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Ashley M R Montanaro is a Professor of Quantum Computation at the School of Mathematics, University of Bristol . Active in quantum computing research since at least 2014, they lead projects at the intersection of quantum algorithms , computational complexity , and quantum information theory , affiliated with the Bristol Quantum Information Institute. Research interests focus on quantum algorithm design , computational complexity analysis , and quantum simulation . Key work includes developing variational quantum algorithms for phase transition detection, Hamiltonian simulation techniques, and quantum-classical hybrid methods for solving complex problems in physics and optimization. Recent publications demonstrate expertise in: Quantum phase diagram simulation with low-depth circuits Quantum speedups for constraint satisfaction problems Quantum communication complexity of machine learning tasks Quantum-enhanced optimization heuristics Hamiltonian simulation with time-dependent product formulas Quantum algorithm complexity analysis Scientific awards include: EPSRC Fellowship (2014-2019) - "New insights in quantum algorithms and complexity" Active in quantum software development through projects like: "Quantum Algorithms from Foundations to Applications" (ERC-2018-COG) "Quantum Computing and Simulation Hub" (2019-2024) "Prosperity Partnership in Quantum Software" (2019-2023)

• Quantum Computation
• Quantum Algorithms
• Computational Complexity
• +3 more

Nguyen Dang is a Lecturer at the School of Computer Science, University of St Andrews, actively supervising PhD students and teaching AI-related modules including Artificial Intelligence (CS3105), Artificial Intelligence Practice (CS5011), Machine Learning (CS5014), and Uncertainty in Artificial Intelligence (CS5016). He leads the Centre for Interdisciplinary Research in Computational Algebra and maintains an active research profile with numerous publications in top conferences. University of St Andrews, School of Computer Science Lecturer (equivalent to assistant professor) Supervising PhD students including Tai Nguyen Teaching multiple AI and Machine Learning courses Dr. Dang's research focuses on the intersection of machine learning and optimization, particularly automated algorithm configuration and design. His work centers on leveraging machine learning techniques to automate the development of optimization algorithms, with special emphasis on deep reinforcement learning for Dynamic Algorithm Configuration and integrating machine learning into constraint programming. His research has significant applications across various domains, especially in automated constraint modeling. The publications reflect strong activity in combinatorial optimization, algorithm selection, and benchmark instance generation. His recent publications demonstrate consistent output in top venues including Artificial Intelligence Journal, GECCO, FOGA, and CP conferences, with notable achievements including Best Paper Awards at GECCO'2025 and GECCO'2022. The research spans theoretical foundations of parameter control, practical applications in constraint programming, and innovative approaches to algorithm configuration. Best paper award at GECCO'2025 Best paper award at GECCO'2022 Nomination for best paper award at FOGA'2023 Best paper award at GECCO'2017 Dr. Dang holds a Leverhulme Early Career Fellowship (2020-2023) worth £90,000 for his project on constraint-based automated generation of synthetic benchmark instances. He has secured additional funding including EPSRC High Performance Computing grants totaling over 2.2 million CPU hours and a COST Action grant. His research group actively develops tools and frameworks for automated algorithm configuration and benchmark instance generation, with several open-source datasets available on GitHub. He is involved with multiple research groups including the Centre for Interdisciplinary Research in Computational Algebra and collaborates extensively with researchers at University of St Andrews and internationally, including at Université de Paris I Panthéon-Sorbonne where he conducted visiting research.

