Kitty Meeks is a Reader in the School of Computing Science at the University of Glasgow, having transitioned from the School of Mathematics and Statistics in 2016. Her research focuses on algorithm design, particularly parameterized and counting complexity, with applications in graph theory and precision medicine. She holds an EPSRC Fellowship (2021–2026) and previously a Royal Society of Edinburgh Personal Research Fellowship (2016–2021). Education: MMath in Mathematics and Computer Science (Oxford, 2009), DPhil in Mathematics (Oxford, 2013). Postdoctoral research at Queen Mary University of London (2012–2014). Research: Explores efficient algorithms for network analysis, including structural properties of real datasets and applications in precision medicine. Current projects address combinatorial optimization for multiple solution spaces. Awards: Royal Society of Edinburgh Fellowship, EPSRC Fellowship. Grants: EPSRC projects on multilayer algorithmics and precision medicine, plus Scottish Crucible Seed Funding. Students: Supervises Peace Ayegba (Student-Project Allocation Problems) and Laura Larios-Jones (Temporal Graph Algorithms).
André Nichterlein is a Permanent Research Associate at the Technical University of Berlin, specializing in Algorithmics and Complexity Theory. He completed his PhD at TU Berlin (2014) and holds a Diploma from Friedrich Schiller University Jena (2010). His career includes postdoctoral research at Durham University (UK) under a DAAD fellowship and extensive work as a research assistant at TU Berlin. Research Focus: Nichterlein's work centers on parameterized algorithms , kernelization techniques , graph problem optimization , and algorithm engineering . His research addresses fundamental challenges in computational complexity through practical algorithmic solutions, particularly in graph theory and network optimization. Publication Trends: His recent articles (2020-2023) demonstrate a strong focus on parameterized complexity frontiers, efficient data reduction methods for NP-hard problems, and applications in network design. Recurring themes include kernelization innovations, graph modification problems, and experimental algorithmics, with consistent contributions to theoretical foundations of computer science.
Krzysztof Onak is the Shibulal Family Career Development Assistant Professor in the Faculty of Computing & Data Sciences at Boston University. His research focuses on theoretical foundations of algorithms for big data, including applications in AI, parallel/distributed systems, and sublinear-time algorithms. He holds a PhD from MIT (2010) and a master's from the University of Warsaw (2005). Before academia, he worked at IBM T.J. Watson Research Center and was a Simons Postdoctoral Fellow at CMU. Education PhD in Computer Science, Massachusetts Institute of Technology (2010) Master of Science in Computer Science, University of Warsaw (2005) Bachelor of Science in Mathematics and Computer Science, University of Warsaw (2003-2004) Research Interests Dr. Onak specializes in algorithms for modern computational challenges, including: Sublinear-time algorithms and property testing Streaming and sketching techniques Parallel and distributed algorithms Graph algorithms and optimization Applications to machine learning and data science Recent Work His publications emphasize scalable solutions for large-scale data problems, with contributions to: Efficient parallel graph algorithms (STOC 2020, STOC 2018) Streaming algorithms for dynamic graphs (SODA 2019) Fair clustering methods (ICML 2019) Awards & Recognition Simons Postdoctoral Fellowship (2010-2012) World Champion in ACM ICPC (2003) Gold Medal in International Olympiad in Informatics (2000) Teaching & Service He teaches graduate and undergraduate courses on algorithms for data science and programming. Currently advises 4 PhD students. Active in organizing theory workshops (e.g., WOLA 2022, STOC 2023 PC member). Former research roles include IBM Research Staff Member (2012-2020) and visiting positions at Microsoft Research and Google Research.
Michael Saks is a Distinguished Professor of Mathematics at Rutgers, The State University of New Jersey . He is affiliated with both the Department of Mathematics and the Computer Science Department as a graduate faculty member. His research focuses on theory of computation and discrete algorithms , with applications in computational complexity, combinatorics, and algorithmic analysis. His recent publications include work on edit distance approximations , randomized algorithms , discrepancy of random matrices , and Boolean function analysis , reflecting a strong emphasis on theoretical foundations. He has contributed to journals such as Combinatorica , Journal of Graph Theory , and Discrete Applied Mathematics , and served on editorial boards and conference program committees, including the 2014 IEEE Conference on Computational Complexity . Michael Saks maintains active research and teaching pages , including guidance for graduate applicants and links to seminars like the Discrete Mathematics/Theory of Computing Seminar . He also participates in initiatives such as the Center for Computational Intractability and DIMACS .
