جست‌وجوی استاد و پژوهشگر

Niv Buchbinderمشاهده پروفایل

استاد

• Combinatorial Optimization
• Online Algorithms
• Algorithmic Game Theory
• +۲ مورد دیگر

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.

Julien Combeمشاهده پروفایل

استاد

• Market Design
• Theoretical Economics
• Matching Theory
• +۴ مورد دیگر

Julien Combe is a tenured Professor at the Department of Economics of École Polytechnique, where he holds a permanent membership at CREST (Center for Research in Economics and Statistics), a joint research center between ENSAE, ENSAI and École Polytechnique. He is also affiliated with the Institut des Politiques Publiques and the Center for Economic Policy Research (CEPR). His academic journey includes: PhD in Economics from Paris School of Economics (2017), supervised by Professor Olivier Tercieux Habilitation à Diriger les Recherches (HDR) completed June 2023 Visiting Scholar at Columbia University (2015-2016) Research Associate at University College London (2017-2019) Combe specializes in theoretical and empirical market design with applications spanning education systems (kindergarten assignment, school choice, teacher allocation) and healthcare (kidney exchange programs). His research uniquely bridges rigorous economic theory with practical policy implementation, frequently collaborating with French government agencies to design efficient allocation mechanisms. As an organizer of CREST's Microeconomic Seminar, he fosters academic exchange in his field. His publication pattern reveals consistent contributions to solving real-world matching problems through sophisticated economic models. Recent work focuses on dynamic assignment mechanisms, kidney exchange optimization, and equitable teacher distribution systems, demonstrating both theoretical innovation and practical relevance. His research methodology combines formal mathematical modeling with empirical validation, making significant contributions to economic science while directly informing public policy decisions. Combe actively engages with policymakers through: Multiple policy briefs for the Institut des Politiques Publiques Technical contributions to France's kidney exchange program Algorithm design for teacher assignment systems Public commentary on school admission platforms like Parcoursup His work has received substantial media attention in France, appearing in Le Monde, Libération, France Info, and other major outlets, demonstrating the societal impact of his research on allocation mechanisms affecting millions of citizens.

András Frankمشاهده پروفایل

استاد

• Combinatorial Optimization
• Matroid Theory
• Submodular Functions
• +۳ مورد دیگر

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 Bércziمشاهده پروفایل

پژوهشگر

• Approximation Algorithms
• Combinatorial Optimization
• Matroid Theory
• +۳ مورد دیگر

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.

Yusuke Kobayashiمشاهده پروفایل

دانشیار

• Graph Algorithms
• Discrete Structures
• Combinatorial Optimization
• +۴ مورد دیگر

Yusuke Kobayashi is an Associate Professor at the Research Institute for Mathematical Sciences (RIMS), Kyoto University. His research focuses on graph algorithms, combinatorial optimization, and discrete structures such as matroids, jump systems, and discrete convex functions. He has contributed to problems involving network design, disjoint paths, and fair allocation of indivisible goods. Education : Bachelor, Master, and Ph.D. in Information Science and Technology from the University of Tokyo (2005, 2007, 2010). Research Interests : Graph Algorithms: Network design, disjoint paths, and reconfiguration problems. Discrete Structures: Matroid theory, jump systems, and discrete convexity. Combinatorial Optimization: Applications to fair division, social welfare, and graph connectivity. Article Trends : Kobayashi’s recent work spans reconfiguration problems (e.g., colorings, matchings), fair allocation (EFX criteria), and quantum routing. His research often bridges graph theory with algorithmic applications, emphasizing structural properties like planarity and 4-edge-connectedness. Scientific Awards : Japan Operations Research Society Student Paper Award (2005). University of Tokyo Dean’s Award (2010). STOC Best Paper Award (2017). MEXT Young Scientists' Award (2020). Operations Research Society of Japan Research Award (2023). Grants and Collaborations : His work has been supported by the Japan Society for the Promotion of Science (JSPS) and involves collaborations with researchers in Japan and Hungary (e.g., Kristóf Bérczi, Naonori Kakimura).

