Find Academic Supervisors

Christine LargouëtView profile

Associate Professor

• Artificial Intelligence
• Explainable AI
• Timed Behavioral Models
• +5 more

Christine Largouët is a tenured Associate Professor in Computer Science at Institut Agro Rennes-Angers , where she leads the Computer Science Teaching Unit since 2006. She is an associate researcher with the DREAM team at IRISA (Institute for Research in Computer Science and Random Systems) and part of the LACODAM team at IRISA/INRIA. Her academic journey includes teaching positions at University of New-Caledonia (2003-2005) and research collaborations with INRAE, IRISA, and INRIA. HDR : Université de Rennes 1 (2019) PhD : Université de Rennes 1 (2000) Her research bridges Artificial Intelligence with Agroecology , focusing on: Learning Timed Behavioral Models Explainable AI for decision transparency Complex Systems Modelling and Analysis Data-Driven Decision Support frameworks Applications to Ecosystem Management and Precision Livestock Farming She employs formal methods like Timed Automata and Model-Checking to analyze agricultural systems and animal behavior patterns. Recent publications demonstrate her work on: Explainable AI interfaces for behavioral data interpretation Timed Automata for agricultural system modeling Machine Learning applications in swine nutrition and welfare Intrusion Detection Systems for agricultural IoT Environmental Modeling with formal verification Her methodological contributions include persistence-based discretization and role-adaptive explanation frameworks.

• Mathematical Logic
• Fibring of Logics
• Modal Logic
• +6 more

Maria Cristina De Sales Viana Serôdio Sernadas is a distinguished professor at Instituto Superior Técnico (IST), Universidade de Lisboa, specializing in mathematical logic and its applications. Her academic career spans several decades with significant contributions to the field of logic systems. Research Interests Professor Sernadas' research focuses on the theoretical foundations and practical applications of logical systems. Her primary areas of interest include: Fibring and combination of logics Modal and intuitionistic logic Probabilistic and quantum logic Proof theory and decidability Temporal and evidence-based logics Applications of logic to computer science and quantum computing Scientific Contributions With over 100 publications spanning more than three decades, Professor Sernadas has established herself as a leading figure in mathematical logic. Her recent work (2021-2025) continues to advance the field with innovative approaches to logical systems, particularly in the areas of evidence logic, quantum observations, and complexity measures in proof structures. She has co-authored several influential books that serve as important references in the field. Awards and Recognition Professor Sernadas maintains an active scholarly profile with an ORCID identifier (0000-0002-5510-3512) and an ISI ResearcherID (D-2269-2011), reflecting her standing in the academic community. Her work is regularly published in top-tier logic journals and has influenced research directions in multiple subfields of mathematical logic. Academic Supervision Professor Sernadas has supervised numerous PhD students whose work has significantly contributed to the field of logic. Her supervision spans topics including modal logic fusion, probabilistic systems, and first-order logic frameworks, demonstrating her broad expertise and commitment to developing the next generation of logicians.

Rattikorn HewettView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Mining
• +10 more

Rattikorn Hewett is a Professor of Computer Science at Texas Tech University's College of Engineering, where she joined the Department of Computer Science in 2004. Previously, she served as a faculty member at Florida Atlantic University, Washington State University Vancouver, and was a research scientist at the Institute for Human and Machine Cognition. She completed her postdoctoral fellowship at Stanford University (1987-1990) after earning her Ph.D. in Computer Science from Iowa State University in 1986. Her research spans multiple areas of artificial intelligence with significant contributions to intelligent data understanding, model-based reasoning, agent control mechanisms, and blackboard systems. She has developed the SORCER system for classification rule induction from databases and has applied her expertise to diverse domains including software risk assessment, genomic data analysis, and hydrological forecasting. Her work on blackboard systems introduced efficient activation and agenda maintenance mechanisms that significantly improved performance in BB1-like systems. Hewett's publications demonstrate consistent contributions across several domains, with recent work focusing on software risk assessment, bioinformatics applications of machine learning, and intelligent data understanding. Her research shows a clear progression from foundational work in knowledge representation and blackboard architectures to practical applications in healthcare, environmental science, and software engineering. She has successfully secured funding from prestigious sources including NSF, DARPA, ONR, NASA, EPRI, and industry partners like Boeing and IBM. Scientific Awards and Recognition: National Science Foundation Research Initiation Award (1993-1997) Exceptional Professor award from Florida Atlantic University Hewett has established a research program on Capability Engineering at TTU to apply and enhance AI techniques for emerging problems in software automation and security. She has served as a referee for numerous prestigious journals and currently serves on the editorial boards of Advances in Artificial Intelligence, Journal of Computational Intelligence in Bioinformatics, and other scholarly publications. Her teaching portfolio includes graduate courses in Artificial Intelligence, Knowledge Discovery and Data Mining, and undergraduate courses in Algorithms and Formal Language Theory. Her research laboratory has focused on developing practical applications of AI techniques to real-world problems, with notable projects including automated assessment of software risks, classification of genetic phenotypes of Osteogenesis Imperfecta, prediction of Lake Okeechobee inflows, and intelligent domain-specific software synthesis control. These projects reflect her commitment to applying theoretical AI concepts to solve concrete problems in healthcare, environmental management, and software engineering.