• combinatorial optimisation
• algorithms
• machine learning
• +5 more

Standa Živný is a Professor of Computer Science at the University of Oxford and a Fellow and Tutor at Merton College. He has been a faculty member at Oxford since 2013 and was promoted to full professor in 2021. His research spans theoretical computer science and discrete mathematics, with a focus on algorithms, computational complexity, and constraint satisfaction problems (CSPs) in various forms, including optimisation, counting, and approximation. His research interests include the power and limitations of convex relaxations, sparsification, submodularity, and the algebraic and logical foundations of tractability in combinatorial problems. He has made significant contributions to understanding when and why certain problems can or cannot be efficiently solved using linear programming and other algorithmic paradigms. The recent trends in his publications show a deep engagement with approximation algorithms, hardness results, sparsification techniques, and the complexity of counting and promise problems. His work often lies at the intersection of algebra, logic, and optimisation, demonstrating the power of interdisciplinary approaches in theoretical computer science. ERC Consolidator Grant (NAASP, 2022–2027) ERC Starting Grant (PowAlgDO, 2017–2022) Royal Society University Research Fellowship (2013–2021) He actively supervises a large cohort of postdoctoral researchers and students, including PhD candidates, master’s, and undergraduate students. His leadership extends to academic service, where he serves as Editor-in-Chief of the SIAM Journal on Discrete Mathematics and holds editorial and committee positions in major journals and funding bodies. He has organised numerous workshops and research programmes at institutions such as Dagstuhl, the Isaac Newton Institute, and AIM. He is involved in major research initiatives, including a Simons Programme on symmetry in computation and an American Institute of Mathematics SQuARE on relaxations for promise CSPs.

• Theoretical Computer Science
• Discrete Mathematics
• Algorithms
• +10 more

Daniel Paulusma is a Professor in the Department of Computer Science at Durham University, where he serves as Head of the Algorithms and Complexity research group (ACiD). He obtained his PhD and Master's degrees from the University of Twente in 2001 and 1997 respectively, and has been at Durham University since 2004, progressing from Lecturer (2004-2011) to Senior Lecturer (2011-2013), Reader (2013-2015), and finally to Professor in 2015. He previously served as Director of Research for the School of Engineering and Computing Sciences from 2014 to 2017. Paulusma's research spans two main areas: structural and algorithmic graph theory, with a focus on computational complexity of graph problems under input restrictions; and cooperative game theory, particularly matching games related to kidney exchange. His work has resulted in numerous survey papers and significant contributions to the field. He has received several prestigious awards including the CIAC 2025 Best Paper Award, ISAAC 2024 and 2023 Best Paper Awards, and the Glover-Klingman Prize for his work on graph contractions. His recent publications demonstrate a strong focus on complexity frameworks for forbidden subgraphs, matching cuts, graph coloring, and kidney exchange algorithms. The research shows a consistent pattern of exploring computational complexity within specific graph classes, particularly H-free graphs, and developing frameworks to classify problems based on graph structure constraints. CIAC 2025 Best Paper Award for "Atoms versus avoiding simplicial vertices" ISAAC 2024 Best Paper Award for "Complexity framework for forbidden subgraphs II: Edge subdivision and the \"H\"-graphs" ISAAC 2023 Best Paper Award for "Matching cuts in graphs of high girth and H-free graphs" Glover-Klingman Prize for "The Computational Complexity of Graph Contractions I, II" Paulusma has successfully supervised numerous PhD students including Yilin Li, Tala Eagling-Vose, Xin Ye, Siani Smith, Giacomo Paesani, Anthony Stewart, Carl Feghali, Jian Song, and Pim van 't Hof. He has secured substantial research funding including grants from the Leverhulme Trust, EPSRC, and Royal Society for projects on graph coloring, kidney exchange algorithms, and network structure analysis. His current projects include "Algorithmic Meta-classifications for Graph Containment" (2025-2028) and "KidneyAlgo: New Algorithms for UK and International Kidney Exchange" (2023-2025). As Head of the Algorithms and Complexity research group (ACiD), Paulusma leads a vibrant research team focused on theoretical computer science problems with applications in network analysis, optimization, and game theory. The group regularly organizes workshops and collaborates with international researchers in the field.

