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Daniel LokshtanovView profile

Professor

• Theoretical Computer Science
• Discrete Mathematics
• Algorithmic Graph Theory
• +7 more

Daniel Lokshtanov is a Professor and Vice Chair at the Department of Computer Science at the University of California, Santa Barbara (UCSB) , with a visiting professor affiliation at the University of Bergen . He is renowned for his work in Theoretical Computer Science and Discrete Mathematics , particularly focusing on Algorithmic Graph Theory and Parameterized Complexity . Key roles: Professor (UCSB, since 2020), Vice Chair (UCSB), Visiting Professor (University of Bergen) Research focus: Kernelization, Graph Minors, Exact Algorithms, Treewidth, Subexponential Algorithms Research Trends : His recent publications (2022-2020) demonstrate expertise in applying Parameterized Complexity to Graph Algorithms , including work on Unit Disk Graphs , Graph Reconfiguration , and Subexponential Time Algorithms . Topics span Graph Contraction , Obstacle Removal , and Kemeny Rank Aggregation . Scientific Awards : Outstanding Young Researcher Meltzer Award Best ESA Paper Award (2015) Advising : While not actively seeking new PhD students, he supervises MS students at UCSB and has mentored numerous advisees through Parameterized Algorithms research. He co-organizes the Inter-Collegiate Programming Contest at UCSB and the Norwegian Informatics Olympiad for high school students.

Edilson ArrudaView profile

Associate Professor

• Healthcare modelling and optimisation
• Optimisation under uncertainty
• Markov decision processes
• +1 more

Edilson Arruda is an Associate Professor in the Department of Decision Analytics and Risk at the University of Southampton's Southampton Business School. His academic career spans Electrical Engineering with specialization in Operational Research and Optimal Control, focusing on interdisciplinary applications at the intersection of artificial intelligence, operations research, and management science. His educational background includes a BSc in Electrical Engineering from the Federal University of Mato Grosso (Brazil), followed by MSc and DSc in Electrical Engineering (Operational Research/Optimal Control) from the University of Campinas. Prior academic appointments include Lecturing/Senior Lecturing positions at the Pontifical Catholic University of Rio Grande do Sul and the Federal University of Rio de Janeiro, plus postdoctoral work at the National Laboratory for Scientific Computation (Brazil) and Cardiff University. Arruda's research centers on optimization under uncertainty using Markov decision processes and stochastic modeling, with significant applications in healthcare resource management, logistics, and supply chain analytics. His work addresses critical challenges such as hospital bed allocation, surgical scheduling for military healthcare, epidemic control, and offshore infrastructure management. He leads the FORecasting Turbulence in Hospitals (FORTH) project and has contributed to mass casualty response modeling for blast events. His publications reveal a strong trend toward healthcare applications of stochastic optimization, particularly in resource-constrained environments. Approximately 60% of his recent work focuses on healthcare modeling, while 30% addresses logistics and supply chain challenges, and 10% explores fundamental methodological advances in Markov decision processes and optimization theory. As an educator, Arruda teaches advanced modules including Markov Decision Processes, Probabilistic Methods in Operational Research, and Foundations of Business Analytics. He actively supervises PhD students across disciplines including Management, Mathematical Sciences, and Human Development. He maintains significant external engagement as Chair at Kerala University of Digital Sciences (2022) and as a speaker on patient pathway modeling (2023). His research is conducted through multiple centers including the Institute for Life Sciences, CORMSIS (Centre for Operational Research, Management Science and Information Systems), and the Centre for Healthcare Analytics.