Daniel Boley is a Professor and Distinguished University Teaching Professor at the University of Minnesota, within the College of Science and Engineering, Department of Computer Science and Engineering. He serves as the Director of Graduate Studies for the Graduate Program in Data Science, which offers a Master's of Science and a Post-Baccalaureate Certificate. His office is located in Kenneth H. Keller Hall at 4-225C. Professor Boley's research spans computational methods in linear algebra, scalable data mining algorithms, and applications in systems biology and bioinformatics. His work focuses on scalable algorithms for convex optimization in machine learning, analysis of networks and graphs from metabolic biochemical networks, and wireless device networks. He has made significant contributions to numerical linear algebra methods for control problems, parallel algorithms, and iterative methods for matrix eigenproblems. His research interests also include algebraic models in systems and evolutionary biology, and biochemical metabolic networks. His recent publications demonstrate a strong focus on applying graph theory and network analysis to diverse domains including robot swarms, medical imaging (particularly for glioblastoma and COVID-19 diagnosis), and metabolic network analysis. His work bridges theoretical computer science with practical applications in biology and medicine, with a consistent emphasis on developing scalable computational methods. The trend shows increasing interdisciplinary collaboration, particularly with medical researchers. Distinguished Member by the ACM Top university award for post baccalaureate, graduate and professional education Distinguished University Teaching Professor title Professor Boley has advised numerous PhD students including Tatiana Lenskaia (2021), Shaozhe Tao (2018), Ham Ching Lam (2014), and others dating back to 1994. His research has been supported by various grants enabling work on scalable computation of elementary pathways through metabolic networks, Markov models of viral evolution, and scalable data mining algorithms for text analysis. He has developed software tools for clustering, dot plot visualization, and educational graphics. Professor Boley directs the Graduate Program in Data Science and has been involved in projects such as the Principal Direction Divisive Partitioning (PDDP) Project. His research group develops practical implementations of theoretical advances, including the PDDP clustering algorithm, Dot.py genome viewer, and various educational graphics tools for introductory programming courses. He maintains active collaborations across disciplines, particularly in bioinformatics and medical imaging applications.
Dr. Or Brook is an Associate Professor of Competition Law and Policy at the School of Law, University of Leeds, and Deputy Director of the Centre for Business Law and Practice. They hold interdisciplinary qualifications, including a PhD from the Amsterdam Centre for European Law and Governance, an LLM in European Competition Law from the University of Amsterdam, and degrees in Law and Economics from the Hebrew University of Jerusalem. Research focuses on international EU competition law, antitrust, and empirical legal methods. Notable projects include the Priority Setting Project (funded by ESRC) and a comprehensive study of judicial review in EU competition law. They co-edit the Research Handbook on Abuse of Dominance and lead the UK branch of ASCOLA, contributing to policy discussions through international collaborations. Education: PhD in Law, University of Amsterdam LLM in European Competition Law, University of Amsterdam LLB and BSc in Economics, Hebrew University of Jerusalem Research interests emphasize empirical analyses of legal texts, institutional enforcement practices, and competition law’s intersections with other regulatory frameworks. Recent monograph Non-Competition Interests in EU Antitrust Law (Cambridge UP, 2022) presents novel empirical data on public policy influences in EU competition enforcement. Key awards include the Michael Beverley Innovation Fellowship (associated member), supporting entrepreneurial academic initiatives. Their work bridges law and economics through interdisciplinary methods, advocating for empirical rigor in European legal scholarship.
Luca Fabrizio Di Cerbo is an Associate Professor of Mathematics at the University of Florida, affiliated with the College of Liberal Arts and Sciences. His research focuses on Differential and Algebraic Geometry, Geometric Topology, and related areas. He earned his Ph.D. in Mathematics from Stony Brook University in 2011, advised by Prof. Claude LeBrun. His work explores topics such as Einstein 4-manifolds, aspherical manifolds, the Singer conjecture, and geometric structures on complex surfaces. Recent contributions include studies on curvature properties, harmonic forms, and L2-invariants. His research has been supported by the National Science Foundation, Simons Foundation, and Marie Curie Fellowship programs. Key trends in his publications include investigations into manifold topology, geometric analysis, and algebraic geometry, with a focus on rigidity theorems, Betti numbers, and geometric convergence. He has also co-edited a volume on geometry and topology of aspherical manifolds (Contemporary Mathematics, 2025). Scientific Awards: Simons Postdoctoral Fellowship (2011-2013), Renaissance Technologies Fellowship, Marie Curie Fellowship Teaching & Mentorship: Supervised graduate research in geometric analysis and differential geometry, taught advanced courses including Differential Geometry I/II, Complex Variables, and Geometric Analysis. Grants: NSF-funded research on topology, geometry, and geometric analysis. He actively participates in academic outreach, delivering lectures at institutions worldwide and organizing conferences such as the 2023 NSF-supported Singer-Hopf Conjecture in Geometry and Topology special session. His work bridges pure mathematics with geometric analysis, emphasizing interdisciplinary connections.
