Find Academic Supervisors

• Labor Economics
• Migration Policy
• Industry 4.0
• +3 more

Katarzyna Woźniak-Jasińska is an Assistant Professor in the Department of Macroeconomics and Development Studies at Poznań University of Economics and Business. Her office is located in Collegium Altum room 1017, and she can be reached at Katarzyna.Wozniak@ue.poznan.pl or +48 61 854 32 95. She serves on university committees including the Senate Commission for Scientific Research and International Cooperation. Research explores labor market transformations with emphasis on: Industry 4.0 impacts on employment OECD migration and integration policies Remote work dynamics and well-being Post-conflict socioeconomic reconstruction Recent work examines digital economy transitions through bibliometric analysis, comparative policy studies, and empirical evaluations of labor market outcomes. Publications frequently address European contexts with methodological diversity from theoretical frameworks to case studies.

Fas LebbieView profile

Researcher

• Transition Design
• Human-Centered Design
• Social Enterprise
• +7 more

Fas Lebbie is a PhD researcher and teaching fellow at Carnegie Mellon University's School of Design , specializing in Transition Design with focus on African mineral resource systems. His transdisciplinary work bridges design theory with technology, entrepreneurship, and social justice frameworks across four continents. Current industry roles: Product Design Manager at Meta , MIT GOV/LAB mentor Academic contributions: 15+ peer-reviewed case studies in fintech, AR design, and criminal justice reform Core research areas examine: Regenerative mining practices in West Africa Design interventions for financial inclusion Digital tools in industrial knowledge transfer Systems mapping of carceral economies Post-extractive economic transitions

• Programming Languages
• Formal Verification
• Compilers
• +5 more

Clément Pit-Claudel is a Tenure-Track Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL), leading the SYSTEMF Laboratory in the School of Computer and Communication Sciences. His work bridges programming languages, formal verification, and systems engineering to build high-assurance software and hardware components. PhD in Computer Science from MIT (2016), thesis on proof-producing compilers MSc in Computer Science from MIT BSc in Computer Science from École Polytechnique (France) Research Interests focus on correct-by-construction program synthesis , domain-specific compilers , and formal verification . He develops tools like Alectryon for interactive Coq proof visualization and Rupicola for verified compiler construction. His work spans hardware description languages (e.g., Kôika ), software verification (e.g., Fiat ), and regex engine formalization (e.g., Elk and Warblre ). Recent publications include foundational work on JavaScript regex mechanisms (ICFP 2024), relational compilation (PLDI 2022), and hardware simulation (ASPLOS 2021). His lab SYSTEMF emphasizes full assurance without compromise through machine-checked proofs and hardware-software co-design. Awards include the MIT Frederick C. Hennie III Teaching Award (2016) and MIT William A. Martin Thesis Award (2016). He has served as program committee member for conferences like PLDI, POPL, and Coq Workshop. Teaching includes Software Construction (400+ students at EPFL) and Interactive Theorem Proving graduate course. He has advised doctoral students in formal methods and compiler design.

James WorrellView profile

Professor

• Logic in Computer Science
• Linear Dynamical Systems
• Automated Verification
• +3 more

James Worrell is a Professor of Computer Science at the University of Oxford, affiliated with Green Templeton College. He holds a prestigious UKRI Fellowship (equivalent to an ERC Advanced Grant) and an EPSRC Established-Career Fellowship. His research focuses on logic in computer science, linear dynamical systems, and automated verification, with applications to formal methods and decision procedures. He has held leadership roles on program committees for major conferences including POPL, CONCUR, FoSSaCS, and ICALP. Notable invited talks include the CRM Workshop on Fluid Dynamics and the MOD23 Summer School in Formal Verification. He currently advises PhD students like Julian D’Costa and collaborates with postdocs Bertrand Teguia and Jakub Konieczny. His research investigates decidability questions in linear systems, recurrence sequences, and matrix semigroups. He has pioneered techniques combining o-minimal geometry, automata theory, and model checking to analyze dynamical systems. Recent work addresses transcendence properties of numerical sequences and robustness in machine learning models. Awards include ERC and UKRI funding recognitions. He teaches advanced courses on logic and proof, automata theory, and computational learning theory at Oxford, with lecture materials available online. His work bridges theoretical computer science with applications in safety-critical systems and formal verification.

