Find Academic Supervisors

Jan DobrosolskiView profile

Lecturer

• Energy-Efficient Computing
• Deep Learning
• Parallel and Distributed Systems
• +3 more

Jan Dobrosolski serves as an Assistant lecturer at the Department of Computer Architecture within the Faculty of Electronics, Telecommunications and Informatics at Gdańsk University of Technology. His academic work focuses on performance-energy optimization in high-performance computing systems, with particular expertise in power management constraints. His research spans energy-efficient computing methodologies across multiple domains: deep learning acceleration, natural language processing tokenization, and reinforcement learning applications. Key specializations include power capping techniques for multi-core CPUs and multi-GPU architectures, neural network training optimization, and logic-based agent development using declarative programming frameworks. Dobrosolski's 2024 publications reveal a cohesive research trajectory centered on sustainable computing practices. His work systematically examines energy-performance trade-offs in tokenizer algorithms, deep neural network training, and neural agents for logic tasks—all under controlled power constraints. This integrated approach demonstrates significant contributions to resource-efficient computing across both CPU and GPU hardware platforms. Contact: jandobro@pg.edu.pl

Enrico TassiView profile

Researcher

• Interactive Theorem Provers
• Type Theory
• Functional Programming
• +3 more

Enrico Tassi is a researcher at INRIA (French Institute for Research in Computer Science and Automation) in the STAMP research team. He obtained his Ph.D. from the University of Bologna in 2008, where he worked on the design and implementation of the Matita interactive theorem prover. His research primarily focuses on the technology of formal proofs, particularly type theory implementation and mathematical modeling. Education: Ph.D. in Computer Science, University of Bologna (2008) Tassi's research explores interactive theorem provers, functional programming, and logic programming. He maintains the small scale reflection extension for Coq and develops the Elpi extension language to enhance OCaml software capabilities. His work enables advanced manipulation of mathematical structures and formal proof environments through high-level programming constructs. His publications demonstrate consistent focus on theorem proving, formal methods, and programming language theory. Recent works emphasize practical implementations in proof assistants (Matita, Coq), unification algorithms, and formal verification of mathematical theories including measure theory and algebra. Tassi has contributed to significant projects including the Mathematical Components library (formalizing the Odd Order Theorem), the Paral-ITP project (scaling Coq for large mathematical libraries), and the CoREACT ANR project. He develops open-source tools including Coq-Elpi plugin and Hierarchy-Builder. He serves on program committees for conferences including CPP, CoqPL and FLOPS. While retired from Debian development (2006-2016), he maintains involvement in open-source communities.

• Internet of Things (IoT)
• Edge Computing
• Digital Twins
• +6 more

Hui Song serves as an Assistant Professor in the Department of Computer Science at Oslo Metropolitan University's Faculty of Technology, Art and Design. Based at Pilestredet 52, Oslo, their work bridges theoretical computer science and practical IoT security applications within the Innovation, Digital Transformation and Sustainability research group. Research spans Internet of Things security , Edge Computing , and Digital Twin architectures with emphasis on trust management and secure software updates. Key interests include community-driven generative AI development, cloud-to-edge continuum security, and sustainable computing frameworks for IoT ecosystems. Recent projects address identity management in constrained devices and resilience engineering for critical infrastructure. Analysis of 2023-2025 publications reveals a consistent focus on security orchestration for IoT/CPS systems through digital twin implementations and intent-based data operations. Emerging trends show increased exploration of ethical generative AI applications and migration strategies from monolithic to microservice architectures in cloud-native environments. Active in both European research collaborations and industry-facing dissemination, Dr. Song contributes to advancing secure, sustainable computing practices through 56+ scientific publications and practical implementation frameworks developed with international partners including Rustem Dautov and Nicolas Ferry.

