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Standa ŽivnýView profile

Professor

• Theoretical Computer Science
• Discrete Mathematics
• Algorithms
• +10 more

Standa Živný is a Professor of Computer Science at the University of Oxford and a Fellow and Tutor at Merton College. He has been a faculty member at Oxford since 2013 and was promoted to full professor in 2021. His research spans theoretical computer science and discrete mathematics, with a focus on algorithms, computational complexity, and constraint satisfaction problems (CSPs) in various forms, including optimisation, counting, and approximation. His research interests include the power and limitations of convex relaxations, sparsification, submodularity, and the algebraic and logical foundations of tractability in combinatorial problems. He has made significant contributions to understanding when and why certain problems can or cannot be efficiently solved using linear programming and other algorithmic paradigms. The recent trends in his publications show a deep engagement with approximation algorithms, hardness results, sparsification techniques, and the complexity of counting and promise problems. His work often lies at the intersection of algebra, logic, and optimisation, demonstrating the power of interdisciplinary approaches in theoretical computer science. ERC Consolidator Grant (NAASP, 2022–2027) ERC Starting Grant (PowAlgDO, 2017–2022) Royal Society University Research Fellowship (2013–2021) He actively supervises a large cohort of postdoctoral researchers and students, including PhD candidates, master’s, and undergraduate students. His leadership extends to academic service, where he serves as Editor-in-Chief of the SIAM Journal on Discrete Mathematics and holds editorial and committee positions in major journals and funding bodies. He has organised numerous workshops and research programmes at institutions such as Dagstuhl, the Isaac Newton Institute, and AIM. He is involved in major research initiatives, including a Simons Programme on symmetry in computation and an American Institute of Mathematics SQuARE on relaxations for promise CSPs.

James B. OrlinView profile

Professor

• Network Optimization
• Combinatorial Optimization
• Transportation
• +7 more

James B. Orlin is the E. Pennell Brooks (1917) Professor in Management and a Professor of Operations Research at the MIT Sloan School of Management. He specializes in network and combinatorial optimization with applications spanning transportation, computer science, operations, and marketing. BA in Mathematics, University of Pennsylvania MA in Mathematics, California Institute of Technology MMath, University of Waterloo PhD in Operations Research, Stanford University His research focuses on designing efficient algorithms for network optimization problems, including shortest path, max flow, and min cost flow. He has contributed to algorithmic theory in logistics, telecommunications, and inventory management, with work on stochastic demand models and data-driven inventory policies. Recent publications include advancements in directed shortest path algorithms, robust submodular function maximization, and energy storage problem complexity. His seminal textbook Network Flows: Theory, Algorithms, and Applications (1993) remains a foundational reference. Leonard G. Abraham Prize Khachiyan Prize Test of Time Award As a mentor, he has advised numerous researchers through collaborative publications and teaching. His work addresses both theoretical algorithm development and practical implementation across diverse domains including airline scheduling, logistics, and network design.

Sonia VanierView profile

Professor

• Artificial Intelligence
• Operations Research
• Network Optimization
• +4 more

Sonia Vanier is a Professor in the Department of Computer Science at École Polytechnique, where she holds multiple leadership positions: Head of the 'Trusted and Responsible AI' Chair (X/Crédit Agricole), Head of the 'Optimization and AI for Mobility' Chair (X/SNCF), Head of 3A, and Scientific Manager of Industrial Relations for both the Department and the Computer Science Laboratory (LIX). She coordinates the GdT OR (Network Optimization) working group and leads the REST (Energy, Services and Transport Networks) research axis of the CNRS GDROD, while serving on its scientific council. Her research develops decision support tools for complex industrial problems through hybrid approaches combining Artificial Intelligence and Operations Research , with focus areas including Network Optimization, ethical AI systems, sustainable computing, and trustworthy AI frameworks. Her work bridges theoretical foundations with applications in telecommunications, transportation, and cybersecurity. Publications demonstrate strong emphasis on optimization techniques (branch-and-price, cutting planes) applied to wireless networks, AI safety, and security challenges. Recent works explore LLM memorization, signomial programming, and multi-commodity flow problems, showing consistent integration of OR with machine learning for industrial-scale problems. Awards: Research Award and Innovation Award, Telecom Valley Association ALOES Orange Innovation Project She leads major industrial-academic partnerships through the Crédit Agricole and SNCF chairs, managing research grants focused on responsible AI deployment and mobility optimization. As Scientific Manager of Industrial Relations, she oversees industry collaborations for LIX laboratory. Affiliated with the Computer Science Laboratory (LIX), she directs the 3A research group and contributes to national initiatives through CNRS GDROD, coordinating research in network optimization and sustainable systems.

