Find Academic Supervisors

• Linear Algebra
• Matrix Analysis
• Nonnegative Matrices
• +7 more

Michael J. Tsatsomeros is a Professor in the Department of Mathematics & Statistics at Washington State University (WSU), affiliated with the College of Arts and Sciences. His academic journey includes positions at the University of Regina (Canada), University of Wisconsin, and the Indian Institute of Technology-Madras. He holds a Ph.D. from the University of Connecticut (1990), an M.Sc. from Concordia University (1986), and a Diploma in Mathematics from the University of Patras, Greece (1984). His research focuses on Linear Algebra and Matrix Analysis , with emphasis on nonnegative matrices, numerical range, and applications in dynamical systems, control theory, and AI. He has supervised numerous Ph.D. and Master’s students, including notable advisees such as Samir Mondal, Hung Le, and Pietro Paparella. His work has been recognized with awards like the 2024 Tom & Julie Lutz Teaching Excellence Award and the 2021 Excellence in Advising Award. Key publications include studies on P-matrices, Cayley transforms, and semimonotone matrices, often appearing in Linear Algebra and its Applications and Electronic Journal of Linear Algebra . He co-authored textbooks like Matrix Positivity (2020) and Linear Algebra and Matrix Methods (2024). Tsatsomeros serves as an Advisory Editor for Electronic Journal of Linear Algebra and Associate Editor for Linear Algebra and its Applications . His grants include the SPARC Collaboration Grant (2019–2021) and NSERC research funding. Beyond academia, his postdoctoral mentorship includes Fei Zhou and Panos Psarrakos, who now hold academic and industry roles. His research bridges theoretical matrix analysis with applied problems in engineering and computer science.

Alistair StewartView profile

Research Fellow

• Theoretical Computer Science
• Algorithms
• Machine Learning
• +3 more

Alistair Stewart is a Research Fellow in the Department of Computer Science at the University of Southern California (USC), working with Professor Ilias Diakonikolas. Previously, he completed his PhD at the University of Edinburgh under the supervision of Professor Kousha Etessami, focusing on algorithms for infinite-state stochastic models. His research interests span theoretical computer science, algorithms, machine learning, and probability theory. Notable areas include robust statistical estimation, distribution learning, algorithmic game theory, and probabilistic systems analysis. His work has been published in top conferences such as SODA, FOCS, COLT, and STOC. Key contributions include advancements in high-dimensional robust estimation, efficient learning algorithms for discrete distributions, and analysis of stochastic models like branching processes and context-free grammars. No scientific awards are explicitly mentioned in the provided texts. His academic trajectory includes collaborations with leading researchers in theoretical computer science, and his PhD thesis addressed foundational problems in stochastic algorithmics.

• Programming Languages
• Information Security
• Software Security
• +12 more

Dr Yi Lu is a Senior Lecturer at the Queensland University of Technology (QUT), Faculty of Science, School of Computer Science. He serves as Deputy Academic Lead (HDR) for the School of Computer Science, contributing to higher degree research leadership. His academic journey includes a PhD in Computer Science from the University of New South Wales (2008) and prior roles as a Lecturer at UNSW (2007–2013) and Principal Researcher at Oracle Labs (2013–2019). PhD in Computer Science, University of New South Wales, 2008 Bachelor of Information Technology (Honours), Queensland University of Technology, 2002 Dr Lu's research lies at the intersection of programming languages and information security, with a focus on theoretical and practical aspects of software security. His work includes type systems for object synchronization, ownership types, information flow control, and the application of deep learning to vulnerability detection. He has made significant contributions to static analysis, points-to analysis, and secure multithreaded programming. His recent publications span topics such as threat hunting in industrial control systems, nontransitive security types, and multi-class vulnerability prediction using graph neural networks. These works reflect a strong trend toward combining formal methods with machine learning to enhance software security, particularly in complex and critical systems. Dr Lu has been involved in significant research initiatives, including the Cyber Security Cooperative Research Centre and the ARC Linkage Project on Java language-based security conformance. His industry experience at Oracle Labs led to patented solutions for real-world cybersecurity problems, demonstrating the practical impact of his research. He supervises research projects on fine-grained vulnerability detection, reproducible deep learning workflows, and open-source supply chain analysis. His teaching includes courses such as Principles of Software Security and Object-Oriented Design, aligning closely with his research expertise. Dr Lu is affiliated with the School of Computer Science at QUT, a research-active unit within the Faculty of Science, contributing to cybersecurity education and innovation in Australia.

