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Adam WiermanView profile

Professor

• Sustainable Computing
• Online Algorithms
• Optimization
• +4 more

Adam Wierman is the Carl F. Braun Professor of Computing and Mathematical Sciences at the California Institute of Technology (Caltech). He holds academic roles including Professor (2012–2024), Braun Professor (2024–present), and served as Executive Officer (2015–2020), Director of Information Science and Technology (2016–2025), and Associate Director (2015–2016). His research focuses on designing sustainable and resilient networked systems through advances in machine learning, optimization, control, and economics. Key applications include data centers, electricity grids, and transportation systems. Education: B.S., M.S., and Ph.D. in Computer Science from Carnegie Mellon University (2001–2007). His work bridges theoretical foundations and practical deployment, emphasizing provably efficient algorithms and market mechanisms. Recognitions include IEEE Fellow, ACM Distinguished Member, and the Northrop Grumman Prize for Excellence in Teaching. Research Interests: Sustainable Computing, Online Algorithms, Optimization, Control Systems, Network Economics, and Applied Probability. His lab develops tools for robust voltage control, carbon-aware scheduling, and scalable reinforcement learning for multi-agent systems. Recent projects include SustainGym, a reinforcement learning benchmark for sustainability tasks. Teaching: Courses include Networks: Structure & Economics, Projects in Networking, and Computer Science Education in K-14. He actively mentors graduate students and postdocs in areas like learning-augmented control and smart grid systems. Affiliations: Member of Caltech’s RSRG (Resilient Sociotechnical Systems Research Group), DOLCIT (Dynamical Learning for Optimization and Control), CSIS (Center for Social and Information Sciences), and CMI (Computation and Mathematical Sciences Institute).

Jiawei ZhangView profile

Professor

• Business Analytics
• Optimization
• Machine Learning
• +7 more

Jiawei Zhang is the Michael Armellino Professor in Business and Professor of Information, Operations and Management Sciences at the Leonard N. Stern School of Business, New York University. He chairs the Department of Technology, Operations, and Statistics and serves as Academic Director of the Master of Science in Data Analytics & Business Computing program. He joined NYU Stern in September 2004 and held a joint position at NYU Shanghai from 2014 to 2017. PhD in Management Science and Engineering, Stanford University, 2004 MS in Operations Research, Tsinghua University, 1999 BS in Applied Mathematics, Tsinghua University, 1996 Professor Zhang's research centers on business analytics, optimization, and operations management. His work integrates machine learning, robust optimization, and stochastic modeling to solve complex problems in supply chain management, healthcare operations, pricing, and revenue management. He investigates decision-making under uncertainty, online learning, and algorithmic approaches to resource allocation. His recent publications reveal a strong focus on theoretical and applied aspects of stochastic optimization, prophet inequalities, assortment optimization, and process flexibility. These works appear in top journals such as Operations Research , Management Science , and Mathematics of Operations Research , reflecting deep contributions to both methodological innovation and practical applications in operations. His editorial service highlights scholarly leadership: Associate Editor, Management Science (2014–present) Associate Editor, Manufacturing & Service Operations Management (2021–present) Associate Editor, Mathematics of Operations Research (2009–present) Associate Editor, Operations Research (2006–present) Professor Zhang teaches a range of courses from undergraduate to PhD levels, including Decision Models and Analytics, Convex Optimization, and Dynamic Programming. He advises PhD students and contributes to executive education programs, including in Risk and Decision Analytics and FinTech. His research is supported by theoretical rigor and real-world applicability, with implications for technology, finance, and healthcare operations.

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.