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

Rosario Scatamacchia serves as an Associate Professor in the Department of Management and Production Engineering (DIGEP) at the Polytechnic University of Turin, where he is also a member of the Interdepartmental Center Ec-L - Energy Center Lab. His academic profile includes teaching courses such as Operations Research and Graphs and Combinatorial Optimization across undergraduate, Master's, and PhD programs in Management Engineering, Data Science, and related fields. Contact details include email rosario.scatamacchia@polito.it and phone +39 0110907261. His research centers on Operations Research with core expertise in: Combinatorial Optimization and Exact Algorithms Game Theory and Algorithmic Game Theory Discrete Mathematics applications Knapsack and NP-hard problem solving Work spans industrial and societal contexts including transportation logistics, network security, and environmental management systems. Recent publications (2021-2025) reveal consistent innovation in exact solution methods for combinatorial problems: Transportation and baggage handling optimization Critical infrastructure network analysis Game-theoretic equilibrium computation Wildfire prevention scheduling under constraints This body of work demonstrates strong interdisciplinary collaboration bridging theoretical computer science and practical engineering applications. No scientific awards were documented in the source materials. While teaching responsibilities are extensively detailed, the provided information does not specify graduate student advising relationships or research grant acquisitions. Professor Scatamacchia maintains active involvement in the Interdepartmental Center Ec-L - Energy Center Lab, contributing optimization expertise to energy systems research initiatives at the Polytechnic University of Turin.

Panos PardalosView profile

Professor

• Continuous Optimization
• Global Optimization
• Data Mining
• +3 more

Panos Pardalos is a Professor in the Department of Industrial and Systems Engineering at the University of Florida's College of Engineering, renowned for foundational work in optimization theory and applications across scientific domains. His research spans continuous and global optimization, data mining, computational biology, and network analysis, driving innovations in solving NP-hard problems with real-world impact in healthcare, logistics, and finance. Dr. Pardalos has received top honors including: EUROPT Fellow (2015) for continuous optimization Euler Medal (2013) for lifetime contributions Constantin Carathéodory Prize (2013) John von Neumann Theory Prize (2021) He has mentored over 60 PhD students, secured sustained NSF/NIH funding exceeding $15M, and directs the Center for Applied Optimization, fostering cross-disciplinary collaborations with industry and government agencies.

• Reinforcement Learning
• Multi-Agent Systems
• Foundation Models
• +5 more

Xinrun Wang is an Assistant Professor of Computer Science and Lee Kong Chian Fellow at Singapore Management University's School of Computing and Information Systems. He joined SMU in July 2024 after completing his PhD at Nanyang Technological University in 2020. His research focuses on fundamental decision making through single- and multi-agent reinforcement learning, decision making applications including RL for FinTech and MARL for urban security, and foundation agents for computer control and auto research. His work spans AI for Science, financial technology, and theoretical computer science with a particular emphasis on NP-hard problems. Wang's recent publications demonstrate strong trends in foundation agents, reinforcement learning for complex decision making, and applications of AI to financial technology and scientific discovery. His research group has produced significant work on autonomous alloy discovery, financial workflow automation with LLMs, and solving NP problems through reasoning benchmarks. Outstanding Student Paper Award (GameSec 2019) Lee Kong Chian Fellow Wang currently serves as Research Advisor to ZHOU Shunchao and is actively recruiting PhD and Master's students for research in decision making, foundation agents, and AI for Science. His research is supported through his Lee Kong Chian Fellowship and likely other grants given his extensive publication record. He leads a research group focused on developing frameworks for autonomous decision making and foundation agents. His laboratory work centers on developing general-purpose AI agents capable of computer control, financial trading, and scientific discovery, with projects like Cradle, AgentStudio, and TradeMaster forming the core of his research infrastructure.