Melina FreitagView profile

Professor

• Numerical Linear Algebra
• Data Assimilation
• Inverse Problems
• +6 more

Melina Freitag is Professor for Data Assimilation at the Institute for Mathematics, University of Potsdam. Her research spans numerical linear algebra, inverse problems, and model order reduction, with applications in geophysics, image processing, and machine learning. She leads the Data Assimilation Group and contributes to the SFB 1294 Collaborative Research Center. Education : Prof. Freitag earned her Diplom in Mathematics at TU Chemnitz (2004) and PhD in Mathematical Sciences from University of Bath (2007). Research Themes : Her work focuses on Krylov subspace methods, low-rank approximations, and preconditioning for large-scale systems. She bridges classical numerical analysis with modern data assimilation, addressing challenges in: Bayesian inverse problems with unstable systems Optimized spectral sampling in X-ray imaging Physics-informed neural networks for Navier-Stokes inversion Parameter-dependent eigenvalue analysis Collaborations & Grants : She collaborates with institutions like KTH Stockholm and Arizona State University. Her group secures funding through SFB 1294 and participates in INI Cambridge networks. Leadership : Co-Chair of GAMM Activity Group on Applied Numerical Linear Algebra SIAM Activity Group on Linear Algebra Chair (2022) Teaching : Delivers courses on numerical optimization, matrix methods in data science, and inverse problems, integrating computational theory with real-world applications.

• Theoretical Computer Science
• Computability Theory
• Algorithmic Learning Theory
• +7 more

Pavel Semukhin is a Lecturer in the Department of Computer Science at Liverpool John Moores University, where he contributes to research and teaching in theoretical computer science and mathematical logic. His research interests include: Reachability problems in linear dynamical systems Finite and probabilistic automata Algorithmic learning theory Computability theory Computable model theory Matrix semigroups and decidability His recent work focuses on decidability and reachability in low-dimensional matrix semigroups, probabilistic automata, and logical structures, with publications in top venues such as CONCUR, MFCS, ICALP, and SODA. His research often bridges theoretical computer science and mathematical logic, especially in the context of automata, algebra, and learning models. His collaborative work with researchers like Igor Potapov, Sanjay Jain, and others has led to significant results in matrix problems and algorithmic learning. Though no formal list of students is provided, his publications suggest mentorship and collaboration with early-career researchers. He has also contributed to editorial work, including a special issue for International Journal of Foundations of Computer Science (2018) related to DLT 2015.

Petr JancarView profile

Professor

• Theoretical Computer Science
• Language and Automata Theory
• Computability and Complexity Theory
• +4 more

Petr Jancar is a Professor at the Department of Informatics, Faculty of Science, Palacký University in Olomouc. He has previously held academic positions at VŠB-Technical University of Ostrava and the University of Ostrava. His research is centered in theoretical computer science, with deep contributions to automata theory, Petri nets, system verification, and computational complexity. Education RNDr, Theoretical Cybernetics and Mathematical Informatics, Faculty of Mathematics and Physics, Charles University, Prague (1982) CSc. (PhD equivalent), Dissertation: "Questions of Decidability of Dynamic Properties of Petri Nets", Charles University (1989) Appointed Associate Professor in Informatics, Faculty of Informatics, Masaryk University (1996) Appointed Professor in Informatics, VŠB-TU Ostrava (2008) His primary research interests include theoretical computer science , focusing on language and automata theory, computability and complexity, algorithm theory, and mathematical logic. A significant portion of his work addresses system verification , particularly using Petri nets and infinite-state systems. He investigates fundamental questions of bisimilarity, equivalence checking, decidability, and complexity in models like pushdown and one-counter automata. Jancar's recent publications (2020–2025) show a strong trend in advancing the theory of Petri nets, especially structural liveness, reachability, and home-space problems, often establishing high complexity bounds (e.g., Ackermann-complete). His work combines deep theoretical insight with formal rigor, frequently published in top venues like LICS, CONCUR, and LogMethCS. He has long-standing collaborations with researchers such as Jérôme Leroux, Zdeněk Sawa, and Antonín Kučera. Scientific Service and Recognition Member, Editorial Board, Information and Computation (since 2015) Member, Scientific Councils at Masaryk University, VŠB-TU Ostrava, Palacký University, and Brno University of Technology Chairman, GAČR Panel P202 Informatics (2015–2017) Member, Program Committees of major international conferences He has been a co-investigator and researcher on multiple GAČR projects, including those on algorithms for infinite-state systems, computational complexity of verification, and modeling of parallel systems. He has also participated in the Center for Applied Cybernetics. He has held research stays supported by grants in Germany, France, Sweden, and the UK. Laboratories and Research Groups His work is closely associated with formal methods and verification research groups at Palacký University and former affiliations. He has contributed to collaborative projects involving MPI verification and infinite-state system analysis.