Yann DisserView profile

Professor

• Combinatorial Optimization
• Online Algorithms
• Graph Theory
• +6 more

Professor Yann Disser is a faculty member in the Department of Mathematics at TU Darmstadt since 2021. He previously held an Assistant Professor (tenure-track) position at TU Darmstadt (2016-2021), a PostDoc position at TU Berlin (2012-2016), and a Visiting Professor role at Augsburg University (2015). His research spans Combinatorial Optimization , Online Algorithms , Graph Exploration , Computational Complexity , and Robust Optimization . Current position: Professor (W2), TU Darmstadt Previous: Assistant Professor (2016-2021), TU Darmstadt PostDoc & Habilitation: TU Berlin (2012-2016) His research focuses on algorithmic approaches to optimization problems, including: Combinatorial Optimization for problems like Steiner trees and knapsack variants Online Algorithms with applications to scheduling and transportation Graph Exploration by mobile agents and complexity bounds Computational Complexity of linear programming pivot rules Network Flows and geometric reconstruction Recent publications analyze incremental maximization with greedy methods, lower bounds for active-set methods , and universal circuit designs . His work appears in top venues like IPCO , ESA , and SODA . He advises a team of researchers including David Weckbecker, Farehe Soheil, and Alexander Birx. Current and past advisees often focus on algorithmic theory, with placements at institutions like HPI Potsdam and Merck.

• Computer Science
• Economics
• Mathematics
• +3 more

Assoc Prof Xiaohui Bei is an Associate Professor at Nanyang Technological University (NTU), holding dual appointments in the School of Physical & Mathematical Sciences (Division of Mathematical Sciences) and the College of Computing & Data Science. His research focuses on algorithmic game theory, fair division, auction design, and computational economics. He earned his Ph.D. from Tsinghua University in 2012 under Prof. Andrew Yao and held postdoctoral positions at NTU and the Max Planck Institute for Informatics. His work bridges theoretical computer science and economics, addressing challenges in fair resource allocation, truthful mechanisms, and market design. Key interests include cake-cutting protocols, auction optimization, and voting systems with mixed goods. He has contributed to foundational studies on Nash welfare, submodular valuations, and connectivity constraints in fair division. Prof. Bei’s research is supported by grants exploring topics such as optimal auction mechanisms and dynamic resource allocation. His publications often emphasize practical algorithmic solutions to complex economic challenges, ensuring both efficiency and fairness.

• Combinatorial Optimization
• Algorithmic Discrete Mathematics
• Routing and Scheduling
• +2 more

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.

Federico Ardila-MantillaView profile

Professor

• Combinatorics
• Algebraic Combinatorics
• Geometric Combinatorics
• +5 more

Federico Ardila-Mantilla is a Professor of Mathematics at San Francisco State University and an Adjunct Professor at Universidad de los Andes, Colombia. His research focuses on combinatorics and its connections to geometry, algebra, and topology, with notable contributions to matroid theory, polytopes, and tropical geometry. He is also deeply involved in promoting equitable and inclusive mathematics education through initiatives like the SFSU-Colombia Combinatorics Initiative . His work bridges pure mathematics and applications, particularly in robotics and discrete geometry. He has held visiting positions including at the Institute for Advanced Study (Princeton, 2024-25). His research spans over 60 publications, emphasizing interdisciplinary approaches to combinatorial problems. Ardila advocates for accessibility in mathematics through axioms such as 'Mathematical potential is equally present in all groups' (cited widely in educational contexts). Key areas of research include algebraic structures on polytopes, Lagrangian geometry of matroids, and geometric enumeration. He collaborates internationally and mentors students across institutions. His outreach efforts aim to make mathematics accessible to underrepresented communities.

Alina EneView profile

Associate Professor

• Algorithms
• Combinatorial Optimization
• Submodularity
• +3 more

Alina Ene is an Associate Professor in the Department of Computer Science at Boston University, within the Faculty of Arts & Sciences. She holds a BSE in Computer Science (High Honors) from Princeton University (2008) and a PhD from the University of Illinois at Urbana-Champaign (2013), advised by Chandra Chekuri. Her research focuses on algorithms, combinatorial optimization (submodularity, graph theory), and their applications to machine learning. She previously served as Assistant Professor at the University of Warwick, Faculty Fellow at the Alan Turing Institute, and postdoctoral researcher at Princeton's Center for Computational Intractability. Key research themes include distributed submodular maximization, streaming algorithms, optimization theory, and machine learning. Her work bridges theoretical guarantees with practical applications in data science and distributed systems. Notable contributions include frameworks for submodular maximization under constraints, randomized coordinate descent methods, and routing algorithms with balance considerations. Publications emphasize algorithm design for submodular functions, stochastic optimization, and graph problems. Recent work explores high-probability convergence in stochastic gradient methods, clipped gradient techniques, and online ad allocation strategies. Her 2016 FOCS paper on distributed submodular maximization remains influential in large-scale optimization. Professional service includes editorial work for conferences like FOCS/STOC/SODA and grants from NSF's CAREER program. No explicit scientific awards are listed, though her fellowships and postdoctoral appointments reflect scholarly recognition.