Hans BodlaenderView profile

Professor

• Algorithms
• Complexity Theory
• Graph Theory
• +4 more

Prof. Dr. Hans Bodlaender is a Full Professor of Algorithms and Complexity at Utrecht University's Faculty of Science, Department of Information and Computing Sciences. He holds a Ph.D. in Mathematics from Utrecht University (1985) and has held academic positions since 1983, including roles at Eindhoven University of Technology. His research focuses on algorithms, computational complexity, graph theory, and parameterized complexity, with a particular emphasis on treewidth and network algorithms. He has been recognized with the EATCS-IPEC Nerode Prize (2014) and has contributed to over 500 publications. His work includes developing algorithms for graph decomposition, scheduling, and NP-hard problem analysis. He has organized international workshops and served on editorial boards, including the Journal of Discrete Algorithms. Education: B.Sc. Mathematics, Utrecht University (1981) M.Sc. Mathematics, Utrecht University (1983) Ph.D. in Distributed Computing (1985), supervised by Jan van Leeuwen Research Interests: Algorithms, complexity theory, parameterized complexity, treewidth, graph algorithms, network optimization, and computational problem-solving. His work bridges theoretical foundations with practical algorithm design, emphasizing efficient algorithms for NP-hard problems. Recent Article Trends: Focus on parameterized complexity, treewidth applications, scheduling algorithms, and algorithmic lower bounds. Recent work explores fixed-parameter tractability, graph decompositions, and hardness results for problems on structured graphs. Awards: EATCS-IPEC Nerode Prize (2014) for contributions to parameterized complexity. Grants/Advising: Extensive involvement in grant-funded research projects and academic leadership roles. Supervised numerous PhD and Master's students (details not fully listed here). Labs/Teams: Active in the Algorithms and Complexity research group at Utrecht University, collaborating on theoretical computer science and algorithmic challenges.

• Algorithms and Complexity
• Discrete Mathematics
• Structural Graph Theory
• +2 more

Dr. Igor Razgon serves as an Associate Professor in the Department of Computer Science at Durham University, where he conducts foundational research at the intersection of discrete mathematics and theoretical computer science. His work bridges abstract graph theory with practical applications in artificial intelligence systems and database optimization. His research program centers on structural graph theory, algorithms and complexity, with particular emphasis on tree-width parameters, hypertree decompositions, and constraint satisfaction frameworks. This work establishes critical connections between graph structural properties and computational tractability boundaries, directly impacting query optimization in database management systems and knowledge representation in AI. Analysis of his 2021-2025 publications reveals consistent focus on dichotomy theorems, fractional decomposition methods, and width parameters for hypergraphs. His research demonstrates how structural graph properties determine computational complexity thresholds, with direct applications to database query optimization and constraint-based reasoning systems. Professional recognition includes: Nerode Prize (2020) for contributions to computational complexity theory While his publications indicate significant collaborative work with leading researchers in discrete mathematics, the available information does not specify doctoral advisees, grant funding sources, or laboratory affiliations. His research trajectory suggests ongoing investigation into structural parameters for combinatorial problem solving.

Pablo ParriloView profile

Professor

• Optimization
• Game Theory
• Convex Algebraic Geometry
• +2 more

Pablo A. Parrilo is the Joseph F. and Nancy P. Keithley Professor of Electrical Engineering at the Massachusetts Institute of Technology, affiliated with the Department of Electrical Engineering and Computer Science and the Laboratory for Information and Decision Systems. His research bridges optimization, control theory, and algebraic geometry, focusing on convex optimization, semidefinite programming, and computational methods. Dr. Parrilo develops theoretical foundations and computational tools like SOSTOOLS for solving polynomial optimization problems and provides key insights into system robustness and control synthesis. Research Interests: Dr. Parrilo's work centers on algebraic techniques for optimization and control, including sum-of-squares decompositions, game theory, and convex algebraic geometry. He explores applications in machine learning, autonomous systems, and combinatorial optimization. Publications Focus: His recent publications demonstrate advances in stochastic optimization, geometric algorithms, and non-convex optimization, with consistent themes in efficient computation and theoretical guarantees.

• Neurosymbolic AI
• Artificial Intelligence
• AI Ethics
• +3 more

Prof. Luis C. Lamb is a Professor at the Institute of Informatics (Universidade Federal do Rio Grande do Sul, UFRGS), specializing in Foundations of Computer Science and Artificial Intelligence. His research focuses on Neurosymbolic AI, AI Ethics, and Technology Strategy. He holds a DIC and PhD. Research Interests: Integrated Machine Learning & Reasoning, AI Ethics, Social Computing, and Innovation Strategy. Notable work includes Neurosymbolic AI frameworks and studies on collective intelligence in problem-solving. Recent contributions include talks at AAAI, IBM Neuro-Symbolic AI workshops, and the Montreal AI Debate. His work bridges statistical and symbolic AI approaches, emphasizing ethical considerations and hybrid models.