Peter Gjøl JensenView profile

Associate Professor

• Model Checking
• Formal Verification
• Reinforcement Learning
• +8 more

Peter Gjøl Jensen is an Associate Professor in the Department of Computer Science at Aalborg University's Technical Faculty of IT and Design. His research spans formal methods, artificial intelligence, and practical applications in energy systems. He is affiliated with multiple research groups including Distributed, Embedded and Intelligent Systems, AI for the People, and Artificial Intelligence and Machine Learning. His research interests focus on Model Checking , Formal Verification , Reinforcement Learning , and applications to Cyber-physical Systems . He has developed expertise in applying theoretical computer science to solve real-world problems, particularly in energy optimization and heat pump control systems. His work bridges the gap between formal methods and practical engineering applications. His recent publications show a clear trend toward applying AI and formal verification techniques to energy systems and cyber-physical applications. The research spans theoretical foundations of model checking and reaches into practical implementations for heat pump control, autonomous systems, and environmental management. His work often involves the UPPAAL and Stratego frameworks for verification and synthesis. Dr. Jensen actively supervises PhD students, including Andreas Holck Hoeg-Petersen on the "Explainable and Causally Enforced Reinforcement Learning" project. His research has attracted media attention, particularly for intelligent heat pump control systems that provide significant energy savings. He is involved in multiple research projects including "Explainable and Causally Enforced Reinforcement Learning" (ongoing) and "BEO-COVID: Decision Support for Evaluation and Optimization in UPPAAL" (completed in 2020).

• Theoretical Computer Science
• Formal Methods
• Automata Theory
• +7 more

Ismaël Jecker is a tenured Associate Professor in the Department of Computer Science at Université de Franche-Comté, affiliated with the Vesontio research team. He previously held postdoctoral positions at the Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw (2021–2023), and at the Institute of Science and Technology Austria (IST Austria) in the Chatterjee group (2019–2021). His educational background includes a PhD in Computer Science from Université libre de Bruxelles (ULB) (2014–2019), supervised by Emmanuel Filiot and Jean-François Raskin, and undergraduate studies in Mathematics at École Polytechnique Fédérale de Lausanne (EPFL). Jecker's research focuses on theoretical computer science, particularly automata theory, formal methods, verification, transducers, weighted automata, and game semantics. His work bridges logic, computation, and algebraic structures, with applications in program verification and formal language processing. He investigates expressiveness, determinization, complexity, and equivalence problems in various automata models. His recent publications, appearing in top venues like LICS, CONCUR, MFCS, and STACS, show a consistent trend toward understanding computational models with quantitative features—such as counting, bidding, and delays—and their algorithmic properties. Themes include reachability in vector addition systems, determinization of weighted automata, and strategic reasoning in graph games with partial observability. Best Paper Award, MFCS 2021 – A Bit of Nondeterminism Makes Pushdown Automata Expressive and Succinct Jecker has collaborated widely with leading researchers in formal methods, including Emmanuel Filiot, Wojciech Czerwiński, Krishnendu Chatterjee, and Christof Löding. While no formal advising or grant information is listed, his extensive publication record and collaborations suggest active mentorship and research leadership. He is a core member of the Vesontio team, which focuses on foundational aspects of computer science, including automata, logic, and verification.