• Structural Graph Theory
• Algorithmic Graph Theory
• Computational Complexity
• +6 more

Evangelia (Eva) Kalyvianaki is a Senior Lecturer (equivalent to Associate Professor) in the Department of Computer Science and Technology at the University of Cambridge , where she is also a member of the Systems Research Group / netos group . Previously she held faculty positions as Lecturer at City University London and as post-doctoral researcher at Imperial College London. Education Ph.D. in Computer Science, Computer Laboratory (SRG/netos group), University of Cambridge M.Sc. in Computer Science, University of Crete, Greece B.Sc. in Computer Science, University of Crete, Greece Research Interests Her research spans the broad areas of Cloud Computing , Big Data Processing , Autonomic Computing , and Distributed Systems . A central theme is the design and management of next-generation, large-scale cloud applications, with an emphasis on applying mathematical reasoning—particularly control-theoretic techniques such as Kalman and H-infinity filtering—to address the complexity and uncertainty inherent in modern distributed infrastructures. Topics of active investigation include adaptive CPU and resource provisioning for virtualized servers, fairness and overload management in federated stream-processing systems, explicit state management for big-data frameworks, and distributed optimization algorithms for large-scale networked systems. Publications & Research Impact Across more than thirty peer-reviewed papers, her work demonstrates a consistent trajectory toward bridging rigorous control theory with practical systems challenges in the cloud. Signature contributions include the THEMIS framework for fair federated stream processing, dynamic block-sizing algorithms for data-stream engines, and robust resource-provisioning schemes based on advanced filtering techniques. Recent publications extend these ideas to fully distributed, finite-time coordination protocols that operate under quantized communications and time-varying delays, reflecting an expanding scope toward large-scale networked control systems. Scientific Awards No specific awards or fellowships are listed in the provided material. Advising & Funding While individual student names are not disclosed, her extensive publication record with numerous co-authors indicates active supervision of doctoral and master’s researchers. Funding acknowledgements in papers suggest support from UK research councils, EU projects, and industrial partnerships, although explicit grant details are not provided. Labs & Teams She is affiliated with the Systems Research Group (netos) within the Cambridge Computer Laboratory, a leading collective focused on networked and operating systems research, providing a collaborative environment for experimental cloud and distributed-systems work.

• Cloud Computing
• Big Data Processing
• Autonomic Computing
• +6 more

Andrei Krokhin is a Professor in the Department of Computer Science at Durham University, UK. His academic roles include being a member of the Algorithms and Complexity Research Group. He holds a PhD in Mathematics from Ural State University (Russia) and has held positions at Warwick University and Oxford University. His research focuses on computational complexity, constraint satisfaction problems (CSP), universal algebra, and combinatorics. Education: PhD in Mathematics, Ural State University, 1990s Research Interests: Professor Krokhin investigates the mathematical and algorithmic foundations of CSP, emphasizing complexity classification and approximation. His work bridges universal algebra, logic, combinatorics, and graph theory. Key themes include algebraic approaches to CSP, constraint optimization, and the interplay between computational complexity and structural mathematics. Awards: EPSRC Advanced Research Fellowship (2006) Principal organizer of the 2006 Oxford Workshop on Mathematics of Constraint Satisfaction Invited plenary speaker at ISMVL 2003 (Tokyo) Invited lectures at NATO ASI Summer School (2003) Advising & Grants: Supervises PhD students (e.g., Yiming Qiu) and leads EPSRC-funded projects like 'Promise Constraint Satisfaction Problems: Structure and Complexity.' He recruits students for research on CSP complexity and approximation. Labs/Teams: Member of the Algorithms and Complexity Research Group at Durham University, collaborating internationally on CSP theory and applications.

• Computational Complexity
• Constraint Satisfaction Problems
• Universal Algebra
• +4 more