Kim Skak LarsenView profile

Professor

• Online Algorithms
• Algorithmics
• Data Structures
• +6 more

Kim Skak Larsen is a Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark, Faculty of Science. He serves as Head of Section and is affiliated with the Algorithms VIP group and the Digital Democracy Centre, reflecting his dual focus on theoretical algorithmics and societal applications of computing. His research lies in online algorithms, data structures, database systems, and algorithmics , with a strong emphasis on competitive analysis and performance measures. Key research themes include competitive ratio optimization, online matching, knapsack problems, and the integration of predictions into algorithmic frameworks. His recent work explores algorithmic trade-offs under uncertainty and misinformation on social media. His recent publications (2023–2024) reveal a consistent focus on online algorithms with predictions , spanning scheduling, matching, and knapsack problems. These works analyze competitive ratios and performance under prediction errors, contributing to the growing field of learning-augmented algorithms. His interdisciplinary engagement includes contributions to public discourse on digital trust and news authenticity. Project: Trade-Offs for Algorithms Facing Uncertainty (2025–2027) Project: Online Algorithms with Predictions - DIREC (2022–2025) Project: Algorithmic Challenges (2014–2017) Project: Trust and News Authenticity (ongoing) He has supervised PhD students and contributed to academic service as a peer reviewer, conference participant, and series editor. He teaches courses such as Formal Languages, Advanced Data Structures, and Geometric Algorithms, and has been active in public outreach through media contributions on combating scam articles online.

• Computer Science
• Algorithms
• Computational Geometry
• +4 more

Sujoy Bhore is affiliated with the Indian Institute of Technology Bombay (Department of Computer Science & Engineering), Université libre de Bruxelles, and Technische Universität Wien. His research focuses on algorithms , computational geometry , and graph theory , with a strong emphasis on geometric optimization , dynamic data structures , and parameterized algorithms . Recent publications highlight advancements in Euclidean spanners for sparse network design online algorithms for dynamic geometric problems Steiner trees and tree covers in planar domains k-median/means approximation using coresets His collaborative work spans institutions, with frequent joint research on geometric intersection graphs , map labeling , and planar graph embeddings . Co-authors include prominent researchers like Csaba D. Tóth, Martin Nöllenburg, and Timothy M. Chan. While no formal awards are documented here, his contributions to algorithmic complexity and geometric networks remain significant.

• Network Function Virtualization (NFV)
• Cloud Computing
• Resource Allocation
• +8 more

Danny Raz is a Professor at the Technion - Israel Institute of Technology, specializing in computer science and networking. His work focuses on cloud computing, network function virtualization (NFV), resource allocation, and online algorithms. Institution: Technion - Israel Institute of Technology, Haifa, Israel Research interests include: Network Function Virtualization (NFV) and service chaining Stochastic and dynamic resource allocation Online algorithms in random-order models 5G/edge computing infrastructure Blockchain network analytics Over the past decade, his publications in venues like IEEE/ACM Transactions on Networking and INFOCOM address: Optimal deployment of cloud services TCAM-based classification and flow measurement Game-theoretic approaches to load balancing Cost-aware live migration and fault recovery Wireless network optimization (4G/5G) Collaborations with researchers like Yuval Shavitt, Joseph Naor, and Haim Kaplan highlight his interdisciplinary work bridging theory and practice in networking and cloud systems.

