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Sivaram NarayanView profile

Professor

• Operator Theory
• Matrix Analysis
• Linear Algebra
• +3 more

Sivaram Narayan is a Professor of Mathematics at Central Michigan University (CMU), affiliated with the Department of Mathematics in the College of Science & Engineering. He holds a Ph.D. from Purdue University (1988), an M.S. from the University of Michigan (1981), and a B.S. from the University of Madras, India (1974). His research focuses on Operator Theory, Matrix Analysis, and Frame Theory, with significant contributions to linear algebra, functional analysis, and graph theory. He has supervised 72 undergraduate researchers, many of whom co-authored papers in refereed journals. Notable awards include the 2009 Outstanding Research Award and 2004 Outstanding Teaching Award from CMU's College of Science & Technology, as well as a 2004 Research Professorship. Narayan has led multiple National Science Foundation (NSF) grants, including REU (Research Experiences for Undergraduates) sites from 2003–2025, fostering undergraduate research in algebra, analysis, and matrix theory. His work on minimum semidefinite rank, frame robustness, and graph theory has advanced the understanding of matrix properties and combinatorial optimization. He has authored/co-authored over 50 publications, including works on composition operators, frame theory, and graph complements. His research often bridges pure mathematics with applications in signal processing and combinatorial frameworks.

• Operations Research
• Optimization Algorithms
• Combinatorial Optimization
• +4 more

Adam Letchford is a Professor in the Department of Management Science at Lancaster University, specializing in Operations Research and Optimization. His research focuses on combinatorial optimization, integer programming, and algorithm design, with particular contributions to vehicle routing, network design, and polyhedral theory. He has authored numerous peer-reviewed articles, including works on clique partitioning polytopes, knapsack problems, and matheuristics. Letchford is actively involved in academic conferences and editorial roles, contributing to the advancement of operational research methodologies and applications in logistics and decision sciences. His work bridges theoretical foundations with practical implementations, addressing challenges in both academic and real-world contexts.

Daniel AndersonView profile

Teaching Professor

• Parallel Computing
• Algorithm Design
• Concurrent Memory Management
• +4 more

Daniel Anderson is an Assistant Teaching Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. He teaches Algorithm Design and Analysis (15-451) and previously taught at Monash University in Australia. His academic journey includes a PhD from CMU under Professor Guy Blelloch and a Bachelor of Science (Honours) in Applied Mathematics from Monash University. His research spans multiple areas of computer science with a strong focus on parallel computing. Anderson develops practical libraries like ParlayLib to make parallel programming more accessible, works on concurrent memory management techniques including "smarter atomic smart pointers," and explores parallel graph algorithms. His work bridges theoretical algorithm design with practical implementation challenges, particularly in making high-performance parallel computing more approachable for programmers. Anderson's publications reveal a strong trajectory in parallel algorithms and memory management, with significant contributions to wait-free algorithms, reference counting techniques, and batch-dynamic data structures. His research consistently appears in top venues like PLDI, SPAA, and EuroPar, demonstrating both theoretical rigor and practical relevance. Recent work focuses on making reference counting as efficient as manual memory reclamation while maintaining usability. Scientific Awards: Herbert A. Simon Award for Teaching Excellence in Computer Science (2024) Alan J. Perlis Graduate Student Teaching Award (2022) While Anderson currently focuses primarily on teaching, he remains active in research supervision, particularly for undergraduate and master's projects related to parallel computing. His work with the ParlayLib research group continues to advance the state of parallel programming tools. Anderson also has experience coaching the Monash University Programming Team for the International Collegiate Programming Contest (ICPC) and serving as a judge for South Pacific Programming Contests.

• Data Mining
• Graph Algorithms
• Network Analysis
• +2 more

Florian Adriaens is a Postdoctoral Researcher in the Department of Computer Science at the University of Helsinki, specializing in data mining, graph algorithms, and theoretical computer science with significant contributions to algorithmic fairness and network analysis. His work bridges theoretical foundations and practical applications in complex graph structures. His core research interests include: Data Mining Graph Algorithms Network Analysis Algorithmic Fairness Theoretical Computer Science Adriaens has published 15 peer-reviewed articles from 2017-2025 in top venues including KDD and WWW, demonstrating evolving expertise from foundational graph problems (diameter minimization, signed graph clustering) to cutting-edge hypergraph optimization and fairness-aware diversification. Recent publications (2024-2025) show increased focus on cardinality-constrained hypergraph cuts, multilayer correlation clustering, and fair representative selection, reflecting growing complexity in his research trajectory. His collaborative work includes researchers like Gionis, Tatti, and Wang, indicating strong interdisciplinary connections within the data mining community. Contact: florian.adriaens@helsinki.fi | ORCID