Paul C. BellView profile

Senior Lecturer

• Theoretical Computer Science
• Computability Theory
• Automata Theory
• +22 more

Dr Paul C. Bell is a Reader in Computability and Automata Theory at the School of Computer Science and Mathematics, Liverpool John Moores University, UK. He has held academic positions at Keele University (Reader), Loughborough University (Lecturer), and the University of Liverpool (University Teacher). He completed his PhD at the University of Liverpool in 2007 and held postdoctoral positions at Université catholique de Louvain (Belgium) and the University of Turku (Finland). Current Affiliation: Liverpool John Moores University, School of Computer Science and Mathematics Previous Positions: Keele University (Reader), Loughborough University (Lecturer), University of Liverpool (University Teacher) Postdoctoral Experience: UCLouvain (Belgium), University of Turku (Finland) PhD: University of Liverpool, 2007 Paul C. Bell's research lies at the intersection of theoretical computer science and mathematics. His work focuses on foundational problems in computability, automata, and formal systems. Key areas include reachability in dynamical and hybrid systems, matrix semigroups, linear recurrence sequences, and algorithmic verification. He also explores quantum and probabilistic automata, Markov chains, Petri nets, and complexity in algebraic and logical systems. Additional interests span online algorithms, GPU computing, energy-aware scheduling, and computational geometry. His research is supported by grants from the Royal Society and London Mathematical Society, and he has received an NVIDIA hardware grant. He is an active supervisor, having guided four PhD students to completion and currently mentoring two more. He serves on the editorial board of The Computer Journal and is involved in numerous programme committees, including STACS and ICTCS-FAW. Royal Society International Exchange Grant Two London Mathematical Society travel grants NVIDIA hardware grant (2015) Dr Bell has supervised four PhD students to completion and is currently supervising two PhD candidates in Theoretical Computer Science and Pure Mathematics. He has served as an external examiner for MEng programmes at the University of Liverpool and for PhD theses at Loughborough, Liverpool, and Turku universities. He has received research grants and collaborates internationally, particularly with institutions in Belgium and Germany. He has organized major conferences such as RP 2021 and BCTCS 2014. Dr Bell has been involved in organizing significant academic events, including serving as co-chair of the 15th International Conference on Reachability Problems (RP 2021) in Liverpool, co-chair of BCTCS 2014, and organizer of a special session on Reachability Problems at Computability in Europe 2022. He has also served on multiple programme committees and as an organizing chair for ICIDM 2018 at LJMU.

• Numerical Analysis
• Applied Mathematics
• Computational Mathematics
• +3 more

Emre Mengi is a Professor in the Department of Mathematics at Koc University, affiliated with the School of Computational Sciences and Engineering. He holds a PhD (2006) and Master’s (2003) in Mathematics from New York University, and a B.A. (2000) in Computer Engineering from Middle East Technical University. Research areas: Numerical Analysis , Eigenvalue Optimization , and Model Reduction Current focus: Subspace frameworks for nonlinear eigenvalue problems and pseudospectral abscissa minimization His recent work emphasizes large-scale stability analysis of descriptor and Hamiltonian systems, distance-to-instability measures, and L∞-norm computation for robust control. He has developed algorithms for nonsmooth eigenvalue optimization and software tools like Eigopt. Scientific Awards : BAGEP Award (2014), Householder Award Honorable Mention (2008), Janet Fabri Prize Honorable Mention (2007), IMA Leslie Fox Prize (2004), Continuous Optimization Young Researcher Award

Igor PotapovView profile

Professor

• Theoretical Computer Science
• Algorithmic Complexity
• Matrix Semigroups
• +6 more

Igor Potapov is a Professor at the University of Liverpool , leading the Algorithms, Complexity Theory and Optimisation (ACTO) Group within the Department of Computer Science. His career includes progressive academic roles from Lecturer (2002) to Professor (current), with a focus on theoretical computer science and formal verification. Research Themes : Reachability problems in matrix semigroups, robot scheduling, broadcasting automata, and computational complexity. Grants & Awards : EPSRC grant (£459K, 2014-2018) on reachability problems NATO Collaborative Linkage Grant (€15,000, 2008-2010) Royal Society International Joint Projects and Travel Grants Nuffield Foundation Grant (£5,000, 2003-2005) Royal Academy of Engineering Fellowship (£3,940, 2008-2009) Recent Work Trends : Focus on geometric coverage algorithms (2025) Collision-free scheduling for robotic systems (2024-2025) Advances in matrix semigroup decidability (2024) Applications to programmable matter and crystal structure prediction (2022-2023) Contact : potapov@liverpool.ac.uk

• Formal Methods
• Cyber-Physical Systems
• Autonomous Systems
• +3 more

Chuchu Fan is an Assistant Professor at the Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, where she leads the Reliable Autonomous Systems Lab. Her research integrates formal methods, machine learning, and control theory to ensure safety in autonomous systems. ACM Doctoral Dissertation Award (2020) Her work focuses on verification algorithms for embedded systems, including tools like DryVR and RealSyn. She applies these technologies to industrial-scale problems such as autonomous vehicles and medical devices, bridging theoretical advancements with practical scalability. Her research emphasizes sensitivity analysis, symbolic reachability, and data-driven verification of nonlinear hybrid systems.