Jendrik SeippView profile

Associate Professor

• Automated Planning
• Machine Learning
• Classical Planning
• +3 more

Jendrik Seipp is an Associate Professor in Artificial Intelligence at Linköping University (LiU), Sweden. He leads the 'Representation, Learning and Planning' lab within the AIICS division of the Department of Computer and Information Science (IDA). His research focuses on automated planning, particularly integrating machine learning with classical planning methods. Education: Docent in Computer Science, Linköping University (2022) Ph.D., University of Basel, Switzerland (2018) M.Sc. and B.Sc., University of Freiburg, Germany (2009–2012) Research Interests: Seipp specializes in classical planning systems, cost partitioning algorithms, heuristic search, and abstraction methods. His work emphasizes optimizing planning efficiency through techniques like saturated cost partitioning and Cartesian abstractions. He contributes to open-source planning tools such as Scorpion and Fast Downward, aiming to make planning accessible to broader audiences. Awards: IJCAI 2021 Distinguished Paper Award ICAPS 2020 Best Dissertation Award Multiple first/second places in international planning competitions (IPC, CoRe Challenge) Advising & Grants: Seipp leads the development of planning systems and frameworks like Scorpion, which won awards at ICAPS 2022 and IJCAI 2021. He collaborates on projects funded by the Wallenberg AI, Autonomous Systems and Software Program (WASP). His lab focuses on advancing symbolic AI methods and their applications in real-world planning tasks. Labs & Teams: He heads the 'Representation, Learning and Planning' lab at LiU, fostering interdisciplinary research in AI planning and optimization. The lab develops tools like Downward Lab for experiment management and contributes to open-source planning communities.

• Bioprocess Engineering
• Industrial Biotechnology
• Bioprocess Modelling
• +3 more

Dr Ali Jahanian is a Research Fellow at Queensland University of Technology (QUT), based in the Faculty of Engineering's Office of the Deputy Dean. He holds a PhD in Bioprocess Engineering (QUT) and an M.Sc. in Chemical Engineering (Biotechnology). His research focuses on bioprocess modeling, precision fermentation, and bioeconomy strategies, particularly through collaborations with industry partners like Sun Pharma and via QUT's Mackay Renewable Biocommodities Pilot Plant. He contributes to the ARC Centre of Excellence in Synthetic Biology and the Centre for Agriculture and the Bioeconomy, advancing sustainable bioprocessing technologies. Dr Jahanian's work integrates mechanistic and data-driven approaches to optimize bioreactor systems, reduce energy consumption in industrial fermentation, and enhance downstream processing efficiency. He teaches Industrial Fermentation and Applications in Biotechnology at QUT. His research spans methane bioconversion, enzyme production for pharmaceuticals, and nanocarrier applications in drug delivery. He actively supervises postgraduate students across Honours, Masters, and PhD programs. Key Research Areas: Bioprocess Engineering, Industrial Biotechnology, Techno-Economic Assessment Industry Collaborations: Sun Pharma, Mackay Pilot Plant

• Probabilistic Programming
• Formal Verification
• Programming Languages
• +3 more

Kevin Batz is a researcher in computer science at RWTH Aachen University, currently affiliated with University College London. He completed his PhD in December 2024 under Professor Joost-Pieter Katoen, focusing on probabilistic program semantics and verification. His work includes co-developing Weighted Programming, a paradigm for mathematical modeling, and advancing deductive verification techniques for probabilistic systems. Education: PhD in Computer Science (RWTH Aachen University, 2024, summa cum laude). Supervised by Prof. J.-P. Katoen. Research Interests: Probabilistic programming semantics, automated verification, formal methods for heap-manipulating systems, and quantitative separation logic. He has pioneered tools like the deductive verification infrastructure for probabilistic programs and contributed to foundational work on expectation analysis. Awards: ETAPS Doctoral Dissertation Award 2025 Best Paper Award at LOPSTR 2020 Springorum Medal 2019 Teaching & Supervision: Taught courses on probabilistic programming, static analysis, and compiler construction. Supervised multiple thesis projects, including award-winning works on quantitative separation logic and invariant-based strategy synthesis. Labs/Teams: Member of the Software Modeling and Verification Group at RWTH Aachen, contributing to the LuFG i2 research unit.