Raffaele CerulliView profile

Professor

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

Raffaele Cerulli is a Full Professor of Operations Research at the Department of Mathematics of the University of Salerno, Italy, where he also serves as Head of Department. He is a Member of the Scientific Committee of UMI (Unione Matematica Italiana) and Director of the Laboratory "Model and Applications of Mathematical Methods." His office is located at the Fisciano Campus, Building F2, First Floor, Room 026, with reception hours on Tuesdays from 3:00 PM to 5:00 PM and Wednesdays from 3:00 PM to 4:00 PM. Dr. Cerulli's research focuses on Operations Research, Combinatorial Optimization, and Network Theory . His work spans multiple application areas including wireless sensor networks, vehicle routing, spanning tree problems, and graph optimization. He has made significant contributions to problems involving labeled graphs, minimum branch vertices spanning trees, and maximum lifetime problems in sensor networks. His research combines theoretical developments with practical applications, particularly in transportation and network systems. Analysis of his recent publications (2022-2025) reveals continued strong activity in combinatorial optimization, with particular emphasis on flow problems, spanning tree variants, and sensor network optimization. His work demonstrates consistent methodological innovation, frequently employing exact algorithms, metaheuristics, and mathematical programming approaches to solve complex combinatorial problems. Many of his recent papers represent extensions or novel variants of classical optimization problems with practical constraints. Throughout his career, Dr. Cerulli has maintained extensive collaborations, particularly with researchers Carrabs, Gentili, and Raiconi, resulting in numerous joint publications across top-tier operations research journals. His work has contributed significantly to both theoretical developments and practical applications of optimization techniques in network systems.

• Operations Research
• Optimization Algorithms
• Combinatorial Optimization
• +3 more

Cesar Rego is a Full Professor of Operations Management and Business Analytics at the University of Mississippi's School of Business Administration, Department of Marketing, Analytics and Professional Sales. He holds tenured positions at multiple institutions, including visiting roles at MIT. His academic journey includes a B.Sc. in Computer Science and Applied Mathematics from Universidade Portucalense (1990), an M.Sc. in Operations Research from the University of Lisbon (1993), a Ph.D. in Computer Science from the University of Versailles (1996), and a Habilitation (post-doctorate) from the same university (2012). Rego specializes in optimization algorithms, heuristics, and metaheuristics, with a focus on combinatorial optimization, parallel computing, and operations research applications. His research addresses problems like the Traveling Salesman Problem, resource scheduling, and clustering. He has contributed to advancements in Tabu Search, Scatter Search, and Ejection Chain methods. Rego has served on NSF and NSERC committees, organized international conference sessions, and delivered invited lectures. His work spans over 50 publications in journals like European Journal of Operational Research and Management Science. Notable areas include algorithm design for large-scale problems and interdisciplinary curriculum development at the operations research-computer science interface. His career includes faculty roles at Portucalense University and the University of Lisbon, alongside his current tenure at the University of Mississippi since 2011. He advises on complex planning procedures and collaborates on projects involving data analytics and optimization frameworks.

• Theory of Computation
• Fine-Grained Complexity
• Graph Theory and Algorithms
• +7 more

Dr. Amir Abboud is a Senior Scientist in the Department of Computer Science and Applied Mathematics at the Weizmann Institute of Science, a leading research institution in Israel. His work is centered on the theory of computation, particularly fine-grained complexity, aiming to establish exact computational complexity bounds for fundamental problems. Research Interests: Amir Abboud's research spans a broad spectrum of theoretical computer science, with a primary focus on Fine-Grained Complexity . He investigates conditional lower bounds based on popular conjectures such as SETH and 3SUM. His work extends to graph algorithms, dynamic data structures, pattern matching, sequence alignment, exact and parameterized algorithms, distributed computing, and circuit complexity. He employs deep connections between parsing, clique problems, edit distance, and spanner constructions to derive new hardness results. Publication Trends: His recent publications, appearing in top venues like FOCS and STOC, reveal a consistent focus on proving conditional lower bounds and developing improved algorithms for graph problems. Themes include subcubic algorithms for Gomory-Hu trees, additive spanners, and the interplay between parsing and clique detection. His work often bridges algorithmic improvements with complexity-theoretic limitations. Scientific Service: Program Committee Member, FOCS 2023 Program Committee Member, ICALP 2023 Program Committee Member, STOC 2023 Program Committee Member, SWAT 2022 Program Committee Member, SODA 2022 Program Committee Member, SOSA 2022 Program Committee Member, ESA 2020 Program Committee Member, CPM 2020 Program Committee Member, IPEC 2019 Program Committee Member, STOC 2019 Program Committee Member, FOCS 2018 Program Committee Member, CPM 2016 Advising and Grants: While the text does not list any formal students or advisees, Dr. Abboud leads significant research in theoretical computer science and collaborates with top researchers worldwide. His publication record and program committee roles indicate a well-funded and influential research trajectory, likely supported by institutional and national research grants, though specific grants are not mentioned. Labs and Research Groups: Dr. Abboud is affiliated with the theoretical computer science group at the Weizmann Institute of Science. He also organizes a Course on Fine-Grained Complexity and is part of a research group focused on algorithms and complexity, as indicated by the website structure.