Joseph S.B. Mitchellمشاهده پروفایل

استاد

• Computational Geometry
• Algorithms and Data Structures
• Optimization
• +۳ مورد دیگر

Joseph S.B. Mitchell is a SUNY Distinguished Professor at the Department of Applied Mathematics and Statistics, State University of New York at Stony Brook. His office is located in Math Tower, Room P-138A. His research spans computational geometry, algorithms, optimization, and related fields. Research Interests: Prof. Mitchell's work focuses on fundamental and applied problems in geometric computing, including: Algorithm design for spatial optimization (e.g., art gallery problems, dispersion) Robotics applications (multi-agent path planning, sensor deployment) Efficient solutions for geometric data structures and visibility constraints Recent Publications: His 15 most recent articles (2024-2025) demonstrate a consistent focus on geometric optimization, visibility problems, and algorithm design for polygons and networks. Common themes include approximation algorithms, multi-robot coordination, and combinatorial solutions for spatial challenges. Awards: SUNY Distinguished Professor (recognizing exceptional academic contributions)

Frits C.R. Spieksmaمشاهده پروفایل

استاد

• Combinatorial Optimization
• Operations Research
• Scheduling
• +۷ مورد دیگر

Prof. Frits C.R. Spieksma is a full professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e) , where he leads research within the Combinatorial Optimization Group. He has held academic positions at Maastricht University, KU Leuven, and the University of British Columbia, and has been at TU/e since 2018. Education: M.Sc. in Econometrics, University of Groningen (1987) Ph.D. in Operations Research, Maastricht University (1992) Research Focus: His work lies at the intersection of combinatorial optimization and real-world applications . Key themes include: Scheduling and clustering problems, especially in sports tournaments Organ allocation optimization for Eurotransplant Assignment and transportation problems Approximation algorithms and graph-theoretic optimization Scientific Service & Leadership: Founder and ex-Chair, EURO Working Group OR in Sports Member, Steering Committees of MAPSP and MathSports International President, EURO (Association of European Operational Research Societies) Former Vice-President, IFORS Former President, Belgian Society of Operations Research (ORBEL) Editorial Boards: Associate Editor, 4OR (2015–present) Associate Editor, Journal of Quantitative Analysis in Sports (2014–present) Associate Editor, Operations Research Letters (2008–2024) Former Associate Editor, INFORMS Transactions on Education , OMEGA , Computers & Operations Research , IIE Transactions , Naval Research Logistics PhD Supervision & Mentoring: He has supervised more than 25 PhD theses at KU Leuven and TU/e, many of whom now hold academic positions worldwide. Conference & Workshop Organisation: Recent leadership roles include General Chair of IPCO 2022 (Eindhoven), organiser of Benders Day 2024 , and co-organiser of the Dagstuhl Seminar on Fairness in Scheduling and Resource Allocation (March 2025).

• Theoretical Computer Science
• Algorithm Design and Analysis
• Approximation Algorithms
• +۳ مورد دیگر

Michael Lampis is a Maître de conférences HDR (Assistant Professor) at LAMSADE , Universite Paris Dauphine. His research focuses on theoretical computer science , particularly in approximation algorithms , parameterized complexity , and graph algorithm design . He has held post-doctoral positions at Kyoto University and KTH, Stockholm, and earned his PhD from the Graduate Center of CUNY under Amotz Bar-Noy. Research Interests include: Structural Graph Parameters (treewidth, pathwidth, clique-width) Algorithmic Meta-Theorems Approximation Schemes Combinatorial Optimization Computational Complexity Recent Research Trends highlight his work on parameterized approximation algorithms for graph problems (e.g., feedback vertex set, matching) and complexity analysis of games/puzzles. His projects S-EX-AP-PE-AL (ANR JCJC), COAL-GAS (CNRS-PSL), and collaborations with Japanese institutions (PARAGA, GRAPA) emphasize cross-border innovation. Scientific Awards include: Best Student Paper Award at WG 2025 Best Paper Award at SOFSEM 2024 Advising involves PhD students Ioannis Katsikarelis, Louis Dublois, and Manolis Vasilakis, alongside Master's advisees like Edouard Nemery and Alban Guerbois. He actively participates in peer review for conferences (ICALP, ESA, STACS) and journals (Algorithmica, JCSS, DAM).