Ruth HoffmannView profile

Lecturer

• Computational Combinatorics
• Graph Theory
• Permutation Classes
• +4 more

Dr. Ruth Hoffmann is a Lecturer at the University of St Andrews, affiliated with the School of Computer Science and the St Andrews GAP Centre. Her research focuses on computational combinatorics, graph theory, permutation classes, computational group theory, and formal verification. She has contributed to improving combinatorial search algorithms for graph and group problems, including subgraph isomorphism and stabiliser chains. Research Interests Engineering efficient algorithms for graph and group problems Constraint programming applications in permutation enumeration and puzzle solving Formal verification of autonomous systems and memory models Combinatorial search optimization and algorithm design Teaching Current modules: Foundations of Computation (CS2001), Programming with Data (CS1003) Past roles: Overseer of BSc Data Science Graduate Apprenticeship and instructor for Computational Complexity (CS3052) and Information Security Management (IS5104) Recent Research Trends Dr. Hoffmann’s recent work emphasizes constraint modeling innovations, including permutation enumeration frameworks and automated puzzle-solving explanations. Her publications address formal verification challenges in autonomous systems and memory consistency protocols, alongside combinatorial search advancements for graph problems. Collaborative projects include EPSRC-funded efforts to enhance group search techniques using graph-based methods. Grants & Projects Enhancing Group Search with Graph Techniques (EPSRC, 2023–2025) C3+: Constraint-based Collaboration (EPSRC, 2018) Labs & Teams Active contributor to the St Andrews GAP Centre, focusing on computational algebra and combinatorics.

Simon ColtonView profile

Professor

• Computational Creativity
• Artificial Intelligence
• Game Design
• +4 more

Simon Colton is a Professor of Computational Creativity, Games, and Artificial Intelligence at Queen Mary University of London, affiliated with the School of Electronic Engineering and Computer Science. His research focuses on AI-driven creativity in domains like music, game design, and visual arts, emphasizing generative techniques and philosophical aspects of creative systems. He leads projects in automated composition, procedural content generation, and interdisciplinary AI applications. Education: Extensive background in AI and computational creativity, with a focus on generative systems. Research Interests: Computational Creativity, Generative AI, Music Generation, Game Design Automation, and Neuro-Symbolic Systems. His work explores how AI can autonomously create artistic content, including music, games, and visual art, with notable contributions to frameworks like ANGELINA for game design and The Painting Fool for automated art. Recent projects include AI-driven music composition and tools for creative support (e.g., Gamika, Danesh). Key grants include the £3M EPSRC-funded IGGI2 Centre for Doctoral Training, focusing on Intelligent Games and Game Intelligence. His lab, the Centre for Multimodal AI, advances research in cross-modal AI creativity.

Sergio Jiménez CelorrioView profile

Professor

• Automated Planning
• Machine Learning
• Bayesian Inference
• +3 more

Sergio Jiménez Celorrio is a Full Professor at the Department of Computer Systems and Computation of the Polytechnic University of Valencia. He has held previous positions including Ramón y Cajal fellow at the University of Melbourne, Juan de la Cierva fellow at Universitat Pompeu Fabra, and teaching assistant at Universidad Carlos III de Madrid, where he earned a Distinguished Thesis Award in 2011. His research focuses on automated planning, Bayesian inference, and machine learning synergies. He has co-organized the 7th International Planning Competition and contributed to top AI conferences. Education: PhD in Artificial Intelligence from Universidad Carlos III de Madrid (2011, Distinguished Thesis Award). Research interests emphasize automated planning frameworks, heuristic search, and integrating machine learning with planning systems. Awards include the IJCAI 2016 Distinguished Paper Award and Sister Conferences Best Paper Award at IJCAI 2022. His work spans over 50 publications in venues like AI Journal, JAIR, and IJCAI. Advises PhD student Diego Aineto García and collaborates on grants and competitions. Active in organizing conferences and workshops.

Michael QuinnView profile

Associate Professor

• Computer Architecture
• Hardware Verification
• Processor Design
• +3 more

Michael Quinn is an Associate Professor in the Department of Computer Science & Engineering at Texas A&M University, with an affiliation in Electrical & Computer Engineering. He holds a Master of Science in Electrical Engineering from the University of Southern California (1978) and a Bachelor of Science in Electrical Engineering from Drexel University (1975). His research and professional expertise focus on hardware verification, processor design, and memory systems. Notably, he has contributed to pre-silicon functional verification strategies for Intel's Xeon and Itanium processors, emphasizing validation methodologies for complex designs. His publications span topics like alpha processor validation, memory module design, and automated test equipment development. Quinn's work highlights a blend of academic research and industry application, with a strong emphasis on ensuring robustness in hardware systems through advanced validation techniques. His articles reflect interdisciplinary engagement between computer science and electrical engineering, addressing both theoretical frameworks and practical implementation challenges.

