uncertain environments, stochastic optimization, and online algorithms. His work bridges theoretical foundations with practical applications in networking and combinatorial optimization. Awards: ACM Fellow, Sloan Fellowship, and multiple teaching awards. Students: Advised over 15 Ph.D. students, including notable researchers in algorithms and theoretical computer science. Grants: Supported by NSF grants, including a CAREER Award, and collaborates on projects like the Indo-US Algorithms under Uncertainty initiative. His work on metric embeddings and graph algorithms has had significant impacts, with contributions to approximation algorithms and competitive analysis. He actively participates in academic conferences and serves as an organizer for programs like the Simons Institute's Algorithms and Uncertainty initiative.

Philip BrownView profile

Associate Professor

• Game Theory
• Multi-Agent Systems
• Cyber-Physical Systems
• +9 more

Philip Brown is an Associate Professor in the Department of Computer Science at the University of Colorado at Colorado Springs. He received his PhD in Electrical and Computer Engineering from the University of California, Santa Barbara in 2018 under the supervision of Jason R. Marden. His research focuses on the intersection of technology and society, utilizing game theory, optimization, and multiagent systems to study incentive mechanisms, cybersecurity, and smart infrastructure. Current Projects: CAREER: Endogenous Information Design for smart transportation networks AFOSR YIP: Robust multiagent coordination with randomized algorithms Deriving satellite maneuver intent via game theory Past Projects: Socially Networked Autonomy: Autonomous vehicle routing COVID-19 Policy Optimization: Localized decision frameworks Value-Based Access Control: Path security mechanisms Dynamic Aviation Routing: Weather-aware path selection Research Areas: Dr. Brown's work spans game theory applications in cyber-social systems, robust network games, and strategic security modeling. He explores how financial/informational incentives shape crowd behavior in smart cities, cybersecurity vulnerabilities from mis-modeled attackers, and the paradoxes of altruism in transportation networks. Publication Trends: His recent articles focus on endogenous Bayesian games for information design, altruism dynamics in congestion games, security modeling for autonomous systems, and pandemic policy analysis. Key subfields include network robustness, incentive mechanisms, and human-machine interaction in infrastructure systems. Scientific Awards: NSF CAREER award (2025) UCCS Outstanding Teacher Award (2024) AFOSR Young Investigator Award (2022) CCDC Best PhD Thesis Award (2018) Best Paper awards at EAI GameNets, IEEE conferences Advising: Dr. Brown has advised 2 PhD graduates (Joshua Seaton & Brandon Collins) and multiple Master's/undergraduate researchers. His lab (DeSCon) investigates decision-making mathematics in infrastructure, epidemiology, and multiagent systems.

Samim GhamamiView profile

Adjunct Professor

• Finance
• Macroeconomics
• Financial Regulation
• +7 more

Samim Ghamami is a prominent economist and academic researcher working at the intersection of finance and macroeconomics. Currently serving as an economist at the U.S. Securities and Exchange Commission (SEC) and an Adjunct Professor at New York University's Courant Institute of Mathematical Sciences, he has held academic roles at Columbia University, UC Berkeley, and the University of Southern California. His work focuses on financial stability, quantitative finance, risk management, digital asset markets, and macroeconomic policy . Ph.D. in Finance and Operations Research from University of Southern California (2009) M.Sc. in Operations Research from University of Tehran (2003) B.Sc. in Operations Research and Industrial Engineering from Iran University of Science and Technology (2000) His research spans advanced financial technologies, systemic risk, and regulatory frameworks, with a particular emphasis on central counterparty (CCP) risk, collateral mechanisms, and market structure reforms. His recent publications analyze extreme value theory applications to financial institutions and systemic risk allocation in complex networks. Ghamami’s academic output reveals trends in financial engineering, risk modeling, and macro-financial linkages . He has developed innovative approaches to counterparty risk quantification and network reliability estimation. Recipient of SEC Chair’s Award for Excellence (2020) for Treasury market reforms INFORMS 2013 Best Presentation Award As an advisor to the Bank for International Settlements and expert for the Financial Stability Board, he has shaped post-crisis capital market reforms. His teaching portfolio includes graduate-level courses in asset pricing, risk management, and financial economics at NYU, Columbia, and UC Berkeley.

