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Giorgio GalloView profile

Professor

• Operations Research
• Algorithms
• Optimization
• +4 more

Giorgio Gallo is a Professor of Operations Research at the Dipartimento di Informatica, Università di Pisa. He has held academic roles since 1969, including Associate Professor (1974–1981) and Professor (1983–present). His research spans Operations Research, Algorithms, and Peace Studies, focusing on interdisciplinary approaches to global challenges like conflict resolution and sustainability. He led the Department of Computer Science (1986–1989) and founded the Interdisciplinary Center 'Scienze per la Pace'. His work integrates systems thinking and ethical considerations in decision-making. Education: Laurea in Electronic Engineering, University of Palermo, 1967 Postdoctoral studies, CNR, Rome, 1967–1969 Research Interests: Conflict Theory, Complexity, Systems Approach, and Applications of Operations Research to Peace Studies. He emphasizes transdisciplinary methods to address global issues like climate change and asymmetric conflicts. Publications: Over 100 articles and books on optimization algorithms, logistics, and peace studies. Recent works analyze conflict dynamics and systemic solutions to socio-environmental challenges.

Sampath K. KannanView profile

Professor

• Algorithms
• Computational Biology
• Program Checking
• +5 more

Professor Sampath K. Kannan is the Henry Salvatori Professor in the Department of Computer and Information Science at the School of Engineering and Applied Science, University of Pennsylvania. His research focuses on algorithms, computational biology, program checking, and network security. ACM Fellow (2013) ACM SIGACT Distinguished Service Award (2012) Outstanding Faculty Advising Award (2005) Ford Foundation 'Best Advisor' Award (2004) His research spans algorithms (massive data processing, shortest-path optimization), computational biology (evolutionary tree reconstruction, phylogenetic analysis), and program checking (runtime verification, trust management). Recent work involves data stream algorithms and healthcare scheduling optimization . He has taught advanced courses in algorithms and complexity theory since 1994, including CIS 677 (Complexity Theory) and GCB 537 (Computational Biology). His publications bridge theoretical computer science and applied systems research , with a focus on scalability, correctness, and security.

• Operations Research
• Optimization
• Game Theory
• +5 more

Evangelos Magirou is a Professor of Operations Research at the Department of Informatics, Athens University of Economics and Business (AUEB) since 1986. He holds a B.S. in Electrical Engineering from Princeton University (1971), M.S. and Ph.D. in Decision and Control Sciences from Harvard University (1976). His research focuses on theoretical and applied operations research, optimization, game theory, and their applications in finance, shipping, and energy policy. Education: B.S. Electrical Engineering, Princeton University (1967-71) M.S. Decision and Control Sciences, Harvard University (1971-72) Ph.D. Decision and Control Sciences, Harvard University (1972-76) Research Interests: Theoretical and applied operations research, optimization techniques, game theory applications in transportation and energy, quantitative methods in shipping economics, and policy analysis. Notable projects include stochastic vessel positioning models, strategic petroleum reserve optimization, and spam email game theory. Professional Roles: Member of the Governing Board of Public Power Corporation S.A. (since 2004) Chairman of the Faculty Union of AUEB (1994-present) Deputy Chairman, Department of Informatics (2003-present) Key Contributions: Over 30 peer-reviewed publications spanning operations research, control systems, and maritime logistics. Active in academic leadership roles and policy advisory committees.

Cristina RequejoView profile

Associate Professor

• Operational Research
• Integer Programming
• Combinatorial Optimization
• +7 more

Cristina Requejo is an Associate Professor at ISEG – Lisbon School of Economics and Management, University of Lisbon, and a researcher at CEMAPRE/REM. Her academic career spans institutions including the University of Coimbra and the University of Aveiro, where she held teaching and research roles in mathematics and operational research. Education: PhD in Mathematics, Operations Research – Faculty of Sciences, University of Lisbon (2004) Master's in Operational Research – University of Lisbon Degree in Mathematics – University of Coimbra Her research centers on optimization, particularly integer programming, combinatorial optimization, robust optimization, vehicle routing, and healthcare logistics. She applies advanced mathematical programming techniques to real-world problems in logistics, scheduling, and network design. Her work bridges theoretical algorithm development and practical applications in sectors like healthcare and transportation. The most recent publications reflect a strong focus on MIP-based heuristics, Lagrangian duality, and vehicle routing with skill and temporal constraints. Her research consistently appears in high-impact operations research journals, demonstrating sustained contributions to algorithmic and modeling advances in optimization. Scientific Awards: No awards explicitly mentioned in the text. She has supervised multiple graduate students, including doctoral candidates funded by FCT scholarships, and has contributed to active research projects promoting international collaboration. Her teaching includes advanced courses in decision making, optimization, and operational research at the doctoral and master’s levels. She is affiliated with research labs such as CEMAPRE/REM, contributing to a vibrant research environment in applied mathematics and economics.