Rajesh GanesanView profile

Associate Professor

• Cybersecurity
• Healthcare Operations
• Transportation Systems
• +6 more

Rajesh Ganesan is an Associate Professor in the Department of Systems Engineering and Operations Research at George Mason University's Volgenau School of Engineering. His research focuses on applying advanced mathematical and computational methods to solve complex decision-making problems under uncertainty across multiple critical domains including cybersecurity, healthcare, defense, and transportation systems. His research interests span cybersecurity operations, healthcare resource allocation, transportation systems optimization, stochastic optimization, and reinforcement learning. Ganesan's work addresses fundamental challenges in sequential decision-making under uncertainty, with applications ranging from cybersecurity analyst scheduling to liver transplant allocation and air traffic management. His research has significant implications for national security, public health, and transportation efficiency. His publication record demonstrates consistent contributions to high-impact journals and conferences, with recent work focusing on cybersecurity operations centers (CSOCs), adaptive resource allocation, deception technologies, and healthcare optimization. His research methodology combines stochastic optimization, approximate dynamic programming, and reinforcement learning to tackle complex real-world problems. Teacher of Distinction Award, Center for Teaching Excellence ISERC Best Track Paper Award, National Security Track Faculty Study Leave, GMU Provost Office Multiple U.S. Patents in process control and manufacturing Best Paper Awards in air transportation and security domains Ganesan has successfully mentored PhD and Master's students including Poornima Balakrishna (recipient of the INFORMS Best Dissertation Award) and Sameer Kulkarni (recipient of the Transportation Research Board Graduate Research Award). His research has been funded by the National Science Foundation, Department of Defense, NIH, and other agencies, addressing critical challenges in security operations, healthcare systems, and transportation networks. His laboratory work centers around the Center for Secure Information Systems and the Center for Air Transportation Systems Research (CATSR), where he develops computational frameworks for dynamic resource allocation in complex adaptive systems.

Michel GoemansView profile

Professor

• Combinatorial Optimization
• Discrete Algorithms
• Approximation Algorithms
• +3 more

Michel Goemans is the RSA Professor and Head of the Department of Mathematics at the Massachusetts Institute of Technology (MIT), with additional affiliations at MIT CSAIL and MIT ORC. Previously, he held the Leighton Family Professorship (2007-2017) and adjunct/visiting positions at the University of Waterloo, University of Louvain, and RIMS Kyoto. His research focuses on combinatorial optimization , discrete algorithms , and approximation methods , with applications spanning network design, stochastic optimization, and algorithmic game theory. His publications consistently explore fundamental problems in mathematical programming and theoretical computer science. Notable awards include: Leroy P. Steele Prize (2022) George B. Dantzig Prize (2021) Farkas Prize (2012) Fellowships: AMS, ACM, SIAM, Guggenheim, Sloan Foundation Doctor Honoris Causa (Université catholique de Louvain) He advises doctoral candidates and postdocs, with prominent former students including David Williamson (Cornell), Jon Kleinberg (Cornell), and Aleksander Madry (MIT). Research is primarily funded by NSF and ONR grants.

• Computational Intelligence
• Evolutionary Multi-objective Optimization
• Evolutionary Machine Learning
• +5 more

Professor Hisao Ishibuchi is Chair Professor of Computer Science and Engineering at Southern University of Science and Technology (SUSTech) in Shenzhen, China, a role he has held since April 2017. Previously, he spent nearly three decades at Osaka Prefecture University, progressing from Research Associate (1987-1993) to Assistant Professor (1993), Associate Professor (1994-1999), and full Professor (1999-2017). He is an IEEE Fellow , served as Vice-President of the IEEE Computational Intelligence Society (2010-2013) , and is currently President of the Japan Society for Evolutionary Computation (2016-2018) . He is Editor-in-Chief of IEEE Computational Intelligence Magazine (2014-2019) and the Journal of the Japan EC Society (2014-2018). Education: Ph.D. in Engineering, Osaka Prefecture University, 1992 M.S. in Engineering, Kyoto University, 1987 B.S. in Engineering, Kyoto University, 1985 Research Focus: Professor Ishibuchi is internationally recognised as a pioneer of computational intelligence , with seminal contributions to evolutionary multi-objective optimisation , evolutionary machine learning , fuzzy systems , neural networks , and hybrid intelligent systems . He introduced the first multi-objective memetic algorithm and early methods for multi-objective fuzzy rule-based classifier design that balance accuracy and interpretability. Publications & Impact: With over 100 journal papers in top-tier venues such as IEEE Transactions on Evolutionary Computation and nearly 500 conference papers, his work has attracted more than 24 000 Google-Scholar citations and an h-index of 68. His recent articles concentrate on many-objective optimisation, fuzzy machine learning, and transfer learning techniques. Honours & Awards: IEEE Computational Intelligence Society Fuzzy Systems Pioneer Award 2019 IEEE Fellow 2014 JSPS Prize 2007 (Japan’s most prestigious mid-career award) Multiple Best Paper Awards from GECCO, FUZZ-IEEE, SCIS & ISIS, WAC, ACIIDS, HIS-NCEI, and others Teaching & Mentoring: At SUSTech he teaches Advanced Algorithms and Advanced Optimization Algorithms , covering greedy algorithms, hyper-heuristics, memetic algorithms, multi-objective optimisation, and performance assessment. His research group actively recruits post-doctoral fellows and research assistants in evolutionary computation, fuzzy systems, and neural networks. Labs & Teams: He leads the Computational Intelligence Research Group at SUSTech, maintaining active collaboration networks across Asia, Europe, and North America, and supervising several post-doctoral researchers and graduate students working on next-generation intelligent systems.