Matteo CiminiView profile

Assistant Professor

• Programming Languages
• Formal Methods
• Type Systems
• +7 more

Matteo Cimini is an Assistant Professor in the Miner School of Computer & Information Sciences at the Kennedy College of Sciences, University of Massachusetts Lowell. He teaches courses in programming languages, algorithms, and computer science foundations across multiple semesters from 2017 through 2025. Dr. Cimini earned his Ph.D. in Computer Science from Reykjavik University (2012), where his dissertation focused on Contributions to the Meta-Theory of Structural Operational Semantics. He also holds an M.S. in Computer Science from the University of Bologna (2008). His research centers on programming language theory, with particular expertise in formal methods, type systems, and language semantics. Dr. Cimini's work bridges theoretical foundations with practical language design, focusing on verification, gradual typing, and concurrency. His research has been published in top-tier programming languages conferences including POPL, ICFP, and PLDI. Dr. Cimini's recent publications demonstrate a strong focus on language-oriented programming, with his "Lang-n-X" series of systems exploring innovative approaches to language design, verification, and analysis. His work often combines theoretical rigor with practical implementation, creating tools that enable new ways of working with programming languages. Best Paper Award at TAP 2023 for "Testing Languages with a Languages-as-Databases Approach" Dr. Cimini actively mentors students, with recent advisees including Ph.D. student Joan and master's student Eswar, who successfully defended a thesis on Lang-n-Prove. He serves on program committees for numerous prestigious conferences including IFL 2025, SLE 2025, FORTE 2025, ICE 2025, and has been involved with conferences dating back to 2015. Dr. Cimini leads the development of the "Lang-n-X" series of systems, which includes tools like Lang-n-Prove (for language proofs), Lang-n-Send (for language communication), and Lang-n-Assert (for language logics). These systems form an integrated framework for language design, verification, and analysis, representing a cohesive research vision that has evolved through consistent publication output.

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Logic Programming
• +5 more

Thomas Eiter is a Full Professor at the Vienna University of Technology (TU Wien) in the Department of Knowledge-Based Systems, Faculty of Informatics. His research focuses on artificial intelligence, knowledge representation and reasoning, logic programming, computational logic, and neurosymbolic AI integration. He leads projects in declarative problem-solving, intelligent agent systems, and stream reasoning frameworks like LARS. Eiter has contributed to foundational work in answer set programming (ASP), algebraic reasoning, and their applications in scheduling, robotics, and real-time data processing. He has extensive international collaborations, including EU-funded projects like HumanE-AI-Net and the Austrian Science Fund (FWF) initiatives. His work emphasizes bridging symbolic AI with modern machine learning techniques, particularly in visual question answering and neural-symbolic systems. Eiter has supervised numerous PhD students and maintains active roles in academic leadership, including editorial boards of journals like Theory and Practice of Logic Programming . Key contributions include development of the DLVHEX system for hybrid knowledge representation, optimization frameworks for ASP, and methodologies for stream reasoning in dynamic environments. His research also addresses ethical AI through projects like the TAIGER initiative, focusing on training AI agents with ethical rules.

Mayur NaikView profile

Professor

• Programming Languages
• Artificial Intelligence
• Neurosymbolic Programming
• +5 more

Mayur Naik is the Misra Family Professor in the Department of Computer and Information Science at the University of Pennsylvania’s School of Engineering and Applied Science. His research lies at the intersection of programming languages and artificial intelligence, with a current focus on neurosymbolic programming, trustworthy AI for healthcare, and AI-enabled software engineering tools. Education & Career: Ph.D. in Computer Science, Stanford University (2008) – advisor Alex Aiken M.S. in Computer Science, Purdue University (2003) – advisor Jens Palsberg B.E. in Computer Science, BITS Pilani (1999) Former faculty at Georgia Institute of Technology and researcher at Intel Labs, Berkeley Research Interests: Naik’s group develops languages, algorithms, and compilers for neurosymbolic programming, an emerging paradigm that unites symbolic reasoning with data-driven learning. Their flagship system is the open-source Scallop language and toolchain, applied to computer vision, cybersecurity, medicine, and bioinformatics. He also investigates AI-assisted programming tools that boost productivity and software quality by marrying traditional program analysis with modern machine learning. Recent Highlights: In 2024 he was named Misra Family Professor; his former student Elizabeth Dinella received the 2025 ACM SIGSOFT Outstanding Dissertation Award; his team released IRIS , an LLM-assisted static analysis framework for security vulnerabilities, and published the first comprehensive book on Neurosymbolic Programming in Scallop . Teaching: He regularly teaches CIS 5470 (Software Analysis) every Fall and CIS 5500 (Database Systems) every Spring, both of which are also delivered in Penn’s MCIT Online and Georgia Tech’s OMSCS programs. Advising & Service: Naik has graduated 8 Ph.D. students and mentored numerous postdocs and undergraduates; many alumni now hold faculty or research positions worldwide. He has served on organizing, program, and steering committees for premier venues such as PLDI, POPL, OOPSLA, SPLASH, ESEC/FSE, ISSTA, SAS, and others.