• Algorithms
• Scheduling Theory
• Combinatorial Optimization
• +5 more

Baruch Schieber is a Professor in the Department of Computer Science at the Ying Wu College of Computing, New Jersey Institute of Technology (NJIT). He currently serves as the Director of the Institute for Future Technologies, a joint Ben Gurion University-NJIT institute. Prior to joining NJIT, he worked at IBM Research where his last role was Manager of Mathematics of AI. Dr. Schieber earned his Ph.D. in Computer Science from Tel-Aviv University, his M.Sc. in Computer Science from Technion, and his B.Sc. in Computer Science (summa cum laude) from Technion. Dr. Schieber's research spans multiple areas of theoretical computer science and optimization. His work primarily focuses on algorithms, scheduling theory, graph theory, and combinatorial optimization . He has made significant contributions to resource allocation problems, real-time scheduling, and approximation algorithms. His research has applications in diverse fields including network optimization, operations research, and computational geometry. Dr. Schieber's approach often combines theoretical rigor with practical applications, as evidenced by his work at IBM Research and continued academic research. His recent publications (2023-2025) reveal continued focus on optimization problems with expansion into areas like private key security, fair voting systems, and parallel sequence analysis, while maintaining his core expertise in scheduling and combinatorial optimization problems. The breadth of his work demonstrates adaptability to emerging challenges while building on decades of foundational research. Dr. Schieber maintains an active research program with numerous collaborations across academia and industry. His publication record spanning from 1986 to 2025 demonstrates remarkable longevity and continued productivity in theoretical computer science. His leadership as Director of the Institute for Future Technologies indicates significant recognition of his expertise within the academic community.

Francesco CarrabsView profile

Professor

• Operations Research
• Combinatorial Optimization
• Graph Theory
• +5 more

Francesco Carrabs is a Full Professor of Operations Research in the Department of Mathematics at the University of Salerno, Italy, a position he has held since 2024. Previously, he served as Associate Professor (2020-2024) and Assistant Professor (2008-2020) at the same institution, demonstrating a steady progression through the academic ranks. Dr. Carrabs earned his PhD in Computer Science from the University of Salerno in 2006 and graduated cum laude in Computer Science from the same university in 2002. His international research experience includes a visiting scholar position at the Centre de recherche sur les transports (CRT) at the University of Montréal, Canada (2004-2006) under Professors Gilbert Laporte and Jean-François Cordeau, followed by a post-doctoral fellowship at HEC Montréal (October 2006-March 2007). His research focuses on theoretical study, development and implementation of exact and heuristic approaches for optimization problems on graphs. His primary research areas include Routing problems, Variants of Spanning Tree problems, Traveling Salesman Problems, Labeled Graph problems, and Wireless Sensor Networks problems. This program has resulted in approximately 50 publications across various formats. His recent publications reveal a strong emphasis on constrained graph problems, particularly those involving conflict constraints in spanning trees and set covering problems. His methodological contributions span exact approaches like branch-and-cut algorithms and heuristic methods including GRASP and genetic algorithms, often accompanied by publicly available datasets and source code. Dr. Carrabs has participated in numerous national and international research projects and has served on scientific committees for various conferences. His teaching activities at the University of Salerno focus on Operations Research and Optimization courses within the SSD MATH-06/A classification. With an h-index of 16 and over 800 citations (excluding approximately 13% self-citations), Dr. Carrabs has established himself as a respected researcher in combinatorial optimization and operations research.