• Machine Learning Theory
• Foundations of Artificial Intelligence
• Operations Research
• +5 more

Yunzong Xu is an Assistant Professor in the Department of Industrial and Enterprise Systems Engineering (ISE) and Coordinated Science Laboratory (CSL) at the University of Illinois at Urbana-Champaign, with affiliations in the Departments of Computer Science (CS) and Electrical and Computer Engineering (ECE). He holds a Ph.D. in Data, Systems, and Society from MIT (2023) and dual B.S. degrees from Tsinghua University in Economics and Mathematical Sciences (2018). His research focuses on machine learning theory , foundations of AI , operations research , and management science , with specific interests in online learning , deep learning , sequential decision making , and mathematical problems related to markets , incentives , and social good . His work bridges theoretical analysis with applications in dynamic pricing, reinforcement learning, and network revenue management. Recent publications highlight algorithmic complexity in contextual bandits, offline reinforcement learning , and phase transitions in constrained bandit problems. His research has been recognized by awards from INFORMS, Applied Probability Society, and IBM Service Science. Honorable Mention, INFORMS George Nicholson Student Paper Competition (2020) Winner, INFORMS Data Mining Best Theoretical Paper Award (2020) Finalist, Applied Probability Society Best Student Paper Award (2019) Finalist, INFORMS Undergraduate Operations Research Prize (2018) Finalist, IBM Service Science Best Student Paper Award (2021) He teaches graduate courses on Foundations of Modern Machine Learning (IE598) and undergraduate courses in optimization models (IE310). For more information, visit his personal homepage .

• Integer Programming
• Operations Research
• Cutting and Packing
• +5 more

José Manuel Vasconcelos Valério Carvalho is a Full Professor at the School of Engineering, University of Minho, and Senior Researcher at Algoritmi Research Center's SEOR R&D Group. His academic career spans decades with significant contributions to combinatorial optimization and operations research. His educational background includes: PhD in Production Engineering (Operational Research major) from University of Minho MSc in Industrial Engineering and Operations Research from Virginia Polytechnic Institute (Fulbright scholar) Research focuses on large-scale integer programming applications in cutting/packing, network design, and operations planning. He develops exact and heuristic algorithms for complex optimization problems, frequently integrating domains like bin packing with vehicle routing using arc-flow formulations and column generation. Recent publications (2016-2025) reveal strong trends toward integrated logistics solutions, emphasizing temporal aspects, multi-trip scenarios, and combined routing/packing challenges. His methodologies consistently yield robust formulations for real-world supply chain applications. He has supervised 7 postdoctoral and 12 PhD students, many achieving award-winning work. Research leadership includes coordination of FCT-funded national projects and European project workpackages. As core member of Algoritmi's SEOR Group and former coordinator of the SEOOR Research Line (consistently rated 'Excellent' by international panels), he contributes to Portugal's leading operations research team.

Frits C.R. SpieksmaView profile

Professor

• Combinatorial Optimization
• Operations Research
• Scheduling
• +7 more

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).