• Fuzzy Logic Programming
• Fuzzy Deductive Databases
• Automatic Program Transformation
• +4 more

Pascual Julián-Iranzo is an Associate Professor in the Information Technologies and Systems Department at the Higher School of Computer Science, University of Castilla-La Mancha (UCLM), where he has served since April 2003. He is a founding member and current co-director of the DEC-tau research group focused on Declarative Programming and Automatic Program Transformation. His academic career includes over 27 years of university teaching experience across various positions, with significant contributions to curriculum development including the Degree Curriculum in Computer Engineering (2008/09 and 2009/10). Education Background: Doctor in Computer Science (1994-2000) from Universitat Politècnica de València, Department of Computer Systems and Computation His research centers on declarative programming languages with emphasis on fuzzy logic programming, functional-logic paradigm integration, and automatic program transformation. He pioneered work in proximity-based reasoning systems like Bousi-Prolog and FASILL, developing novel approaches for similarity-based unification, thresholded tabulation, and fuzzy deductive databases. His publications demonstrate consistent innovation in fuzzy logic semantics, particularly in handling truth degrees and semantic similarity measures for knowledge representation. Analysis of his 15 most recent publications reveals a strong trajectory in formalizing fuzzy logic programming frameworks, with increasing focus on practical implementations (2018-2023) after establishing theoretical foundations (2015-2017). Key thematic clusters include proximity-based unification algorithms (2018-2021), semantic frameworks for truth degree management (2016-2017), and real-world applications in natural language processing (2021) and security systems (2017). Research Recognition: Positive evaluation by CENAI for three consecutive research periods: [1997-2002], [2003-2008], and [2009-2014] Consistent publication record with 24 scientific journal articles (16 JCR-indexed) As an academic leader, Julián-Iranzo has supervised 2 doctoral theses and chaired the PROLE 2021 Conference Program Committee. His grant portfolio includes significant European funding, notably the MERINET project (2016-2019) supported by ERDF and MINECO. He has served on 12 conference program committees and reviewed for top journals including Journal of Theory and Practice of Logic Programming. His DEC-tau research group maintains active international collaborations, evidenced by coordination of the UCLM node in the ALFA Lernet Project (2005-2009) and partnership with Argentina's National University of San Luis (2009-2013).

H. M. ZaferView profile

Associate Professor

• Arabian and African Languages
• Historical Linguistics
• Medieval Studies
• +7 more

H. M. Zafer is an Associate Professor at the University of Washington , specializing in Arabian and African Languages through pre-modern networks of knowledge spanning 6th-16th century Horn of Africa and Western Arabian societies. He also serves as a Senior Lecturer in the Program in African Studies. Education: PhD from Cornell University (2014) Zafer's research focuses on historical linguistics, medieval studies, and Islamic studies, particularly analyzing the Ecumenical Community in the context of Red Sea wars and Early Muslim communitarianism. His current projects include The Comrades of the Ship (Early Muslim historiography) and The Ethiopic Quran (16th-century Ge'ez commentary on the Quran). His scholarly contributions bridge interdisciplinary themes in comparative literature, religious studies, and computational methodologies through his earlier work in machine learning applications for bioinformatics and natural language processing. Scientific Awards: Mellon-Sawyer Fellow, University of Notre Dame Katz Fellow, University of Pennsylvania His publications from 1997-2009 explore hierarchical classification, Bayesian aggregation techniques, and neural networks applied to genomic prediction, linguistic analysis, and automated puzzle generation, demonstrating technical expertise in algorithm development and data modeling.