• Discrete Optimization
• Multilevel Programming
• Network Flows
• +5 more

Leonardo Lozano is an Associate Professor in the Department of Operations, Business Analytics, and Information Systems at the University of Cincinnati's Lindner College of Business. He holds a Ph.D. in Industrial Engineering from Clemson University (2017), an M.Sc. from the University of Florida (2014), and both an M.Sc. and B.Sc. from Universidad de los Andes (2011, 2009). His research integrates theoretical and applied optimization, focusing on discrete optimization, multilevel programming, and network flows to address challenges in vehicle routing, scheduling, and security. Dr. Lozano's work emphasizes algorithmic innovation for complex decision-making under uncertainty. His recent publications explore neural network optimization, robust logistics, and stochastic modeling, reflecting a consistent focus on scalable computational methods. Key methodologies include decision diagrams, bilevel programming, and exact algorithms for large-scale networks. Awards & Honors : Glover-Klingman Best Paper Award (2020), Lindner Research Excellence Award (2024), Daniel J. Westerbeck Teaching Award (2022), and multiple Dean's Teaching Excellence recognitions. Grants & Projects : Co-led a $418K Air Force project (2022-2025) on UAV surveillance optimization and a $300K Office of Naval Research grant (2019-2021) for robust optimization. Current work includes autonomous trucking network optimization. He serves as Faculty Advisor for the INFORMS Student Chapter and actively contributes to departmental leadership, including roles as Assistant Department Head and committee chair. Dr. Lozano reviews for top journals like Operations Research , Mathematical Programming , and INFORMS Journal on Computing .

• Theoretical Computer Science
• Algorithm Design and Analysis
• Discrete Optimization
• +3 more

Tatsuya Terao is a Research Fellow at Kyoto University's Research Institute for Mathematical Sciences, holding a prestigious JSPS DC1 fellowship from April 2024 to March 2027. He operates within the Discrete Optimization Group under the supervision of Professor Yusuke Kobayashi, having completed both his Bachelor of Science (2022) and Master of Science (2024) at Kyoto University. His research focuses on theoretical computer science with particular expertise in matroid theory , quantum query algorithms , and discrete optimization . Terao has developed novel approaches for matroid intersection approximation, parameterized quantum algorithms for graph problems, and submodular maximization with matroid constraints. His work demonstrates a consistent pattern of improving query complexities and developing innovative algorithmic techniques that advance theoretical boundaries in combinatorial optimization. His recent publications reveal a strong trend toward optimizing independence oracle queries in matroid problems while expanding into quantum computing applications for fundamental graph problems. The research shows increasing sophistication in handling complex constraints while reducing computational requirements. Research Fellow of the Japan Society for the Promotion of Science (DC1) Terao collaborates extensively with leading researchers in theoretical computer science, particularly with his advisor Yusuke Kobayashi, as evidenced by multiple co-authored publications in top-tier conferences. His work on the shortest disjoint paths problem demonstrates effective interdisciplinary collaboration with Hirai and Namba's research on (A+B)-paths. While no formal grants are explicitly mentioned beyond his JSPS fellowship, his research direction suggests strong institutional support for theoretical algorithm development. Working within Kyoto University's Research Institute for Mathematical Sciences, Terao contributes to Japan's strong tradition in theoretical computer science research, particularly in combinatorial optimization and discrete mathematics. His research group appears focused on pushing the boundaries of what's computationally feasible in matroid theory and quantum algorithm design.