Sara Grundel is a leading researcher at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany. Her work focuses on computational methods in systems and control theory, particularly in model order reduction, gas network simulation, and optimization of energy systems. Education: Diplom in Mathematics, ETH Zurich (2005) PhD in Mathematics, Courant Institute of Mathematical Sciences, New York University (2011) Research Interests: Sara’s research encompasses mathematical control theory, stability analysis, and numerical methods for differential-algebraic equations. She applies these techniques to gas and energy networks, epidemic modeling, and multi-agent systems. Her interdisciplinary work bridges computational mathematics with real-world engineering and public health challenges. Recent Publications: Her 15 most recent articles (2024–2012) demonstrate expertise in parametrized PDEs, model reduction for coupled systems, and control strategies for SARS-CoV-2 containment. Key subtopics include adaptive meshing, stability-preserving algorithms, and optimization of nonlinear network dynamics. Scientific Contributions: Developed clustering-based model reduction techniques for networked systems Investigated hyperbolic discretization methods using Riemann invariants Advanced polynomial root radius optimization with affine constraints Collaborations: Sara frequently collaborates with researchers like Peter Benner and Martin Gersen on energy grid simulations and control theory. She participates in international conferences (GAMM, IEEE CDC, MTNS) and contributes to edited volumes in applied mathematics.
Alexandre Vigny is a junior professor at University Clermont Auvergne, France, where he conducts research at the intersection of logic, algorithms, and graph theory. His work focuses on theoretical computer science, particularly in model checking, query enumeration, and distributed algorithms on sparse graph classes. Research Interests: Theoretical Computer Science Logic in Computer Science Parameterized and Distributed Algorithms Graph Theory and Structural Sparsity Database Query Evaluation Reconfiguration Problems His recent publications explore algorithmic meta-theorems, first-order logic with connectivity, elimination distance, and distributed domination, primarily on sparse and structurally constrained graphs. His work appears in top venues such as LICS, ICALP, PODS, and JACM. Scientific Awards: No awards explicitly mentioned. Advising and Grants: Co-supervising Jona Dirks, PhD student since October 2024, with Mamadou Kanté. No specific grants mentioned, but active in collaborative research with prominent figures like Sebastian Siebertz, Luc Segoufin, and Patrice Ossona de Mendez. Labs and Teams: Previously part of Sebastian Siebertz’s team at the University of Bremen. Collaborates with researchers across Europe, including in Warsaw and Paris. Involved in the theoretical computer science community, co-organizing the PODC-DARE workshop.
Brendan Pass is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. His research focuses on optimal transportation theory, with applications in mathematical economics, mathematical physics, and density functional theory. He is a core member of the Kantorovich Initiative, an interdisciplinary group dedicated to advancing optimal transport methodologies and their practical implementations. His work bridges pure mathematics with applied fields, addressing complex problems in multi-marginal transport, economic equilibrium modeling, and geometric analysis. Pass’s academic contributions span theoretical frameworks for optimal transport between spaces of differing dimensions, cyclic cost structures, and robust risk management. He explores intersections with diverse disciplines, including econometrics, financial mathematics, and geometric probability. His publications often address uniqueness conditions for Monge solutions, Wasserstein metrics, and barycenter problems in metric spaces. He has co-authored works on topics ranging from hedonic pricing models to vectorial martingale transport. Recent research trends include leveraging optimal transport for inequality measurement, robust optimization, and data denoising. His methodologies emphasize structural analysis and algorithmic design, with applications in crop root systems, quantum chemistry (via density functional theory), and option pricing. Pass frequently collaborates with institutions globally, contributing to both theoretical advancements and real-world problem-solving. Teaching responsibilities include advanced mathematics courses such as Math 156 (Calculus for Business/Economics) and Math 300 (Boundary Value Problems). His scholarly output includes over 50 peer-reviewed articles, conference contributions, and edited volumes. Current projects explore ODE characterizations of transport problems, distributional robustness, and partial identification in econometric models.
Petr Golovach is a Research Professor at the Department of Informatics, University of Bergen. His research focuses on Discrete Mathematics and Theoretical Computer Science, particularly graph theory, algorithms, parameterized complexity, and clustering. He has held academic positions at Syktyvkar State University (1991–2007), Durham University (2009–2011), and currently teaches advanced courses like Advanced Algorithms Techniques and Enumeration Algorithms at the University of Bergen. His research interests include graph algorithms, parameterized complexity, matroid theory, and algorithmic enumeration. He has organized notable events like the Dagstuhl Seminar 2018 and serves on program committees for STACS, IPEC, and SWAT. Notable contributions include work on hybrid clustering algorithms, graph cuts under matroid constraints, and parameterized tractability of path and cycle problems. He has supervised PhD students like Nidhi Purohit and master’s students including Øyving Stette Haarberg and Andreas Steinvik. His publications span over 200 peer-reviewed papers in top venues such as Journal of the ACM and conferences like SODA and ICALP. His research bridges extremal combinatorics with algorithm design, emphasizing practical and theoretical advancements in graph-based problems.