Pedro Orvalho is a Research Associate in the Department of Computer Science at the University of Oxford, working with Professor Marta Kwiatkowska on the FUN2MODEL ERC project. His research bridges theoretical computer science with practical applications in software engineering and programming education. His educational background includes: PhD in Computer Science and Engineering (2025) from Instituto Superior Técnico, Universidade de Lisboa MSc in Information Systems and Computer Engineering (2019) from Instituto Superior Técnico BSc in Information Systems and Computer Engineering (2017) from Instituto Superior Técnico Orvalho's research spans Artificial Intelligence, Automated Reasoning, Formal Methods, and Program Repair, with significant contributions to programming education tools. His work integrates formal methods with machine learning techniques to develop novel approaches for program verification and repair, particularly focused on introductory programming assignments. His scientific achievements have been recognized with prestigious awards: Vencer o Adamastor (VoA) - 3rd Edition (2025) ELISE Mobility Grant (2024) COST Travel Grant (2022) Excellence in Teaching IST Awards (2021 and 2024) ACM SIGSOFT Distinguished Paper Award (ESEC/FSE 2021) FCT PhD Scholarship (2020-2024) With five years of teaching experience at Instituto Superior Técnico, Orvalho has developed educational tools like GitSEED and MENTOR that bridge his research with practical classroom applications. His research has been supported by multiple grants including the ERC FUN2MODEL project and FCT PhD Scholarship, demonstrating both academic and practical impact. He maintains active collaborations with researchers from Czech Technical University in Prague, Carnegie Mellon University, and industry partners like OutSystems, contributing to an international research network focused on software reliability and educational technology.

• Artificial Intelligence
• Automated Reasoning
• Formal Methods
• +4 more

Brian Carey is Associate Professor in Political Theory at Durham University's School of Government and International Affairs, holding this position since 2021 after prior roles at Trinity College Dublin, University of Limerick, and University College Cork. His academic credentials include: PhD in Political Theory, University of Manchester (2015) MSc in Human Rights, University College Dublin BA and MA in Philosophy, University College Cork Core research spans political feasibility and public deliberation under non-ideal conditions, critically examining distributive justice , linguistic justice , methodology , and social epistemology . His work consistently bridges theoretical rigor with practical applications in contexts of human fallibility and societal complexity. Recent publications (2024-2025) emphasize methodological innovations in feasibility theory and critiques of echo chamber concepts, while earlier works established foundations in linguistic justice and children's rights. This cohesive trajectory demonstrates sustained engagement with adapting ideal theories to real-world constraints across employment ethics, citizenship, and language policy.

• Political Feasibility
• Public Deliberation
• Distributive Justice
• +6 more

Anna Huber is a Senior Lecturer in Mathematics and Computing at Teesside University's Department of Computing & Games Centre for Sustainable Engineering. She holds a PhD from Saarland University (2010) and completed postdoctoral research at Durham University (2010-2013) on EPSRC-funded submodular optimization projects. Her research focuses on algebraic structures, graph theory, and combinatorial algorithms with applications in discrete optimization. She has published influential work in SIAM journals on bisubmodular functions and valued constraint satisfaction problems. Education: PhD in Mathematics and Informatics, Saarland University & Max Planck Institute (2010) Previously affiliated with University of Derby as Lecturer in Mathematics Research Themes: Explores algebraic methods for solving combinatorial optimization problems, particularly through randomized algorithms and graph-based approaches. Current work emphasizes tractability analysis in constraint satisfaction frameworks and bisubmodularity applications. Grants & Projects: EPSRC Project: 'Submodular Optimization, Lattice Theory and Maximum Constraint Satisfaction Problems' (2010-2013) Labs/Teams: Active in Teesside University's Computing & Games research groups, collaborating on sustainable engineering computing applications.