• Analysis of Algorithms
• Data Analysis
• Massive Data Sets
• +15 more

Robert Krauthgamer is the Harry Weinrebe Professor of Computer Science and currently serves as Department Head in the Department of Computer Science & Applied Mathematics at the Weizmann Institute of Science , within the Faculty of Mathematics and Computer Science . He is a leading researcher in theoretical computer science, particularly in the analysis of algorithms. Research Interests: His research focuses on Analysis of Algorithms , with deep expertise in Data Analysis and Massive Data Sets , Combinatorial Optimization , Approximation Algorithms , Hardness of Approximation , Embeddings of Finite Metrics , and Routing and Peer to Peer Networks . He also maintains a broad interest in Discrete Mathematics and High-Dimensional Geometry . His recent publications highlight work in graph algorithms, parameterized complexity, streaming algorithms, and metric embeddings. Publication Trends: His most recent work, including papers from SODA 2016, demonstrates a strong trend in the design and analysis of efficient algorithms for fundamental problems in graph theory, optimization, and data streams. Key themes include kernelization and sampling techniques for dynamic graph streams, subexponential parameterized algorithms, deterministic derandomization of the polynomial method, and structural results for graph modification problems. His research often bridges theoretical insights with applications in computational biology and network science. Service and Recognition: Journal Editorial: Editor-in-Chief of SIAM Journal on Computing (2019–2025), Associate Editor (2012–2017); Managing Editor of Theory of Computing (2007–2018), and current Editorial Board Member. Conference Leadership: Program Committee Chair for SODA 2016 and HALG 2018; Steering Committee member for SODA, ESA, and HALG; and committee member for the Gödel Prize (2019–2021). Workshops: Organizer of numerous workshops on sublinear algorithms, fine-grained complexity, and high-dimensional data. Teaching and Mentorship: He regularly teaches advanced courses such as Randomized Algorithms and Sublinear Time and Space Algorithms . He advises a large group of MSc and PhD students and hosts postdoctoral researchers, demonstrating a strong commitment to training the next generation of computer scientists. His former students have gone on to successful academic and research careers. Laboratories and Research Groups: He is a key member of the Foundations of Computer Science (theory) seminar at Weizmann and has organized the TheoryLunch and Reading Group in Algorithms, fostering a vibrant research community within the department.

Maksim KitsakView profile

Associate Professor

• Network Science
• Complex Networks
• Hyperbolic Geometry
• +6 more

Maksim Kitsak is an Associate Professor (tenured) at the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology. He serves as the M.Sc. Program Coordinator for the Wireless Communication and Sensing track and is an Editorial Board Member for Nature Scientific Reports and IEEE Transactions on Network Science and Engineering. He also co-chairs the Dutch Network Science Society. Dr. Kitsak earned his Ph.D. in Physics from Boston University in 2009, advised by Prof. H.E. Stanley and Prof. S. Havlin. His academic career includes positions at Northeastern University, Dana-Farber Cancer Institute, and University of California San Diego before joining Delft University of Technology in March 2020 as a tenure-track Assistant Professor, achieving tenure in May 2022. His research spans network science with expertise in complex networks, hyperbolic geometry, epidemic spreading, and network resilience. He has pioneered work on using latent geometric spaces to understand network structure and function, with applications in communication networks, pandemic forecasting, and molecular biology. His influential 2010 Nature Physics paper on identifying influential spreaders in networks challenged conventional wisdom and spurred significant follow-up research. His publication record shows a strong trajectory with recent work focusing on hyperbolic geometry applications across multiple dimensions, network resilience in transportation systems, and epidemic modeling. His research has appeared in high-impact journals including Nature Communications, Nature Scientific Reports, and Physical Review journals, demonstrating both theoretical depth and practical applications. NWO VICI research grant for studying shortest paths in large incomplete networks Dutch Research Council (NWO) funding for "Mathematical Description and Inference of Complementarity Mechanisms in Complex Networks" Seed funding from TU Delft Safety and Security Institute for privacy and security theory Dr. Kitsak actively mentors students including Ph.D. candidates Roberto Gheda (Federated Learning, AI, and Privacy), Yongdin Tian (decentralized Machine Learning), and Elizaveta Evmenova (complementarity principles in networks), as well as M.Sc. students. His research group at Delft University of Technology focuses on advancing network science theory while applying insights to real-world problems in communication networks, network medicine, and pandemic response, with two current postdoctoral openings funded by the NWO VICI grant.