Kamlesh MathurView profile

Professor

• Supply Chain Management
• Operations Research
• Vehicle Routing
• +4 more

Kamlesh Mathur is a Professor and Chair of the Operations Department at the Weatherhead School of Management, Case Western Reserve University. He has been affiliated with the institution since his initial appointment in 1980 and contributes to supply chain logistics, vehicle routing, and service center location optimization. Education: PhD (Case Western Reserve University, 1980), MS (Wayne State University, 1976), MTech (IIT Kanpur, 1975), BE (Engineering College Jodhpur, 1972) His research focuses on supply chain distribution, emphasizing vehicle routing efficiency and service center location strategies . Recent publications highlight stochastic optimization, multi-echelon logistics, and reverse supply chain design, with applications in manufacturing and telecommunications. Key trends in his work include location-allocation modeling , heuristic algorithm development , and interactive software systems for operations research problems, reflecting interdisciplinary applications from biomedical engineering to radio frequency management. Dr. Mathur teaches courses in business statistics, predictive modeling, and global supply chain logistics. He serves on the editorial review board of the Journal of Business Logistics and is a long-standing member of INFORMS.

• Operations Research
• Scheduling Algorithms
• Discrete-Continuous Scheduling
• +6 more

Professor Joanna Józefowska is a distinguished academic at Poznań University of Technology, where she serves as a full professor in the Faculty of Computing and Telecommunications, Institute of Computer Science. With a career spanning over three decades since completing her doctoral dissertation in 1990, she has established herself as a leading expert in operations research and scheduling algorithms. Her research spans several key areas within operations research, with particular emphasis on: Discrete-continuous scheduling problems Resource allocation and optimization Just-in-time scheduling models Applications of apportionment theory Energy-efficient computing systems Quantum approaches to transportation problems Professor Józefowska's recent work demonstrates her ability to adapt traditional operations research methods to address contemporary technological challenges, with publications in 2022-2023 focusing on energy-efficient computing systems and quantum approaches to electric vehicle charging infrastructure. Her research output shows consistent productivity throughout her career, with publications appearing in reputable journals such as European Journal of Operational Research, Annals of Operations Research, and Computers and Operations Research. As an academic mentor, Professor Józefowska has successfully guided multiple doctoral students to completion, with dissertations covering topics ranging from ontology enrichment to discrete resource allocation. Her supervision has helped develop the next generation of researchers in operations research and computer science, contributing to the academic ecosystem at Poznań University of Technology and beyond. Her scholarly contributions extend beyond research and supervision to include editorial roles for significant publications in her field, demonstrating recognition of her expertise by the broader academic community. Professor Józefowska continues to actively contribute to the advancement of knowledge in operations research while maintaining her position as a respected academic at one of Poland's leading technical universities.

• Software Engineering
• Requirements Engineering
• Agile Methodologies
• +4 more

Professor Jerzy Nawrocki is a distinguished academic at the Poznań University of Technology, where he serves in the Faculty of Computer Science and Telecommunications within the Institute of Computer Science. With a career spanning several decades since completing his doctoral dissertation in 1984 on 'Cost analysis of dynamic memory allocation in real-time systems,' he has established himself as a prominent researcher in software engineering. Professor Nawrocki's research primarily focuses on: Software Engineering methodologies and practices Requirements Engineering, particularly Non-functional Requirements Agile Software Development processes Use Case Analysis and specification Code Quality assessment and metrics Software Documentation quality models His recent work has examined task-specific code metrics, the application of Definition of Done in agile projects, and the maintenance of non-functional requirement templates. Professor Nawrocki has published extensively, with numerous articles in reputable journals such as IEEE Access, Journal of Systems and Software, and Information and Software Technology. Professor Nawrocki has supervised eight doctoral dissertations, mentoring researchers who have gone on to make their own contributions to the field of software engineering. His students include Sylwia Kopczyńska, Bartosz Alchimowicz, Jakub Jurkiewicz, and Mirosław Ochodek. His work demonstrates a consistent focus on empirical approaches to software engineering challenges, with many of his publications presenting empirical studies that validate or challenge existing practices in the field.