Ahmed Dohaمشاهده پروفایل

دانشیار

• Business Model Innovation
• Artificial Intelligence
• Generative AI
• +۵ مورد دیگر

Dr. Ahmed Doha is an Associate Professor at the Sprott School of Business, Carleton University, specializing in Supply Chain Management. He holds a PhD in Operations Management and Information Systems from York University, Canada, and MSc/BSc degrees in Electrical and Computer Engineering from Queen’s University and Mansoura University, respectively. Education: PhD (York), MSc (Queen’s), BSc (Mansoura) Current sabbatical status Contact: 5036 Nicol Hall, Carleton University, Ottawa, ON K1S 5B6 His research focuses on applying emerging technologies like artificial intelligence (AI) and the Internet of Things (IoT) for business model innovation and value creation. Key areas include: Generative AI and Large Language Models (LLMs) in organizational and personal AI assistants E-commerce, recommendation systems, and crowdsourcing applications Semantic ontologies and knowledge bases integration Combating corruption through AI-enabled business models Research methodology follows full-cycle design science principles, from problem identification to field experimentation. Funded by SSHRC, NSERC, CANARIE, and industry partners. Scientific awards: NSERC Alexander Graham Bell Canada Graduate Scholarship Teaching: PhD-level AI research methods, applied AI, technology management, and supply chain fundamentals

Alain Hertzمشاهده پروفایل

استاد

• Operations Research
• Management Science
• Combinatorial Optimization
• +۶ مورد دیگر

Alain Hertz is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He is affiliated with two major research centers: Institut de valorisation des données (IVADO) and Groupe d'études et de recherche en analyse des décisions (GERAD). His expertise spans operations research, management science, and algorithm development with applications across multiple industries. Professor Hertz specializes in combinatorial optimization, graph theory, algorithms, heuristics and metaheuristics. His research bridges theoretical mathematics with practical applications in scheduling, vehicle routing, and decision support systems. Recent work explores graph-based approaches for recommender systems and chemical graph theory, demonstrating the versatility of his mathematical frameworks. His publication record shows consistent output across top journals in operations research and graph theory. Recent articles (2021-2025) focus on extremal graph theory, chemical graphs, graph coloring variants, and applications of graph theory to recommender systems. This demonstrates both theoretical depth and practical relevance in his research trajectory. Prix de la ministre de l'Éducation, du Loisir et du Sport - Gouvernement du Québec (2011) Professor Hertz has supervised 13 doctoral students and 18 master's students, with thesis topics ranging from optimization methods for semi-supervised clustering to wind farm network design. His supervision approach emphasizes both theoretical foundations and practical applications, preparing students for careers in both academia and industry. His research has been supported by various grants enabling collaborative work with international partners. Through his affiliations with IVADO and GERAD, Professor Hertz contributes to interdisciplinary research initiatives that connect mathematical theory with real-world problems in data science, artificial intelligence, and industrial engineering.

Weina Wangمشاهده پروفایل

استادیار

• Applied Probability
• Stochastic Systems
• Reinforcement Learning
• +۷ مورد دیگر

Weina Wang is an Assistant Professor in the Computer Science Department at Carnegie Mellon University, joining in Fall 2018. Her research lies at the intersection of applied probability , stochastic systems , and reinforcement learning , focusing on decision-making in large-scale systems with applications to computing resource orchestration, data privacy, and graph statistics. She has received prestigious awards including the NSF CAREER Award (2022) , ACM MobiHoc Best Paper (2022) , and ACM SIGMETRICS Rising Star Research Award (2023) . PhD in Electrical Engineering, Arizona State University (2016) Bachelor’s in Electronic Engineering, Tsinghua University (2009) Her recent publications span restless bandits , queueing theory , and attributed graph alignment , reflecting her dual focus on fundamental limits and algorithmic solutions. Notable collaborations include work on privacy-preserving data routing , phase-aware scheduling , and erasure-coded servers for heterogeneous traffic. She has advised PhD students Jalani Williams , Tuhinangshu Choudhury , and Yige Hong . Her research has been recognized with best paper awards and grants like the NSF CAREER . She also contributes to professional societies, recently joining the INFORMS Applied Probability Society council . Her teaching includes courses like Probability and Computing and Fundamentals of MDPs and Reinforcement Learning .

• real-time decision-making
• dynamic optimization under uncertainty
• online markets
• +۴ مورد دیگر