Clark BarrettView profile

Professor

• Formal Methods
• Automated Reasoning
• Verification
• +7 more

Clark Barrett is a Professor in the Department of Computer Science at Stanford University, where he conducts research in formal methods, automated reasoning, and verification. He is affiliated with several research centers including the Stanford Center for Automated Reasoning, Stanford Center for AI Safety, Stanford Agile Hardware Project, and Stanford Center for Blockchain Research. His research focuses on developing formal methods and tools for verifying complex systems, with particular emphasis on satisfiability modulo theories (SMT), verification of neural networks, hardware design verification, and security. His work bridges theoretical foundations with practical applications across multiple domains. Over the past decade, Barrett's research has evolved from foundational work in SMT solving to increasingly diverse applications including neural network verification, hardware verification, and AI safety. His recent publications demonstrate a strong focus on practical verification techniques for real-world systems, particularly in the areas of hardware design, neural networks, and programming languages. The trend shows an expansion from core verification techniques to broader applications in AI safety and secure systems design. 2021 CAV (Computer Aided Verification) Award Barrett leads several major research initiatives and collaborates extensively with industry partners. His work on the Marabou neural network verification framework, SMT-LIB standard, and Symbolic QED verification methodology have had significant impact in both academic and industrial settings. He has supervised numerous PhD students and postdoctoral researchers who have gone on to successful careers in academia and industry. He is a key member of the Stanford Center for Automated Reasoning, which develops foundational technologies for automated reasoning, and the Stanford Center for AI Safety, where he focuses on formal methods for ensuring the safety and reliability of AI systems. His work on the Stanford Agile Hardware Project aims to revolutionize hardware design through formal methods and verification techniques.

Rajeev AlurView profile

Professor

• Formal Methods
• Cyber-Physical Systems
• Programming Languages
• +7 more

Overview Rajeev Alur is the Zisman Family Professor and Director of the ASSET Center for Trustworthy AI at the University of Pennsylvania . His career spans groundbreaking work in formal methods, cyber-physical systems, and programming languages. PhD from Stanford University (1991) Bachelor's degree from IIT Kanpur (1987) Research Interests His research focuses on ensuring safety and trustworthiness in AI systems, particularly autonomous systems like self-driving cars and medical devices. Key areas include: Neurosymbolic integration Temporal logic in reinforcement learning Verification of neural network controllers Trustworthy machine learning Extending formal methods to modern AI Recent Publications Rajeev's recent work (2023-2025) emphasizes compositional verification, neurosymbolic learning, and secure microservice architectures. Notable trends include: Combining formal specifications with deep learning Scalable verification techniques for neural networks Relational query synthesis Application to autonomous systems with neural perception Chordal sparsity models for efficient analysis Scientific Recognition Alonzo Church Award (2016) Knuth Prize (2024) EATCS Award (2025) ACM/IEEE Fellow Advising & Grants He has advised 30+ PhD students now in academia (Georgia Tech, UCSD, etc.) and industry. As lead PI of the NSF Expeditions project ExCAPE , he drove program synthesis research. Currently serves as General Chair for FLoC 2026 . Labs & Teams Directs the ASSET Center and collaborates with Penn's PRECISE Center and PL Club . His work integrates with industry verification tools at Amazon, Microsoft, and Google.

Joaquín AriasView profile

Associate Professor

• Constraint Logic Programming
• Answer Set Programming
• Event Calculus
• +4 more

Joaquín Arias is an Associate Professor at King Juan Carlos University since 2025. His career includes 2020-2025 as Assistant Professor at the same institution and prior pre-doctoral research at IMDEA Software Institute (2013-2020). He has collaborated with institutions like University of Texas at Dallas and Aalto University. Education: Ph.D. in Computer Science (2020, Universidad Politécnica de Madrid) M.Sc. in Computer Science (2015) B.Sc. in Computer Science (2014) M.Arch. in Architecture (2002) Research interests revolve around Constraint Logic Programming , Answer Set Programming , and their applications in Event Calculus , Stream Data Analysis , and Value-Aware Systems . He has developed frameworks like s(CASP) for non-grounded reasoning and Mod TCLP for tabled constraints. Recent articles demonstrate expertise in integrating Large Language Models with logic programming, automated legal reasoning , and real-time system verification . His work emphasizes explainability, constraint handling, and semantic coherence in AI systems. Scientific awards include the best paper prize at CAEPIA 2021 . He has contributed to proceedings as editor for ICLP workshops and authored numerous papers in TPLP , PADL , and AI & LAW .