Luc De RaedtView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Mining
• +3 more

Luc De Raedt is a Full Professor of Computer Science at KU Leuven, where he serves as the Director of Leuven.AI - KU Leuven Institute for Artificial Intelligence and Head of the Subdivision Declarative Languages and Artificial Intelligence within the Faculty of Engineering Science. He is also a Wallenberg Guest Professor at Örebro University's Center for Applied Autonomous Sensor Systems. Professor De Raedt's research focuses on the intersection of Artificial Intelligence, Machine Learning, and Logic Programming. His work spans probabilistic logic programming, neurosymbolic AI, statistical relational learning, and applications in various domains. He leads the Declarative Languages and Artificial Intelligence research unit (DTAI) at KU Leuven, which has been at the forefront of integrating symbolic and subsymbolic AI approaches. His research has evolved from traditional Inductive Logic Programming to Statistical Relational Learning and more recently to Neurosymbolic AI, which combines neural networks with symbolic reasoning for more transparent and trustworthy AI systems. His recent publications demonstrate a strong focus on neurosymbolic AI, with work spanning theoretical foundations as well as practical applications in areas like constraint learning, relational reinforcement learning, and probabilistic programming. His research integrates learning and reasoning in AI, with particular emphasis on creating systems that can both perceive patterns and perform logical reasoning. ERC Advanced Grant 2015 ERC Advanced Grant 2023 EurAI Fellow AAAI Fellow ELLIS Fellow Former IJCAI Trustee Elected Member of the Royal Flemish Academy of Belgium Wallenberg Guest Professor Professor De Raedt leads numerous research projects including "Bayesian Neurosymbolic AI for Trustworthy Machine Learning," "Neurosymbolic AI," "Theoretically Sound Neurosymbolic AI," and "Deep Probabilistic Logics." He is also involved in the Flanders Artificial Intelligence Research program (FAIR) as a work package leader. His research group includes PhD students and postdocs working on various aspects of neurosymbolic AI and probabilistic programming, with significant funding from European Research Council grants. He leads the Declarative Languages and Artificial Intelligence research unit (DTAI) at KU Leuven, which consists of multiple research teams focusing on different aspects of AI, machine learning, and logic programming. The unit has a strong international presence and collaborates with researchers worldwide, maintaining leadership in probabilistic logic programming through systems like ProbLog.

Craig InnesView profile

Research Fellow

• Cyber-Physical Systems
• Formal Verification
• Machine Learning
• +4 more

Craig Innes is a Research Fellow at the University of Edinburgh , affiliated with the Institute for Perception, Action and Behaviour (IPAB) . His research bridges symbolic logic and formal verification with black-box probabilistic models in cyber-physical systems , focusing on trustworthiness , risk assessment , and safety guarantees for autonomous vehicles and robotics . His work explores temporal logic specifications , adaptive experiment design , and probabilistic calibration to improve safety validation . Key trends in his publications include formal verification for machine learning systems , physics-informed simulation for soft robotics , and risk-driven perception system design . He also investigates unawareness in decision-making and scenario generation via large language models . Craig supervises PhD students in areas like hybrid AI for cyber-physical systems and autonomous robotics , with funding opportunities through EPSRC Doctoral Training Partnerships and Centre for Doctoral Training programs in Dependable Robotics and Machine Learning Systems . He emphasizes research proposal development and collaboration with industry in his supervision approach.