• Combinatorial Optimization
• Applied Mathematics
• Integer Linear Programming
• +3 more

Diego DELLE DONNE is an Assistant Professor (tenure-track) at ESSEC Business School in Paris, France, within the Information Systems, Data Analytics and Operations department. He holds a PhD in Computer Science from the University of Buenos Aires (2016), along with earlier degrees from the same institution. Prior to his current role, he held post-doctoral positions at ESSEC, École Polytechnique, and Sorbonne Paris Nord University. His research focuses on combinatorial optimization, applied mathematics, and their real-world applications in logistics, urban planning, and resource management. Education: PhD in Computer Science, University of Buenos Aires (2016) MSc in Computer Science, University of Buenos Aires (2009) BSc in Computer Science, University of Buenos Aires (2008) Research Interests: His work emphasizes optimization algorithms for sustainable urban logistics, such as integrating public transport for last-mile deliveries to reduce CO2 emissions, and integer linear programming applications in hydrocarbon field planning, waste management, and salmon farm operations. He also explores theoretical challenges in graph coloring, polyhedral studies, and sparse approximation techniques. Recent Article Trends: Recent publications highlight advancements in column generation methods for multi-echelon delivery systems, branch-and-price algorithms for clustering and coloring problems, and strategic planning for freight integration with public transit. His work bridges theoretical optimization with practical urban and industrial challenges, emphasizing sustainability and efficiency. Advising & Grants: He has supervised multiple master’s theses, including works on leaf collection optimization and logistics routing. While specific grant details are not detailed, his research projects reflect collaboration with industry partners like petroleum companies in Argentina and urban management initiatives. Labs/Teams: His current project focuses on leveraging public transport’s excess capacity for urban parcel delivery, part of broader efforts to enhance sustainable logistics through mixed-integer programming and operational research techniques.

• Computational Geometry
• Algorithms
• Graph Theory
• +3 more

Mark de Berg is a Professor at Eindhoven University of Technology, The Netherlands, specializing in computational geometry, algorithms, and graph theory. His research focuses on geometric optimization problems, including the Traveling Salesman Problem (TSP), dominating set algorithms, motion planning, and Voronoi games. Recent publications highlight his work on stable approximation algorithms, geometric intersection graphs, and motion planning in rectilinear environments. Collaborations include researchers like Arpan Sadhukhan, Frits C. R. Spieksma, and Boris Aronov. Scientific awards: No awards explicitly mentioned in the provided data. Advising: No student names listed in the provided records.

• Operations Research
• Industrial Engineering
• Optimization Modeling
• +4 more

Mehmet SAVSAR is a Professor at Uskudar University , with a career spanning multiple institutions including Kuwait University, King Saud University, and Pennsylvania State University. His work focuses on industrial engineering, optimization modeling, and simulation analysis across diverse sectors. Current roles: Head of Department (Industrial Engineering) at Uskudar University (2020–2025), Head of Department (Engineering Management) (2021–2025) Education BSc Forest Industry Engineering, Karadeniz Technical University (1975) MSc Industrial Engineering, Pennsylvania State University (1978) PhD Industrial Engineering, Pennsylvania State University (1982) Research Interests Mehmet’s research bridges operations research , simulation modeling , and optimization in industrial and healthcare contexts. His recent work includes AI-driven traffic signal optimization, waste minimization in manufacturing, and lean strategies for battery recycling. Publications highlight applications of linear programming , probabilistic networks , and Markov chain analysis . Scientific Contributions Spanning 2020–2025, his 15 most recent works cover topics from surgical scheduling to urban mobility, emphasizing cross-disciplinary solutions. Key trends include smart city infrastructure , sustainable manufacturing , and data-driven decision-making . Awards Best Paper Award, International Conference on Industrial Engineering and Operations Management (IEOM) 2020 Teaching & Projects Recent courses include Engineering System Simulation , Advanced Quality Control , and Graduation Thesis supervision. He has led projects in facility layout design, warehouse optimization, and healthcare operations.