Robert Endre TarjanView profile

Professor

• Algorithms
• Data Structures
• Graph Theory
• +4 more

Robert E. Tarjan is the James S. McDonnell Distinguished University Professor of Computer Science at Princeton University, where he has been a faculty member since 1985. He maintains an active research program in theoretical computer science and algorithms despite being on leave status. His career spans multiple prestigious academic institutions and significant industry collaborations. His educational background includes: B.S. in Mathematics from Caltech (1969) M.S. in Computer Science from Stanford University (1971) Ph.D. in Computer Science from Stanford University (1972) Tarjan's research has fundamentally shaped theoretical computer science, particularly in graph algorithms and data structures. He pioneered the use of depth-first search techniques, developed amortized analysis for data structures, and created influential algorithms including union-find with path compression, splay trees, and Fibonacci heaps. His work bridges theoretical elegance with practical applications in network optimization and computational problems. The concepts he developed are now standard material in undergraduate algorithm courses worldwide. Tarjan has received numerous prestigious honors: ACM Turing Award (1986) for fundamental achievements in algorithms and data structures First recipient of the Rolf Nevanlinna Prize (1983) Membership in the National Academy of Sciences (1987), National Academy of Engineering (1988), and American Philosophical Society (1990) SIAM Fellow (2009), ACM Fellow (1994), and American Academy of Arts & Sciences Fellow (1985) Caltech Distinguished Alumni Award (2010) and Blaise Pascal Medal (2004) As an advisor, Tarjan has mentored notable computer scientists including Danny Sleator, Neal Sarnak, and Haim Kaplan. His industry collaborations have been extensive, with significant roles at AT&T Bell Laboratories (1980-1989), NEC Research Institute (1989-1997), InterTrust Technologies (1997-2001), Compaq/HP Research Labs (2002-2003), and Microsoft. These partnerships have translated theoretical advances into practical applications while securing research funding for his academic work. In March 2025, Tarjan was recognized with a lifetime achievement award from the International Congress of Basic Science for his outstanding contributions to theoretical computer science. Tarjan's research laboratory has been a hub for theoretical computer science innovation, with particular focus on combinatorial algorithms. His approach emphasizes elegance and efficiency, seeking what he describes as algorithms from 'The Book' that records God's most elegant mathematical proofs and algorithms. His dynamic research group has consistently produced groundbreaking work that bridges theoretical insights with practical computational problems.

Christian ScheidelerView profile

Professor

• Distributed Algorithms
• Data Structures
• Security in Distributed Systems
• +4 more

Christian Scheideler is a Professor at the Institute for Computer Science (Theory of Distributed Systems) within the Faculty of Electrical Engineering, Computer Science, and Mathematics at the University of Paderborn. He serves as institute director, advisory committee member of SPAA, and associate editor for Journal of the ACM and Journal of Computer and System Sciences . His editorial and leadership roles extend to managing Journal of Interconnection Networks and steering committees for conferences like DISC, SIROCCO, and ALGOSENSORS. Director, Institute for Computer Science Advisory Committee, SPAA Associate Editor, Journal of the ACM and Journal of Computer and System Sciences Managing Editor, Journal of Interconnection Networks Steering Committee member for DISC, SIROCCO, ALGOSENSORS His research focuses on distributed algorithms, data structures, security in distributed systems, randomized algorithms, stochastic processes, network theory, and discrete mathematics . Notably, he investigates hybrid programmable matter systems, self-stabilizing overlay networks, and robust distributed protocols under adversarial conditions. Recent publications (2021-2025) highlight work on 3D programmable matter shape formation , low-diameter graph decompositions , reconfigurable circuits in amoebot models , and blockchain lightweight state replication . Topics span distributed computing, computational geometry, and network security. Scientific awards include the 2022 Edsger W. Dijkstra Prize in Distributed Computing for foundational work in self-stabilization and overlay networks. He has served as PC Chair (DISC 2022) and organized numerous conferences (SPAA, SSS, SIROCCO).

Domagoj MatijevićView profile

Associate Professor

• Machine Learning
• Optimization
• Bioinformatics
• +5 more

Domagoj Matijević is an Associate Professor at the School of Applied Mathematics and Informatics within Josip Juraj Strossmayer University of Osijek . His academic career spans computational geometry, optimization algorithms, and bioinformatics applications. PhD in Computer Science (Algorithms and Complexity), Max-Planck-Institute for Computer Science, Saarbrücken (2007) MS in Computer Science, Saarland University (2002) BS in Mathematics and Computer Science, University of Osijek (2001) Research interests focus on Machine Learning , Computational Geometry , and Bioinformatics , particularly through software tools like Fortuna for RNA splicing analysis and Trajan for comparing single-cell trajectories. His work bridges theoretical computer science with practical implementations in C++ , Python , and CUDA for high-dimensional data processing. 2023 Best Paper Award at MIPRO's Artificial Intelligence Systems track Key contributor to Neural Network-Based Pollen Prediction and Well-Separated Pair Decomposition implementations Developed Trajan for dynamic pseudotime warping and Fortuna for novel splicing event detection Currently teaches Algorithm Complexity , Computational Geometry , and Embedded Systems . Past projects include NVIDIA-funded GPU implementations and German-Croatian collaborations on kinetic spanners.