Shi LiView profile

Professor

• Theoretical Computer Science
• Design and Analysis of Algorithms
• Combinatorial Optimization
• +11 more

Shi Li is a Professor in the Theory Group at the Department of Computer Science and Technology, School of Computer Science, Nanjing University. He previously held faculty positions at the University at Buffalo (2015–2023) as Assistant and Associate Professor, and was a Research Assistant Professor at Toyota Technological Institute at Chicago (2013–2015). He earned his Ph.D. from Princeton University in 2014 under Moses Charikar and completed his B.S. in Computer Science and Technology at Tsinghua University, where he was part of Andrew Chi-Chih Yao’s Special Pilot Class. His research lies at the intersection of theoretical computer science and combinatorial optimization, with a focus on the design and analysis of algorithms for problems in clustering, scheduling, network design, facility location, and online algorithms. He also explores learning-augmented algorithms and differential privacy in algorithmic contexts. His work combines deep theoretical insights with practical algorithmic frameworks, often leveraging linear programming relaxations, iterative rounding, and randomized techniques. The recent publications highlight a consistent trend in approximation algorithms, particularly in clustering (e.g., correlation clustering, fair k-set selection), scheduling (e.g., unrelated machine scheduling, load balancing), and robust optimization. His work frequently appears in top-tier theoretical venues such as STOC, FOCS, SODA, and ICALP, with increasing emphasis on fairness, privacy, and efficiency in algorithm design. Best Paper Award of Track A, ICALP 2011 Co-winner of Best Paper Award, FOCS 2012 Invited to Special Issue of SICOMP (FOCS 2017 paper) Best Paper Award, COCOON 2018 Invited to Special Issue of SICOMP (STOC 2019 paper) Best Paper Award of Track A, ICALP 2024 Outstanding Paper Award, SPAA 2024 Shi Li has advised several PhD and master’s students, including Yuda Feng, Han Dai, Zihao Liang, and Jia Ye, and has mentored postdoctoral researcher Ruilong Zhang. He has served on numerous program committees (e.g., STOC, SODA, ICALP) and is an Editorial Board Member of ACM Transactions on Algorithms . He teaches core algorithm courses such as Design and Analysis of Algorithms and Advanced Algorithms , and actively collaborates with researchers worldwide. His lab focuses on theoretical foundations of efficient and fair algorithm design, with applications in large-scale data analysis and distributed systems.

• Algorithms
• Theoretical Computer Science
• Networks
• +4 more

David Kempe is a Professor in the Thomas Lord Department of Computer Science at the University of Southern California (USC), part of the Viterbi School of Engineering. His research focuses on algorithms, theoretical computer science, and their applications to networks, auctions, mechanism design, and information flow. He has advised numerous Ph.D., M.S., and undergraduate students, many of whom now hold prominent roles in academia and industry. His research has been supported by grants including NSF CAREER, Sloan Fellowship, and ONR Young Investigator awards. Kempe organizes conferences such as STOC 2018 and co-founded the USC Theory Group, fostering collaboration through regular meetings and seminars. His work bridges algorithmic foundations with real-world applications, addressing challenges in fairness, network dynamics, and machine learning. Key achievements include developing voting rules with optimal metric distortion and algorithms for fair matching under uncertainty. His publications span influential topics in social networks, game theory, and data science. Collaborations with industry (e.g., Facebook, Snapchat) highlight applied contributions to recommendation systems and media matching.

Evimaria TerziView profile

Professor

• algorithmic data mining
• network analysis
• recommendation systems
• +3 more

Evimaria Terzi is a Professor and Department Vice Chair at Boston University (BU), affiliated with the Data Management Lab@BU. Her research focuses on algorithmic data mining with applications in network analysis, recommendation systems, ranking, and clustering. She holds a PhD from the University of Helsinki and has held prior roles at IBM Almaden Research Center (2007–2009) and the Helsinki Institute for Information Technology (HIIT) before 2007. Her work spans theoretical and applied domains, including team formation algorithms, fairness in AI, and large language model evaluation. Notable contributions include studies on LSM tree optimization, counterfactual explanations for auditing fairness, and the dynamics of memorization in LLMs. Her recent publications emphasize flexibility in database systems and ethical AI practices. Evimaria’s research has been recognized through her contributions to conferences like WSDM and KDD, where she has served in organizing roles. The themes of her work consistently bridge algorithmic innovation with real-world applications in social networks, healthcare, and collaborative systems.