• Algebra
• Graph Theory
• Randomized Methods
• +3 more

Dr. Jakub Opršal is a Research Fellow at the University of Birmingham specializing in the theoretical foundations of computer science and mathematics. His research focuses on the computational complexity of constraint satisfaction problems (CSPs) and their variants. Previously, he held postdoctoral positions at ISTA (2022-23), Oxford (2021-22), Durham University (2018-21), TU Dresden (2016-18), and Jagiellonian University (2016), establishing a strong international research profile. Dr. Opršal received his PhD from Charles University in Prague in 2016 under the supervision of Libor Barto. His doctoral work laid the foundation for his ongoing contributions to universal algebra and computational complexity theory. Dr. Opršal's research integrates advanced mathematical frameworks including universal algebra , homotopy theory , category theory , and mathematical logic to analyze constraint satisfaction problems. He has made significant contributions to understanding the computational complexity of CSPs, particularly through topological methods and algebraic structures. His work often establishes dichotomy theorems and proves hardness results for various CSP variants, with increasing focus on promise constraint satisfaction problems. Analysis of Dr. Opršal's publication record reveals a sophisticated evolution in methodology, with recent work increasingly incorporating category-theoretic approaches and topological techniques to tackle fundamental questions in computational complexity. His research demonstrates a consistent pattern of bridging abstract mathematical concepts with concrete computational problems, particularly in establishing connections between algebraic structures and computational hardness. Dr. Opršal actively seeks PhD students and has organized significant academic events including the Birmingham CSP Meeting (2024). He serves on program committees for major theoretical computer science conferences and collaborates extensively with researchers across Europe, including prominent institutions like Oxford, Durham, and ISTA. As a key organizer of the Birmingham Constraint Satisfaction research group, Dr. Opršal contributes to a vibrant international research community. He participates in the CSP World Congress series and maintains active collaborations that advance the theoretical foundations of constraint satisfaction problems through interdisciplinary approaches combining computer science, mathematics, and logic.

• Constraint Satisfaction Problems
• Computational Complexity
• Universal Algebra
• +4 more

Dr. Barnaby Martin is an Associate Professor in the Department of Computer Science at Durham University, part of the Algorithms and Complexity Group (ACiD). Previously, he held a lectureship at Middlesex University and undertook postdoctoral roles at Durham and Paris institutions. His research focuses on Complexity Theory, including Proof Complexity and Computational Complexity, as well as Finite Model Theory and links between logic and complexity. Current work emphasizes Constraint Satisfaction Problems (CSP), Proof Complexity, and Algorithmic Graph Theory. His recent publications explore forbidden subgraphs, edge subdivision, Steiner Forest problems, and H-free graphs. Dr. Martin’s articles often address computational challenges in graph theory and algorithm design, with a recurring theme of analyzing tractability and hardness in specific graph classes. He has collaborated extensively with researchers like Daniel Paulusma, Siani Smith, and others on topics ranging from graph coloring to quantified constraint satisfaction problems. He supervises postgraduate students Tala Eagling-Vose and Yiming Qiu. His work has been published in venues such as Algorithmica , SIAM Journal on Computing , and ACM Transactions on Computational Logic . His research bridges theoretical computer science and discrete mathematics, contributing to foundational understanding of computational limits in structured domains.

• Complexity Theory
• Proof Complexity
• Computational Complexity
• +3 more

Dr. Felix Ulrich-Oltean is a Lecturer in the Department of Computer Science at the University of York. His research focuses on constraint satisfaction, combinatorial optimization, boolean satisfiability, and machine learning applied to automated algorithm selection and configuration. He holds a PhD in Computer Science from the University of York (2019–2023), a PGCE in Secondary Mathematics from Leeds Trinity University, and a BSc in Computer Science from the University of York. His recent work emphasizes SAT encoding techniques for pseudo-boolean and linear integer constraints, with contributions to automated tabulation methods in constraint models. Research trends include integrating machine learning for algorithm selection, optimizing constraint satisfaction problem resolution, and developing efficient SAT-based solutions for complex combinatorial problems. No scientific awards or grants are explicitly mentioned in the provided texts. He advises no listed students and has no disclosed lab affiliations. His office is located at CSE/108, and contact is facilitated through a university web form to protect email privacy.