Andrew McGregor is a Professor in the Department of Computer Science at the University of Massachusetts Amherst, affiliated with the Manning College of Information and Computer Sciences. He is a core member of the Theory Group and directs the TRIPODS Institute for Theoretical Foundations of Data Science. His research focuses on algorithms for massive data sets, data streams, and information theory, with applications in clustering, approximation algorithms, and coding theory. Education: PhD, Computer Science, University of Pennsylvania (2007) MEng, Computer Science, University of Pennsylvania (2002) BA, Mathematics, University of Cambridge (2000) Research Interests: Design and analysis of algorithms for data streams and massive datasets Clustering algorithms and approximation techniques Coding theory and information-theoretic approaches Algorithmic fairness and combinatorial optimization Recent Article Trends: Advances in streaming algorithms for dynamic graphs and parameterized complexity Graph reconstruction and allocation problems under noisy conditions Quantum communication and scheduling policies for satellite networks Entropy estimation and probabilistic methods in data analysis Awards: NSF CAREER Award (2010) ACM PODS Test-of-Time Award (2020) Lilly Teaching Fellowship (2012) Outstanding Teacher Award (2016) Grants & Labs: Director, TRIPODS Institute for Theoretical Foundations of Data Science Collaborations with Microsoft Research and UCSD's Information Theory & Applications Center Active in Center for Data Science and Theoretical Computer Science Group Future Work: Expanding research on quantum algorithms, scalable causal inference, and fair data diversification techniques.
Yangming Li is a Research Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge, affiliated with the Cambridge Image Analysis research group. His work bridges applied mathematics, theoretical physics, and machine learning, with a focus on developing innovative models for image analysis, generative processes, and natural language understanding. Research interests include Fourier Neural Operators, diffusion models, generative adversarial networks (GANs), and their applications in solving complex problems across scientific computing and data-driven domains. His recent contributions explore operator learning for PDEs, robust diffusion models under noisy conditions, and adversarial attacks in text watermarking systems. Publications highlight advancements in operator-based neural networks, risk-sensitive generative modeling, and domain-aware NLP frameworks. His methodologies emphasize mathematical rigor while addressing practical challenges like missing data and model expressivity limitations. Active collaborations span interdisciplinary teams at DAMTP and the broader University of Cambridge research community.
Dr. Kenneth W. Regan is a Professor in the Department of Computer Science and Engineering at the University at Buffalo, The State University of New York, where he is affiliated with the School of Engineering and Applied Sciences. He earned his BS in Mathematics from Princeton University in 1981 and his PhD in Mathematics from Oxford University in 1986. Dr. Regan's research spans theoretical computer science, mathematical logic, and computational complexity with a distinctive focus on chess analysis. His work bridges abstract theoretical concepts with practical applications, particularly through his "Fidelity" Chess Research project. He maintains the influential research blog "Gödel's Lost Letter and P=NP" which explores fundamental questions in theoretical computer science. His recent publications demonstrate a consistent focus on quantum computing algorithms, mathematical logic, and chess-based cognitive modeling, with significant contributions to quantum circuit simulation, complexity theory, and methods for analyzing human-computer differences in decision-making. His work has been featured in Chess Life, the New York Times, and NPR Weekend Edition. Developed innovative methods for analyzing chess games to measure cognitive tendencies Created mathematical frameworks for understanding human vs. computer decision-making patterns Contributed to quantum circuit complexity and simulation techniques As an educator, Dr. Regan teaches courses ranging from foundational theoretical computer science (CSE396) to specialized topics in quantum computing (CSE439/510) and cognitive analysis of chess data (CSE702). His teaching materials are known for their mathematical rigor and practical applications, with extensive lecture notes and resources developed over many years of instruction.
Jiehua Chen is an Associate Professor in the Department of Algorithms and Complexity at TU Wien (Vienna University of Technology), part of the School of Informatics. Her research focuses on algorithmic social choice, fair division, and computational complexity with applications to multi-agent systems, stable matchings, and parameterized algorithms. She leads the project Structural and Algorithmic Aspects of Preference-based Problems in Social Choice (2019–2027), funded by the Vienna Science and Technology Fund (WWTF). Her work spans theoretical contributions to voting systems, fair allocation mechanisms, and graph-based problems, with notable publications in venues like AAAI, IJCAI, and ACM Transactions on Economics and Computation. She teaches courses such as Algorithmic Social Choice and Quantum Computing and Complexity Theory , and has supervised research like E. Ceylan's diploma thesis on optimal seat arrangement algorithms. Chen’s research emphasizes practical algorithm design for social choice challenges, including refugee resettlement, participatory budgeting, and hedonic games. She frequently presents at international conferences and collaborates on interdisciplinary projects involving computational complexity and graph theory.