Pierre GantyView profile

Research Professor

• Computer Science
• Verification
• Formal Languages
• +3 more

Pierre Ganty is an associate Research Professor at IMDEA Software Institute in Spain, specializing in verification, language theory, and automata theory. Research Focus His research interests include algorithmic verification techniques for infinite-state systems, abstract interpretation refinement, automata theory applications, and Petri net analysis. Primary domains span theoretical computer science and formal methods with practical applications in software verification. Publication Trends Recent works (2023-2025) predominantly explore language inclusion problems, automata theory extensions, and verification frameworks, with consistent focus on theoretical foundations and model checking applications. Advising Current and former advisees include: Nicolas Manini (PhD student since 2021) Kyveli Doveri (PhD graduate 2024) Elena Gutiérrez (PhD graduate 2020) Pedro Valero (PhD graduate 2020) Research Team Leads a research group at IMDEA Software Institute developing tools like ωVPLInc and MIST for automata-based verification.

Courtney DixonView profile

Lecturer

• Computer Science
• Computer Science Education

Courtney Dixon serves as a Lecturer in the Department of Computer Science at Appalachian State University in Boone, North Carolina. Her office is located in room 308-A of Anne Belk Hall, with mailing address Appalachian State University, Anne Belk Hall 312-P, Boone NC 28608. She is reachable by telephone at (828) 262-3127 for departmental inquiries. Her professional focus centers on undergraduate instruction within computer science education. Key research interests include: Computer Science: Core programming concepts, software engineering fundamentals, and algorithmic problem-solving Computer Science Education: Curriculum development, pedagogical methodologies, and instructional design for computing disciplines No scientific awards or honors were documented in the source material. The provided information indicates no active research grants, student advising responsibilities, or laboratory affiliations, with her role primarily emphasizing teaching within the department's academic framework.

• Computer Science education
• Probabilistic algorithms
• Graph algorithms
• +1 more

Dr. Eleni Akrida serves as an Associate Professor in the Department of Computer Science at Durham University and holds the position of Deputy Executive Dean (Academic Student Experience) in the Faculty of Science. Since joining Durham in 2019, she has significantly contributed to academic leadership, including a term as Director of Undergraduate Studies from 2020 to 2025, shaping curriculum development and student experience across the department. Her academic foundation includes a Mathematics degree from the University of Patras, Greece, followed by Computer Science studies at the University of Liverpool, UK, establishing her interdisciplinary expertise in theoretical and applied computer science. Dr. Akrida's research bridges cutting-edge Computer Science education and theoretical algorithms. She pioneers pedagogical frameworks for abstraction skill development and investigates neurodivergent student experiences in CS education. Concurrently, her theoretical work explores probabilistic algorithms, graph complexity, and dynamic network optimization, yielding foundational insights in temporal graph theory and algorithmic game theory. Analysis of her publication trajectory reveals a strategic evolution: recent work (2023-2025) emphasizes educational innovation through frameworks for abstraction skills and neurodivergent inclusion studies, complemented by NLP contributions to plagiarism detection. Earlier research (2014-2021) established her authority in temporal graph algorithms, with seminal journal publications on connectivity, exploration, and game-theoretic models in dynamic networks. Her research impact is amplified through competitive grant funding: Exploring the Experiences of Neurodivergent Students in Computer Science Higher Education in the UK (CPHC Special Project, 2025/26) A Theoretical Computer Science Commons in the hybrid era (CPHC Special Project, 2022/23) Dr. Akrida actively mentors postgraduate researchers including Saira Richardson and leverages her leadership roles to drive educational innovation. She contributes to Durham's Pedagogical Innovations in Computer Science and Algorithms and Complexity research groups, fostering interdisciplinary collaborations that bridge theoretical rigor with practical educational applications.