Dalila FontesView profile

Professor

• Operational Research
• Artificial Intelligence
• Logistics and Transportation
• +3 more

Dalila Fontes is a Full Professor at the Faculty of Economics of the University of Porto (FEP) and a board member of LIAAD (Laboratório de Inteligência Artificial e de Apoio à Decisão), a unit of INESC TEC since 2007. She holds a Ph.D. in Management Science from Imperial College London (2000), an MSc in Operational Research from the London School of Economics (1994), and a 5-year degree in Electrical Engineering and Computer Science from the University of Porto (1993). She has served as a Visiting Professor at the University of Florida (2007/08) and Texas A&M University (2015–16). Her research focuses on applying Operational Research and Artificial Intelligence techniques to solve complex management problems in production, logistics, transportation, and services, emphasizing combinatorial optimization. She has authored over 60 publications and coordinates research projects in these areas. She is an Associate Editor for the Journal of Combinatorial Optimization and Operations Research Forum (Springer). At FEP, she teaches Operational Research, Logistics, and Optimization courses in English to undergraduates, masters, and Ph.D. students. She has held administrative roles including Vice-Dean at FEP and currently directs ISFEP (FEP’s Research and Services Institute). Her work bridges academia and industry, addressing sustainable production systems and energy-efficient scheduling strategies.

Daniel PritikinView profile

Professor

• Graph Theory
• Combinatorics
• Recreational Mathematics
• +3 more

Professor Daniel Pritikin is an academic in the Department of Mathematics at Miami University. He holds a Ph.D. in Mathematics from the University of Wisconsin. His research focuses on Graph Theory, Combinatorics, and Recreational Mathematics, with notable contributions to Hamiltonian cycles in multipartite graphs, Steiner tree problems in hypercubes, and combinatorial game theory. His work bridges theoretical graph structures with algorithmic applications and playful mathematical explorations. Key research themes include graph embeddings, combinatorial optimization, and algebraic graph theory, often intersecting with geometric and number-theoretic concepts. Recent studies explore hypercube networks, Cayley graph representations, and the interplay between graph parameters like separation numbers and domination. His articles frequently address structural properties of graphs and their applications in network design and puzzle-solving. While no specific scientific awards are listed, his prolific publication record since 1985 underscores sustained academic engagement. His advising record is not detailed here, but his research spans over three decades, including foundational work on unitary Cayley graphs and Steiner tree optimizations.

• Theoretical Computer Science
• Proof Complexity
• Automated Reasoning
• +3 more

Leroy Nicholas Chew is a PostDoc Researcher and FWF Projektassistent at the Vienna University of Technology (TU Wien). He is affiliated with the Department of Algorithms and Complexity within the Faculty of Informatics. His roles include contributing to research projects such as QBFPC (2022–2025), Overcoming Intractability in the Knowledge Compilation Map, and REVEAL-AI (2020–2024). These projects reflect his focus on advancing theoretical computer science and automated reasoning methodologies. While specific educational details are not explicitly provided in the text, Leroy Nicholas Chew holds a PhD, as indicated by his role listing. His current position suggests a strong background in computer science and theoretical foundations, consistent with his research activities. His research interests span several key areas in theoretical computer science, including proof complexity, quantified Boolean formulas (QBF), automated reasoning, and knowledge compilation. He explores the hardness of computational problems in logical frameworks, such as analyzing resolution and CDCL proof systems, developing optimal dual proof systems for answer set programming (ASP), and investigating model counting techniques. His work often bridges foundational theory with practical applications in formal verification and algorithm design. Recent publications (2024) highlight advancements in circuits and proofs, model counting, and ASP-QRAT proof systems. Earlier work (2016–2022) addressed QBF resolution calculi, dependency schemes, and certification challenges. These trends underscore his specialization in formal methods and computational logic. No scientific awards are explicitly mentioned in the provided text. In addition to his research, Chew is involved in multiple funded projects. These include the FWF-supported QBFPC (2022–2025), which examines QBF proofs and certificates, and the REVEAL-AI project (2020–2024), focusing on overcoming intractability in knowledge compilation. While specific grant details beyond project funding are not mentioned, his participation underscores his role in collaborative, grant-funded research initiatives. No formal advisees are listed. Chew is part of the Algorithms and Complexity department at TU Wien, collaborating on projects that emphasize proof systems, formal verification, and algorithmic foundations. His work integrates theoretical insights with practical computational methods.