Adam Teodor PolakView profile

Assistant Professor

• Algorithms
• Fine-Grained Complexity
• Learning-Augmented Algorithms
• +3 more

Adam Teodor Polak serves as an Assistant Professor in the Department of Computing Sciences at Bocconi University, where his research centers on theoretical algorithms with dual emphases on fine-grained complexity and learning-augmented algorithms. His work investigates fundamental questions about computational hardness while developing prediction-enhanced algorithms that maintain worst-case guarantees. Polak earned his PhD from Jagiellonian University in 2019 under Paweł Idziak, including a research visit at MIT with Virginia Vassilevska Williams. He subsequently held postdoctoral positions at the Max Planck Institute for Informatics and EPFL before joining Bocconi. His research program addresses why computational problems resist efficient solutions and how imperfect predictions can robustly improve algorithmic performance. This manifests in two interconnected streams: establishing conditional lower bounds for problems like 3SUM and Orthogonal Vectors, and designing learning-augmented frameworks for dynamic graph problems, caching, and optimization that blend theoretical rigor with practical machine learning insights. Recent publications reveal accelerating momentum in algorithms with predictions, with over half of his 2023-2025 output appearing in top ML venues (ICML, NeurIPS, ICLR) alongside traditional theory conferences (STOC, SODA). This cross-pollination demonstrates how worst-case theoretical guarantees can coexist with data-driven performance gains across graph algorithms, scheduling, and combinatorial optimization. Scientific recognition includes: Best Paper Award at ESA 2024 for knapsack algorithm breakthroughs Bronze Medal at ACM ICPC World Finals (2011) 2nd Place in PACE 2018 Challenge for Steiner tree algorithms Polak actively shapes the field through program committee service (ESA, ICALP, SOSA) and community building, notably co-organizing the 2022 Workshop on Algorithms with Predictions (ALPS) and decade-long high-school algorithmics workshops. His industry collaborations with Teroplan and Google demonstrate real-world impact in route planning and distributed systems. Current teaching includes graduate Algorithms courses at Bocconi, while his experimental work on GPU-accelerated graph algorithms and medical computer vision continues to bridge theoretical insights with practical implementation challenges.

Marcel MongeauView profile

Professor

• Operations Research
• Global Optimization
• Numerical Optimization
• +5 more

Marcel Mongeau is a Professor in Operations Research at the École Nationale de l'Aviation Civile (ENAC) in Toulouse, France, affiliated with the OPTIM team under the MORO axis . His research focuses on operational research, global optimization, numerical optimization, and mixed-integer programming, with applications in aeronautics, chemical engineering, life sciences, and other industries. Education PhD in Combinatorics & Optimization (1991), University of Waterloo, Canada MSc in Mathematics (1987), University of Montreal, Canada BSc in Mathematics (1985), University of Montreal, Canada Habilitation to Direct Research (2003), Paul Sabatier University, France Research Interests : Mongeau's work addresses complex optimization problems through mathematical modeling and algorithm development. Key areas include aircraft trajectory planning, contrail avoidance, stochastic programming, and multi-modal transportation optimization. His methodologies integrate exact optimization, heuristic approaches, and computational modeling. Publications Trends : Recent publications emphasize air traffic flow management , contrail-safe trajectories , stochastic aircraft scheduling , and multi-modal transportation synchronization . Applications span environmental impact minimization, machining efficiency, and climate-aware aviation planning. Scientific Recognition : He is a Member of the Academy of Technologies , reflecting his contributions to applied mathematical sciences. Advising & Grants : Mongeau has supervised PhD students and postdoctoral fellows, particularly in optimization and operational research. His research has been supported through collaborations with institutions like IBM, INRIA, and CNRS. Labs & Teams : He is associated with the OPTIM team at ENAC and previously worked with the MOGISA group at LAAS-CNRS and the Optimisation & Interactions group at Paul Sabatier University.