Inge Li GørtzView profile

Professor

• Algorithms
• Data Structures
• Design and Analysis of Algorithms
• +7 more

Inge Li Gørtz is a Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where she leads research in algorithms and data structures. She is affiliated with the Algorithms, Logic and Graphs section and has been a principal investigator on externally funded research projects. She also serves as chairman of the Danish Society for Computer Science since 2009. Research Interests: Her work centers on the design and analysis of algorithms, with a focus on approximation algorithms, pattern matching, graph algorithms, and compressed data representations. She investigates efficient query processing, string indexing, and algorithmic challenges in highly repetitive data, including applications in bioinformatics and data streams. The recent publications highlight a strong trend in theoretical and practical algorithm design, particularly in string indexing, compressed automata, and dynamic data structures. These works span venues like SOFSEM and STACS, emphasizing innovations in sublinear query time, sliding window models, and memory-efficient representations. Postdoc stipend from Carlsberg Foundation (2006–2008) for 'Approximation of Transportation on Demand Problems', kr 865,183 Advising and Grants: She actively supervises multiple PhD students on projects such as biomedical image segmentation using graph cuts, hierarchical compression of DNA data, and dynamic graph algorithms. She has served as main or co-supervisor in several funded PhD projects. She has been principal investigator on a Carlsberg Foundation grant and has participated in multiple Danish Council for Independent Research projects. Labs and Teams: She is a core member of the Algorithms, Logic and Graphs (ALG) section at DTU, contributing to a vibrant research environment focused on theoretical computer science and algorithmic problem solving.

David P. WilliamsonView profile

Professor

• Discrete Optimization
• Approximation Algorithms
• Traveling Salesman Problem
• +6 more

David P. Williamson is a Professor at Cornell University in the School of Operations Research and Information Engineering (ORIE), with a significant leadership role as former Chair of the Department of Information Science in the Cornell Ann S. Bowers College of Computing and Information Science from July 2021 through December 2023. His academic journey began at MIT where he earned his B.S. in Mathematics (1989), followed by an M.S. (1990) and Ph.D. (1993) in Computer & Information Science under Professor Michel X. Goemans. After completing a postdoc at Cornell under Professor Éva Tardos, he worked at IBM Research at both the T.J. Watson Research Center and Almaden Research Center before joining Cornell University in 2004. B.S. (Mathematics), Massachusetts Institute of Technology (1989) M.S. (Computer & Information Science), Massachusetts Institute of Technology (1990) Ph.D. (Computer & Information Science), Massachusetts Institute of Technology (1993) Professor Williamson's research centers on discrete optimization, specializing in approximation algorithms for NP-hard optimization problems. His work spans network design, scheduling, facility location, clustering, ranking, and particularly the traveling salesman problem. He has made seminal contributions to the field, evidenced by his co-authored paper 'Improved Approximation Algorithms for Maximum Cut and Satisfiability Problems Using Semidefinite Programming' which earned the 2022 AMS Steele Prize. His research approach emphasizes simple yet powerful approximation algorithms with provable performance guarantees, bridging theoretical computer science and operations research. Analysis of his recent publications reveals a strong focus on the traveling salesman problem, with particular attention to integrality gaps of semidefinite programming relaxations, combinatorial algorithms for solving Laplacian systems, and novel approaches to cycle cut instances. His work consistently demonstrates how theoretical insights can yield practical algorithmic improvements, with applications spanning network design, revenue management, and graph theory. Williamson has also contributed significantly to educational resources through his textbook 'Network Flow Algorithms' (2019) and 'The Design of Approximation Algorithms' (2011, with David Shmoys). American Mathematical Society Steele Prize for Seminal Contribution to Research (2022) SIAM Fellow (2016) ACM Fellow (2013) Lanchester Prize for best contribution to operations research (2013) Professor of the Year (ORIE Undergraduate Voted) (2018) ACM STOC 30-year Test of Time Award (2024) Professor Williamson has demonstrated significant academic leadership through his service as Chair of the Department of Information Science and as former Editor-in-Chief for the SIAM Journal on Discrete Mathematics. His teaching portfolio includes undergraduate courses like ENGRI 1101 (introduction to operations research) and ORIE 1380 (introduction to data science), as well as graduate courses including ORIE 6330 (network flows) and ORIE 6334 (spectral graph theory and algorithms). His research has attracted substantial funding from the National Science Foundation, including awards for projects like 'AF: Small: Looking Under Rocks: A Search for a Provably Stronger TSP Relaxation' (2019) and 'AF: EAGER: Approximation algorithms for the traveling salesman problem' (2015). While specific laboratory affiliations aren't detailed in the available information, Professor Williamson's work is deeply embedded in Cornell's theoretical computer science and operations research communities. His research collaborations span multiple institutions, with frequent co-authorship with colleagues at Cornell and beyond. His recent publications indicate active engagement with current challenges in approximation algorithms, particularly those related to the traveling salesman problem and semidefinite programming relaxations, suggesting ongoing leadership in these critical areas of theoretical computer science and operations research.