• Constraint Satisfaction
• Combinatorial Optimization
• Boolean Satisfiability
• +3 more

Mark Jerrum is a Professor of Mathematics at Queen Mary University of London, part of the School of Mathematical Sciences. His research focuses on combinatorics, computational complexity, and stochastic processes, particularly in the design and analysis of randomized algorithms. He explores the mixing times of Markov chains and computational complexity of counting problems, including partition functions and generating functions, often motivated by statistical physics, constraint satisfaction, and graph polynomials. Notable grants include an EPSRC-funded project on Sampling in Hereditary Classes (EP/S016694/1, 2019–2023). He has contributed to teaching, serving as module organiser for MTH4213 (Numbers, Sets and Functions) in 2023–24. His work bridges theoretical computer science and discrete mathematics, with applications in algorithmic design and probabilistic analysis. Research highlights include advancements in perfect sampling algorithms, approximation algorithms for counting problems, and foundational work on the interplay between statistical physics models and computational complexity. He is affiliated with the Centre for Combinatorics, Algebra and Number Theory, reflecting his interdisciplinary approach to combinatorial and algorithmic challenges.

• Combinatorics
• Computational Complexity
• Theoretical Computer Science
• +7 more

Hubie Chen is a Senior Lecturer in the Department of Informatics at King's College London, part of the Faculty of Natural, Mathematical & Engineering Sciences. Prior to working in the UK, he was an Ikerbasque Research Professor in Donostia-San Sebastian, Spain. He has held invited positions at institutions such as École Polytechnique, Humboldt-Universität zu Berlin, and Universität Wien. Dr. Chen received his PhD and BS degrees in computer science and mathematics from Cornell University and Stanford University, respectively. His academic career spans multiple prestigious institutions across Europe and the United States, demonstrating his international recognition in theoretical computer science. Dr. Chen's research focuses on the theoretical foundations of computer science, with particular expertise in database theory, logic in computer science, computational complexity, and constraint satisfaction. His work bridges theoretical computer science with practical database applications, exploring the fundamental limits and possibilities of computation and data management. His recent book "Computability and Complexity" published by MIT Press provides a comprehensive introduction to automata theory, computability theory, and complexity theory, including parameterized complexity. Dr. Chen's publication record demonstrates a consistent contribution to top-tier theoretical computer science venues. His recent work shows a strong focus on constraint satisfaction problems, database query theory, and computational complexity. He has made significant contributions to understanding the theoretical foundations of database queries, constraint satisfaction, and computational complexity, with publications in prestigious venues such as Computational Complexity, ACM Transactions on Computation Theory, and proceedings of ICDT, PODS, and LICS. Dr. Chen has received recognition for his work, including the ICDT 2017 Best Paper Award. His research has been supported by the Engineering and Physical Sciences Research Council (EPSRC), with active projects on "Algebraic methods for Quantified Constraints" (2024-2026) and "Query Evaluation" (2023-2026). Within King's College London, Dr. Chen is affiliated with the Software Systems Group and contributes to the broader research ecosystem of the Department of Informatics, which includes hubs for Trusted Autonomous Systems, Finance (FinTech), Algorithms and Data Analysis, and Security.

• Database theory
• Logic in computer science
• Computational complexity
• +3 more

Silvia Butti is a Senior Research Associate at the University of Oxford's Department of Computer Science and holds a Junior Research Fellowship at Lady Margaret Hall. Her research focuses on theoretical computer science, particularly constraint satisfaction problems (CSPs), algorithms, and complexity theory. She collaborates with Standa Živný on projects funded by the UKRI-ERC grant NAASP. She earned a PhD from Universitat Pompeu Fabra, an MSc in Mathematics and Foundations of Computer Science from Oxford, and a BSc from University College London. Her work bridges algebraic methods and computational complexity, exploring topics like promise CSPs, hierarchies (e.g., Sherali-Adams, Weisfeiler-Leman), and approximation algorithms. She has been awarded the INPhINIT fellowship and Marie Skłodowska-Curie COFUND funding. She has taught courses on Combinatorial Optimization, Computational Complexity, and Probability and Computing at Oxford, and Discrete Mathematics at UPF. Butti actively participates in academic outreach, including Maths Fest and Royal Institution Masterclasses, and has presented her research globally at venues like ICALP, LICS, and STACS. Her contributions span theoretical advancements and collaborative projects, with a focus on solving foundational challenges in computational complexity.

• Theoretical Computer Science
• Constraint Satisfaction Problems
• Algorithms and Complexity Theory
• +3 more