Mitch MarcusView profile

Professor

• Statistical natural language processing
• Cognitively plausible models
• Linguistic structure acquisition

Mitch Marcus is a Professor Emeritus in the Department of Computer and Information Science at the University of Pennsylvania's School of Engineering and Applied Science. He earned his Ph.D. from MIT in 1978 and joined Penn in 1987 after working at AT&T Bell Laboratories. His research focuses on statistical natural language processing and cognitively plausible models for automatic acquisition of linguistic structure. Dr. Marcus created and led the Penn Treebank Project, which revolutionized natural language parsing accuracy. He has served as PI on multiple major projects including DARPA LORELEI and GAILA AIX, focusing on unsupervised morphology acquisition and child language acquisition simulations. Awards: Fellow of American Association for Artificial Intelligence (1992) Founding Fellow of Association for Computational Linguistics (2011) Advising: His former PhD students hold positions at institutions including MIT, Johns Hopkins, Google, and Microsoft Research. Research groups: Directed the Penn Treebank Project and co-led the OntoNotes Project. Currently collaborates on natural language understanding for human-robot interaction.

Marion MartinView profile

Academic

• Chemistry Education
• Materials Science
• Physical Chemistry
• +3 more

Marion Martin is an Associate Teaching Professor and co-Director of Undergraduate Programs in the Department of Chemistry at North Carolina State University. They are affiliated with the College of Sciences and hold offices in Fox Hall 250. Their research focuses on chemistry education innovation, materials science, and surface chemistry, with a particular interest in automated feedback systems for student learning and structure-property relationships in introductory chemistry education. Education history includes a BS and MSc in Chemistry from Furman University (2000–2001), a PhD in Physical Chemistry from Stanford University (2007), and postdoctoral research in Materials Science at Caltech (2009). Their work bridges educational technology and traditional chemistry research, addressing both pedagogical advancements and material characterization challenges. Publications emphasize curriculum development in group projects, AI-driven assessment tools, and surface analysis of self-assembled monolayers. They are a key member of the Department of Chemistry’s leadership, contributing to undergraduate program coordination and academic advising.

Hila PelegView profile

Assistant Professor

• Programming Languages
• Software Engineering
• Human-Computer Interaction
• +7 more

Hila Peleg is an Assistant Professor in the Department of Computer Science at the Technion – Israel Institute of Technology, where she co-leads the TecSE lab with Prof. Shachar Itzhaky. Her research lies at the intersection of Programming Languages, Software Engineering, and Human-Computer Interaction, focusing on program synthesis and interactive developer tools. Her research interests center on creating intelligent, theory-driven tools that enhance programmer productivity and correctness. She explores interaction models that integrate formal methods like separation logic into practical synthesis systems, enabling more versatile and reliable code generation. Her work spans both foundational models and real-world applications, from web layout synthesis to computational crafting. The trend in her recent publications shows a strong focus on interactive and practical program synthesis, blending formal verification with user-centered design. She investigates how synthesis can be made more usable through live programming, best-effort results, co-design of tools and languages, and integration with developer workflows. Her work increasingly emphasizes the human aspect of programming tools. Distinguished Paper Award, PLDI 2021 Distinguished Artifact Award, SPLASH 2020 Hila Peleg advises multiple graduate students, including PhD and MSc candidates, and leads the ERC-funded EXPLOSYN project, which supports advanced research in program synthesis. She has taught advanced courses such as User-Centered Programming Tools and seminars in programming languages, and is actively involved in the academic community through conference service and organization. She is a core member of the TecSE lab, which focuses on advancing software engineering through programming language theory and interactive systems. The lab fosters interdisciplinary research at the boundary of formal methods and human-centered tool design.

• Space Instrumentation
• Radiation Detection
• On-Board Data Management Systems
• +5 more

Sebastián Sánchez Prieto is a Professor at the Department of Automatic Control, Universidad de Alcalá, Spain, affiliated with the Space Research Group (SRG-UAH). He holds a Doctorate from the same institution (1998) with a thesis on cosmic ion telescope control systems. His research focuses on space instrumentation, radiation detection systems, on-board data management, and embedded systems for space applications. He has contributed extensively to the Solar Orbiter mission, working on the Energetic Particle Detector (EPD) instrumentation and software validation. His technical expertise spans FPGA-based signal processing, RISC-V processor architectures, and model-driven engineering for space systems. Notable work includes advancing virtualization techniques for mixed-criticality space systems, digital beamforming architectures, and neural network applications in particle trajectory analysis. Publications emphasize cutting-edge topics like fault-tolerant computing in space environments, adaptive signal processing for space communications, and low-power positioning systems. He actively develops educational frameworks integrating on-board data management concepts across interdisciplinary university curricula. His contributions include over 50 peer-reviewed articles addressing hardware-software co-design, space software verification, and radiation effects mitigation. Current research explores AI-driven approaches for space instrumentation data analysis and next-generation on-board computing solutions.