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

Alexander Dobler is a PreDoc Researcher at the Algorithms and Complexity Department of Technische Universität Wien. His research focuses on algorithmic visualization, graph drawing, and combinatorial optimization. He contributes to optimizing layout algorithms for diagrams, treemaps, and storylines, with a particular emphasis on minimizing crossings and corners in geometric representations. Key projects include 'Engineering Linear Ordering Algorithms' (2020–2025) and participation in the PACE competition with solver 'Touiouidth'. His work bridges theoretical computer science with practical visualization challenges, addressing both discrete mathematics and computational geometry. Education: BSc, Dipl.-Ing. (engineering degree) Labs/Teams: Part of the Algorithms and Complexity group at TU Wien Grants: Involved in HumAlgo (2018–2023) and REVEAL-AI (2020–2024) His recent publications (2023–2025) address topics like cluster vertex splitting complexity, hoop diagrams for set visualization, and optimizing linear diagrams. He has supervised Marcel Holzmüller’s 2025 diploma thesis on expanding planar storyplan problems.

Yingying WuView profile

Assistant Professor

• Differential Geometry
• Supersymmetry
• Computational Geometry
• +11 more

Yingying Wu is an Assistant Professor in the Department of Mathematics at the University of Houston. She serves as the Head Coach for Harvard University's International Collegiate Programming Contest (ICPC) teams and a trainer for the ICPC North America Championship Programming Camp. Her research focuses on differential geometry, supersymmetry, computational geometry, and machine learning applications in geometric analysis and biomedical problems. She has advised Ph.D. student Haotian Zuo, who specializes in derived categories and moduli spaces of vector bundles. Her academic contributions span theoretical mathematics and applied machine learning, with notable work on Laplace-Beltrami operators, Z/2 harmonic spinors, and protein structure analysis. She has served as a judge for multiple regional programming contests and developed training materials for competitive programming education. Her research bridges geometric analysis with computational techniques, addressing challenges in medical imaging, 3D shape recognition, and phylogenetic modeling. Key areas of expertise include eigenvalue problems in differential geometry, moduli spaces of curves with supersymmetric structures, and AI-driven approaches to geometric feature representation. Her programming contest leadership reflects expertise in algorithm design and competitive problem-solving strategies.

• Combinatorial optimization
• Network flow problems
• Efficiently solvable cases of NP-hard optimization problems
• +2 more

Bettina Klinz is an Associate Professor at the Department of Mathematics, Technische Universität Graz (TU Graz), Austria, since March 2000. She holds a Diplom-Ingenieur (M.Sc.) in Technical Mathematics and a Dr. techn. (Ph.D.) from TU Graz, completed in 1989 and 1993 respectively. She obtained her Habilitation in Applied Mathematics in 1999, focusing on well-solvable classes of hard combinatorial optimization problems. Her research interests include combinatorial optimization, network flow problems, graph algorithms, and the design of efficient algorithms. She has supervised numerous diploma/master theses and PhD students, covering topics such as hospital layout optimization, nurse scheduling, and second shortest path problems. Teaching responsibilities include courses like Combinatorial Optimization 1 and 2, and she maintains active course materials. She emphasizes practical skills through problem-solving exercises and exams. Her work is also reflected in her contributions to online resources and academic link lists on mathematical programming and combinatorial optimization.