Seth GoldsteinView profile

Associate Professor

• Programming Languages
• Systems
• Computer Architecture
• +9 more

Seth Goldstein is an Associate Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. His research focuses on ensembles (massively scalable systems), distributed systems, reconfigurable computing, and programmable matter (Claytronics). He leads projects like BoLT (a decentralized lending platform) and Phoenix (computing without traditional processors). He has advised numerous graduate students and teaches courses including Compiler Design, Computer Architecture, and Cloud Computing. Research Interests : Goldstein explores systems with massive interacting components, such as molecular-scale devices, modular robotics, and economic systems. His work spans compilers (e.g., register allocation optimizations), time synchronization protocols for embedded systems, and algorithms for self-reconfiguring robots. Recent projects include BlockGraph (a secure distributed ledger) and peer code review methodologies. Key Contributions : Goldstein's work on Claytronics pioneered programmable matter, enabling shape-shifting materials. His BoLT platform addresses lending market inefficiencies using blockchain principles. He has also developed algorithms for modular robot coordination and energy-efficient self-reconfiguration. Labs/Teams : Goldstein directs research in the CMU Robotics Institute and collaborates with interdisciplinary teams on projects like the PipeRench and Phoenix architectures. His lab explores intersections between nanotechnology, distributed systems, and societal applications like new monetary systems.

Annemarie BorgView profile

Assistant Professor

• Nonmonotonic Logic
• Formal Argumentation
• Defeasible Reasoning
• +3 more

Annemarie Borg is an Assistant Professor in the Responsible AI Group at the Department of Information and Computing Sciences, Utrecht University. She maintains an affiliated research position with the Research Group for Nonmonotonic Logic and Formal Argumentation at Ruhr University Bochum, where she completed her PhD. Her core research investigates defeasible reasoning through formal argumentation frameworks, emphasizing computational models of scientific inquiry and explainable AI systems. Borg's research spans: Nonmonotonic Logic : Modeling human-like reasoning under uncertainty. Formal Argumentation : Theoretical foundations for structured and abstract argumentation frameworks. Agent-Based Modeling : Simulating scientific communities and inquiry efficiency. Explainable AI : Developing transparent decision systems for real-world applications (e.g., Dutch National Police). Her publications focus on hypersequent-based argumentation, stability estimation in dynamic systems, and MCS (maximal consistent subsets) reasoning. Recent work emphasizes practical AI explainability, with theoretical rigor in logic design and computational efficiency. No awards, grants, or supervised students are documented in available sources. She collaborates with the Responsible AI Group at Utrecht, focusing on ethically aligned computational systems. Her future directions likely involve refining argumentation-based explanation frameworks for high-stakes AI applications.

Paul FodorView profile

Associate Professor

• Artificial Intelligence
• Natural Language Processing
• Logic Programming
• +4 more

Dr. Paul Fodor is an Associate Professor of Practice in the Department of Computer Science at Stony Brook University. He holds a Ph.D. (2011) and M.S. (2006) from Stony Brook University, and a B.Eng. (2002) from Technical University of Cluj-Napoca, Romania. His research focuses on logic programming, complex event processing, knowledge representation, and natural language processing. He contributed to projects like IBM Watson, OpenRuleBench, and ETALIS. Fodor teaches numerous courses, including CSE 114, 160, 215, 305, and 307. He leads the Computer Science Undergraduate Program and coordinates the Accelerated BS/MS Program. Research Grants & Collaborations : SILK language (Vulcan Inc.), Knowledge representation (XSB Inc./DARPA), and collaborations with Prof. Michael Kifer. His work spans rule-based systems, semantic web, and AI applications. Awards : 2019 SUNY Chancellor's Award for Excellence in Teaching, multiple undergraduate teaching awards, and the RuleML 2013 Challenge award. His research emphasizes declarative rule languages and their applications in knowledge bases and event processing. Labs & Projects : Co-developed ErgoAI, ETALIS, and contributed to the IBM Watson team. Active in rule-based benchmarking (OpenRuleBench) and logic-based systems for event processing.

• Declarative Programming
• Answer Set Programming
• Knowledge Representation
• +2 more

Jia-Huai You is Professor in the Department of Computing Science at the University of Alberta, where he previously served as Department Associate Chair (Graduate). His research focuses on declarative problem-solving paradigms in artificial intelligence. Research examines computational aspects of knowledge representation systems including answer set programming, constraint programming, and SMT solving. Current investigations analyze semantic foundations, computational properties, and implementation strategies for declarative systems.