Vahideh Hosseinikhah Manshadi serves as the Michael H. Jordan Professor of Operations at Yale University's School of Management, with a secondary appointment as Assistant Professor in the Department of Statistics. Her academic profile reflects interdisciplinary expertise spanning operations research, statistics, and public health modeling. Professor Manshadi's research focuses on real-time decision-making and dynamic optimization under uncertainty , with significant applications across multiple domains. Her work addresses critical challenges in online markets , health-care operations , maritime port logistics , and matching markets . She has developed sophisticated optimization frameworks that account for network structures, user behavior in crowdsourcing platforms, and fair resource allocation in constrained environments. Analysis of her recent publications reveals a strong trend toward addressing complex matching problems in dynamic environments. Her research bridges theoretical optimization with practical applications in humanitarian contexts (refugee resettlement), platform economics, and social services. The interdisciplinary nature of her work is evident in publications spanning top journals in operations research, management science, and computer science. Professor Manshadi maintains an active research agenda as evidenced by consistent high-impact publications in premier journals including Management Science , Operations Research , and Manufacturing & Service Operations Management . Her work demonstrates significant methodological innovation while addressing real-world operational challenges across multiple sectors. Her academic leadership extends to the Public Health Modeling initiative at Yale, where her optimization expertise contributes to addressing complex public health challenges through quantitative approaches. While specific information about research grants and advising is not detailed in the provided text, her publication record indicates active collaboration with numerous researchers across different institutions.

• Game Theory
• Fair Allocation
• Matching Theory
• +۴ مورد دیگر

Jens Gudmundsson is an Assistant Professor at the Department of Food and Resource Economics, University of Copenhagen, Denmark. He is affiliated with the Center for Blockchains and Electronic Markets (BCM), which is funded by the Carlsberg Foundation. His work bridges theoretical economics with practical applications in blockchain technology and environmental systems. His primary research interests include: Game Theory Fair Allocation Matching Theory Mechanism Design Theory Social Choice Theory Blockchain Applications Environmental Economics Dr. Gudmundsson earned his PhD in Economics on matching theory from Lund University in 2015. His research portfolio demonstrates sophisticated applications of economic theory to real-world problems, particularly in decentralized systems. His publications span top journals including Management Science, Games and Economic Behavior, and Journal of Environmental Economics and Management, reflecting interdisciplinary impact across economics, computer science, and environmental studies. His scientific work shows consistent development across three main streams: blockchain economics (focusing on incentive structures and smart contracts), environmental resource allocation, and fundamental game theory. Recent publications through 2025 indicate ongoing productivity and relevance in addressing contemporary challenges in decentralized systems design and fair resource distribution.

Przemysław Falkowski-Gilskiمشاهده پروفایل

استادیار

• Geoinformatics
• Smart Cities
• IoT (Internet of Things)
• +۵ مورد دیگر

Dr. Przemysław Falkowski-Gilski serves as an Assistant Professor at the Department of Geoinformation Systems within the Faculty of Electronics, Telecommunications and Informatics at Gdańsk University of Technology. His research bridges geospatial engineering with computational intelligence, focusing on real-world applications in urban environments and sensor networks. His primary research domains include: Geoinformatics and GNSS positioning systems IoT infrastructure for smart cities Swarm intelligence optimization algorithms Edge computing resource allocation AI-driven data analysis for urban mobility Recent publications (2024-2025) reveal a cohesive research trajectory addressing critical challenges in smart city resource management through UAV-assisted edge computing, Android-based urban navigation, and sensor ontology integration. His work consistently applies advanced optimization techniques—such as Golden Jackal and Beetle Swarm algorithms—to solve practical problems in geospatial data processing and IoT systems, demonstrating strong interdisciplinary methodology.

• Behavioral Economics
• Experimental Economics
• Matching Markets
• +۱ مورد دیگر

Rustamdjan Hakimov serves as an Associate Professor in the Department of Economics at the University of Lausanne since August 2022. His research bridges theoretical market design with empirical validation through behavioral and experimental economics, focusing on matching mechanisms in education and labor markets. His academic credentials include: Doctor of Economic Science, Technical University Berlin (2016; Dissertation: Three essays on behavioral market design; Summa cum laude) M.Sc. in Economics and Management Science, Humboldt University Berlin (2010) Diploma in Mathematical Methods and Operations Research, Kyrgyz-Russian Slavic University (2007) Professor Hakimov's work centers on understanding strategic behavior in matching systems through controlled experiments. He examines how market participants respond to institutional rules in school choice, college admissions, and appointment systems. His research demonstrates how seemingly minor design features—such as transparency mechanisms or information structures—significantly impact market outcomes, fairness, and efficiency. By combining game-theoretic modeling with experimental validation, he develops practical solutions for real-world allocation problems. His publication portfolio reveals consistent output in top-tier journals since 2015, with accelerating productivity after 2018. The research spans theoretical market design (e.g., iterative deferred acceptance mechanisms), behavioral investigations (e.g., self-confidence effects in matching), and field applications (e.g., charitable giving experiments in Kyrgyzstan). Recent work increasingly addresses contemporary challenges like algorithmic hiring systems and privacy concerns in personalized pricing.