Rakesh V. VohraView profile

Professor

• Economics
• Computer Science
• Game Theory
• +5 more

Rakesh V. Vohra is the George A. Weiss and Lydia Bravo Weiss University Professor at the University of Pennsylvania, holding primary appointments in the departments of Computer and Information Science, Economics, and Electrical and Systems Engineering. He co-directs the Warren Center for Network and Data Sciences. His research spans game theory, market design, auction theory, and computational economics, with a focus on mechanism design, spectrum markets, and systemic risk in financial networks. He explores interdisciplinary topics such as NFTs, blockchain applications, and algorithmic fairness in healthcare equity. His work integrates economic theory with computational methods, addressing challenges in resource allocation, strategic behavior, and network dynamics. Recent studies analyze optimal bailouts in financial systems, sequential information acquisition, and the implications of unlicensed spectrum policies on wireless competition. He also investigates behavioral aspects of decision-making under uncertainty and the design of robust economic systems. Notable contributions include foundational work on combinatorial auctions, stable matching mechanisms under constraints, and the role of network effects in market stability. Though no formal awards are listed, his prolific publication record reflects significant academic influence across multiple disciplines.

Yuri FaenzaView profile

Associate Professor

• Discrete Optimization
• Operations Research
• Mathematical Programming
• +4 more

Yuri Faenza is an Associate Professor in the Department of Industrial Engineering and Operations Research at Columbia Engineering, with affiliations to the Data Science Institute (DSI) and Foundations of Data Science Center. His research bridges Discrete Optimization, Operations Research, and Computer Science, focusing on algorithmic theory and applications to Market Design (particularly School Choice) and Machine Learning. Key research areas include Knapsack , Matching , and Extended Formulations , with methodological work on polytope structures, greedy algorithms, and stochastic or semi-random models. His recent publications emphasize stable matching, optimization under uncertainty, and the intersection of discrete mathematics with data science. Scientific awards include the NSF CAREER award Meta Research Award . He has served on program committees for major conferences like ALGA, EC, APPROX, and IPCO, and is an Associate Editor for journals including Mathematical Programming and Discrete Optimization . Prior to Columbia, he held postdoctoral positions at the University of Brussels, EPFL, and University of Padua.

Arash AsadpourView profile

Associate Professor

• Data Science/Analytics
• Operations Management
• Operations Research
• +7 more

Arash Asadpour is an Associate Professor at the Narendra Paul Loomba Department of Management, Zicklin School of Business, Baruch College, City University of New York (CUNY). His research focuses on operations research, optimization, and marketplace dynamics, particularly in gig economy platforms and online retailing. PhD in Management Science and Engineering, Stanford University 2010 B.Sc. in Computer Engineering, Sharif University of Technology, Tehran, Iran 2005 His work spans data science , dynamic pricing , stochastic optimization , and matching markets , with applications to ridesharing, online advertising, and resource allocation. Recent publications emphasize platform economics, algorithmic game theory, and submodular function optimization. Arash has received prestigious awards including the Best Paper Award at the ACM-SIAM Symposium on Discrete Algorithms (SODA) in 2010, top honors in Iran’s national graduate entrance exam (2004), and silver medals in national informatics olympiads (1999-2000).

Roie LevinView profile

Assistant Professor

• Approximation Algorithms
• Online Algorithms
• Submodular Functions
• +5 more

Roie Levin is an Assistant Professor at Rutgers University's Department of Computer Science. He received his PhD in Algorithms, Combinatorics and Optimization from Carnegie Mellon University in 2022, advised by Anupam Gupta. Prior to that, he worked at the Allen Institute for Artificial Intelligence (2015-2017) and earned dual BSc degrees in Computer Science/Applied Mathematics and Mathematics from Brown University (2015). Before joining Rutgers, he was a Fulbright Postdoctoral Fellow at Tel Aviv University under Niv Buchbinder. Current Role: Assistant Professor in Computer Science Academic Training: PhD (2022) CMU, BSc (2015) Brown University Postdoctoral: Fulbright Fellow at Tel Aviv University Levin's research focuses on approximation algorithms for uncertain environments (online/dynamic/streaming models) and submodular function optimization. His work spans theoretical foundations and practical implementations across distributed systems, geometric constraints, and reinforcement learning paradigms. Teaching includes graduate and undergraduate algorithms courses (CS 344, CS 513) with emphasis on problem-solving techniques, computational complexity, and modern algorithmic trends. His publications showcase expertise in online algorithms, submodular optimization, and approximation theory with applications in clustering, caching, and machine learning. The 2025 articles demonstrate continued exploration of online consistency and contention resolution, while 2023-2024 works focus on submodular optimization under uncertainty and dynamic environments. Earlier publications (2015-2017) cover semantic parsing, geometric approximation, and planar graph optimization. Fulbright Postdoctoral Fellow Levin's research connects theoretical guarantees with practical implementations, bridging classical algorithm design with modern machine learning applications. His recent work explores primal-dual methods in online settings and robust subspace approximation techniques for streaming data environments.