Patrick DohertyView profile

Professor

• Artificial Intelligence
• Autonomous Systems
• Knowledge Representation
• +6 more

Patrick Doherty is a Professor and Head of the Artificial Intelligence Laboratory (AILAB) at Linköping University's Department of Computer and Information Science (IDA). His research focuses on knowledge representation, automated planning, and intelligent autonomous systems, with applications in UAV-based emergency rescue and disaster informatics. He leads the AIICS division, emphasizing collaborative robotics and multi-agent systems. He holds a EURAI (ECCAI) fellowship and serves as Editor-in-Chief of the Artificial Intelligence Journal. His work includes developing UAV systems for alpine rescue, real-time mission planning frameworks, and robust robotic collaboration protocols. Notable contributions include the SHERPA project and the RGS RDF graph synchronization system. Doherty's research also addresses human-robot collaboration, safety-critical decision-making, and resilient autonomous systems within the Wallenberg AI, Autonomous Systems and Software Program (WASP). His research group has won international competitions in UAV and automated planning domains, demonstrating advanced UAV deployment in emergency scenarios. Key areas include geolocated object detection, distributed situation awareness, and efficient wireless mesh network configurations for disaster response. Doherty frequently delivers keynote speeches at conferences such as IJCAI, KR, and ECAI, emphasizing practical applications of AI in safety-critical systems. Key Projects: SHERPA (human-robot collaboration in alpine rescue), WASP public safety arena, UAV navigation systems Awards/Fellowships: EURAI Fellow, Editor-in-Chief of Artificial Intelligence Journal Labs/Teams: AILAB, Unmanned Aircraft Systems Technology Laboratory (UASTECH) Publications span foundational AI theory (e.g., forgetting policies, rough set reasoning) to applied robotics (e.g., UAV navigation, real-time search algorithms). His work bridges high-level mission planning with low-level control, ensuring robustness and autonomy in dynamic environments.

• Proof Theory
• Deep Inference
• Theoretical Computer Science
• +5 more

Paola Bruscoli is a Lecturer in the Department of Computer Science at the University of Bath, specializing in Proof Theory, Deep Inference, and Theoretical Computer Science. She holds a PhD in Computer Science (1997) from the University of Ancona and has held academic roles at institutions including TU Dresden (Substitute Associate Professor, 2011–2012) and the International Baccalaureate (Consultant for Computer Science studies, 2008–2022). Her research focuses on structural proof theory, proof complexity, and computational logic, with contributions to efficient proof systems and normalization techniques. Education and Qualifications: PhD in Computer Science, University of Ancona, 1997 French Qualification 'Maitre des Conférences' in Computer Science (2004 and 2008) Fellow of the Higher Education Academy, 2022 Research Interests: Proof Theory and Structural Proof Systems Deep Inference and its Applications Computational Logic and Concurrency Linear Logic and Proof Complexity Key Contributions: EPSRC-funded projects on efficient proof systems and complexity in deep inference Development of quasipolynomial normalization procedures in deep inference Publications on proof search structures, cut-elimination, and analytic proof systems Awards: Recipient of the French 'Maitre des Conférences' qualification Fellow of the Higher Education Academy

Rolf SchwitterView profile

Senior Lecturer

• Natural Language Processing
• Formal Language Processing
• Controlled Natural Languages
• +4 more

Rolf Schwitter is a Senior Lecturer at the School of Computing, Macquarie University. His research focuses on natural and formal language processing, including controlled natural languages, answer extraction, knowledge representation, probabilistic logic programming, and Semantic Web technologies. He holds an h-index of 13 with over 944 citations. His work emphasizes bridging human-readable and machine-processable systems, particularly in legal and technical domains. Key research contributions include developing frameworks like PENG ASP for declarative programming in natural language, smart contract systems, and hybrid explainability tools like HESIP. He leads projects such as Policy Automation: Reconstructing Policy Documents for Objective Decision Making (2018–present) and has collaborated on initiatives like TwitterNews+ for real-time event detection. Research Themes: Controlled Natural Languages, Smart Contracts, Explainable AI, Legal Informatics Notable Projects: User-guided legal document processing, error-free smart contracts, hybrid prediction explanations Received the 2023 Faculty of Science and Engineering Award for Inter-School Collaboration. Active in academic publishing with over 117 research outputs spanning conferences, journals, and book chapters.