Mark de BergView profile

Professor

• Algorithms
• Computational Geometry
• Geometric Networks
• +3 more

Mark de Berg is a Full Professor at TU/e and the chair of the TU/e Algorithms Group, focusing on algorithms and spatial data structures. His research spans computational geometry, FPT algorithms, and geometric networks, emphasizing practical applications in complex networks and design. He holds a PhD from Utrecht University (1992) and is co-author of seminal textbooks on computational geometry. Department: Mathematics and Computer Science Institute: EAISI Research Groups: Algorithms, EAISI Foundational His work addresses algorithmic challenges in spatial data efficiency, with over 225 publications and notable grants like the VICI grant and leadership in the Gravitation Program Networks. He serves on editorial boards and conference committees, advancing algorithmic research globally. Recent work explores approximation algorithms for geometric coverage, truthful budget aggregation mechanisms, and geometric network optimization. His contributions bridge theory and application, influencing both academic and industrial domains. Awards: VICI grant from NWO Teaching includes courses on algorithms, geometric algorithms, and discrete mathematics. His research extends to motion planning for robots and diverse combinatorial solutions, showcasing interdisciplinary impact.

Aaron SidfordView profile

Associate Professor

• Optimization
• Algorithm Theory
• Theoretical Computer Science
• +4 more

Aaron Sidford is an Associate Professor in the Department of Management Science and Engineering and the Department of Computer Science at Stanford University. He holds a PhD in Electrical Engineering and Computer Science from MIT, advised by Jonathan Kelner. His research focuses on optimization theory, algorithm design, and computational complexity, with significant contributions to convex optimization, graph algorithms, numerical linear algebra, and machine learning theory. He has taught courses such as Introduction to Optimization Theory (MS&E213/CS269O) and Discrete Mathematics and Algorithms (CME305/MS&E316), emphasizing theoretical foundations and large-scale problem-solving. His work bridges continuous and discrete optimization, often leading to efficient algorithms with proven convergence guarantees. Award highlights include the Best Paper Award at FOCS 2022 and COLT 2022, along with notable recognitions for contributions to dynamic graph algorithms and convex optimization. He advises PhD students focusing on optimization theory and its applications, and his research has been supported by grants from NSF, ONR, and industry partnerships. His current research explores cutting-edge techniques in optimization, including faster max-flow algorithms, memory-efficient convex optimization, and adaptive gradient methods. He collaborates widely, contributing to both theoretical advancements and practical algorithmic implementations.

Jonatan SchroederView profile

Assistant Professor

• Computer Science Education
• Operating Systems
• Computer Networks
• +3 more

Jonatan Schroeder is an Assistant Professor in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering. He holds a Ph.D. in Computer Science from the University of British Columbia (2016) and degrees from Brazil's Federal University of Paraná (B.Sc. and M.Sc.). His work focuses on Computer Science Education, Operating Systems, and Security, emphasizing practical teaching tools to enhance student engagement. He contributes to York University's new Digital Technologies program at the Markham campus. Education: Ph.D. in Computer Science, University of British Columbia, 2016 M.Sc. in Computer Science, Federal University of Paraná B.Sc. in Computer Science, Federal University of Paraná Research & Teaching Interests: Computer Science Education (e.g., PrairieLearn platform) Operating Systems & Networks Computer Architecture & Security Mobile Learning Solutions (e.g., LIVES project) Professional Contributions: Curriculum developer for York's Markham campus Digital Technologies program Published work on fault-tolerant routing, distributed systems, and educational technologies Affiliations: Keele Campus: 4700 Keele Street, Toronto, ON Markham Campus: 1 University Boulevard, Markham, ON