Niv BuchbinderView profile

Professor

• Combinatorial Optimization
• Online Algorithms
• Algorithmic Game Theory
• +2 more

Prof. Niv Buchbinder is a faculty member in the Department of Statistics and Operations Research at the School of Mathematical Sciences, Tel Aviv University. His research centers on algorithmic solutions for combinatorial optimization in offline and online contexts, with significant contributions to primal-dual methodologies and algorithmic game theory. His academic background includes a Ph.D. in Computer Science from the Technion (2008) under Prof. Seffi Naor and an M.Sc. in Computer Science from the Technion (2003) under Prof. Erez Petrank. Key research areas encompass Combinatorial Optimization, Online Algorithms, Algorithmic Game Theory, Primal-Dual Methods, and Submodular Optimization, focusing on competitive analysis for problems like set cover, ad-auctions, and caching. Recent publications (2012-2015) reveal a concentrated effort in submodular optimization and online decision-making, with applications in advertising, resource allocation, and machine learning. These works consistently employ primal-dual frameworks to achieve strong competitive ratios in adversarial settings. Scientific recognition includes: Best Paper Award at ESA 2007 for “Online Primal-Dual Algorithms for Maximizing Ad-Auctions Revenue” Best Paper Award at FOCS 2011 for “A Polylogarithmic Competitive Algorithm for the k-Server Problem” No information is available regarding student advising or research grants. Similarly, details about laboratory facilities, research teams, or future projects are not provided in the source materials.

András FrankView profile

Professor

• Combinatorial Optimization
• Matroid Theory
• Submodular Functions
• +3 more

András Frank is a Professor at the Department of Operations Research, Eötvös Loránd University, Budapest. As founder and leader of the MTA-ELTE Egerváry Research Group on Combinatorial Optimization (EGRES), he focuses on combinatorial optimization, matroid theory, submodular functions, matchings, graph theory, and polynomial-time algorithms. University: Eötvös Loránd University Department: Operations Research Research Group: EGRES (MTA-ELTE Egerváry Research Group on Combinatorial Optimization) His research addresses both theoretical and applied aspects of combinatorial optimization, including network flows, graph connectivity, and algorithmic applications. He authored the book Connections in Combinatorial Optimization (Oxford University Press, 2011), which explores polynomial-time algorithms and their role in graph theory and matroid theory. Key trends in his recent work include fair integral flows, discrete convex optimization, and algorithmic approaches to supermodular functions. While no specific scientific awards are listed in the provided texts, his contributions have been recognized through publications in top-tier venues and leadership in the EGRES group.

Kristóf BércziView profile

Researcher

• Approximation Algorithms
• Combinatorial Optimization
• Matroid Theory
• +3 more

Kristóf Bérczi is a researcher at the Department of Operations Research, Eötvös Loránd University, Hungary. His work intersects computer science, mathematics, and operations research, focusing on theoretical and applied optimization problems. MSc and PhD in Operations Research, ELTE Postdoc at Hungarian Academy of Sciences Research Interests: Approximation algorithms Combinatorial optimization Matroid theory Submodular functions Algorithmic game theory Graph theory Recent Publication Trends: His 2024-2025 work explores matroid-constrained partitioning, inverse optimization algorithms, rainbow subgraph problems, and envy-free pricing models, often combining matroid theory with practical algorithm design. Scientific Contributions: Active in program committees (APPROX 2019, IPCO 2024, WALCOM 2025) Organized workshops on matroid optimization and matching theory Editorial board member of SIAM Journal on Discrete Mathematics Mentorship: Supervises PhD students Áron Jánosik, András Imolay, and others, with graduates now in postdoc/lecturer positions at institutions like CMM Chile and LSE UK.

Jeffrey A BilmesView profile

Professor

• Machine Learning
• Discrete Optimization
• Submodularity in Machine Learning and Data Analysis
• +2 more

Jeffrey A Bilmes serves as a Professor in the Department of Electrical and Computer Engineering at the University of Washington, actively contributing to academic and research initiatives within the institution. His research program centers on foundational and applied aspects of computational theory, with emphasis on: Machine Learning: Algorithmic development and theoretical analysis for data-driven systems Discrete Optimization: Solving combinatorial problems with discrete variable constraints Submodularity: Properties and applications of submodular functions in data analysis Time Series Analysis: Modeling sequential and temporal data structures Applications: Implementation of theoretical frameworks across real-world domains No scientific awards were documented in the source material. Details regarding student mentorship and research grant funding were not provided in the available text.