• Computational Neuroscience
• Machine Learning
• Simulation-Based Inference
• +3 more

Pedro J. Goncalves is a computational neuroscientist and machine learning researcher serving as Group Leader at VIB-NERF and Associate Professor at KU Leuven . His work bridges theoretical neuroscience and advanced machine learning methodologies to study neural computation. Organizational Affiliations VIB-NERF (NERF.be/en#/) KU Leuven (www.kuleuven.be/english/kuleuven) Co-developer of the sbi Python package His research focuses on simulation-based inference (SBI) for modeling neural circuits, with applications in neurodegeneration studies (e.g., Parkinson's disease), uncertainty quantification in brain imaging, and development of open-source tools for computational neuroscience. Key projects involve: Collaborative work with Patrik Verstreken's lab on ML methods for brain-on-chip data analysis Leadership in the SBI Hackathon for improving open-source inference workflows PhD recruitment via VIB.AI International PhD Call 2025 He actively contributes to community initiatives like the SBI Discord Server and interdisciplinary collaborations across Europe. Publications include work on masked variational autoencoders for neural data modeling in Cell Reports and foundational SBI methodologies.

• Predictive Analytics
• Recommender Systems
• AI Applications
• +3 more

Sung-Hyuk Park is an Assistant Professor at KAIST College of Business, focusing on predictive analytics, recommender systems, and AI applications. His work spans marketing technology, healthcare informatics, and computer vision, with recent publications in journals like IEEE Transactions and Biomedical Signal Processing . Current research integrates deep learning, social network analysis, and optimization models across domains.

• Monetary Economics
• Macroeconomics
• Econometrics
• +11 more

Francisco J. Ruge-Murcia is Professor of Economics at McGill University, Montréal, where he also served as Department Chair from 2019 to 2024. He is an elected President of the Canadian Economics Association (2021-2022) and a Senior Houblon-Norman Fellow at the Bank of England (Fall 2025). His research blends macroeconomics, monetary economics, and econometrics to study central-bank behaviour, inflation dynamics, and collective decision-making in policy committees. Education Ph.D. in Economics, University of Virginia, USA M.A. in Economics, University of Virginia, USA B.Sc. in Industrial Engineering, Universidad Industrial de Santander, Colombia Research Focus Professor Ruge-Murcia’s work centres on monetary economics , macro-econometrics , and collective decision-making in monetary policy committees . He develops and estimates nonlinear DSGE models to analyse how central banks set policy, how shocks propagate through production networks, and how price rigidity and skewness risk affect aggregate dynamics. A unifying theme is the interplay between committee governance (voting records, deliberation, chairman influence) and macro-financial outcomes (inflation, bond prices, exchange rates). Recent projects use high-frequency data—including Twitter sentiment—to measure the impact of trade-policy uncertainty on exchange rates. Scientific Awards & Fellowships President, Canadian Economics Association, 2021–2022 Bank of Canada Fellowship Award, 2017–2021 Postdoctoral Research Fellowship, Bank of Spain, 1999–2000 Top-5 % author ranking in RePEc across multiple metrics Grants & Institutional Support His research has been supported by the Bank of Canada Fellowship, the Bank of Spain, and recurring grants from CIREQ. Ongoing collaborations with the Federal Reserve Banks of Richmond and Minneapolis, the IMF, and the Bank of England underpin his international network. Labs & Research Groups He is affiliated with the Centre interuniversitaire de recherche en économie quantitative (CIREQ) and maintains active ties to policy institutions such as the Bank of Canada, the Bank of England, and the Society for Economic Dynamics.

Toni KarvonenView profile

Assistant Professor

• Statistics and Probability
• Applied Mathematics
• Numerical Analysis
• +10 more

Toni Karvonen is an Assistant Professor of Applied Mathematics at LUT University's Computational Engineering Department, School of Engineering Sciences, since September 2024. His research focuses on statistics, applied mathematics, and computational methods with emphasis on Gaussian processes, uncertainty quantification, and kernel-based approximation. He has held postdoctoral positions at the Alan Turing Institute (2020–21) and the University of Helsinki (2021–24). Research Areas: Statistics and Probability, Applied Mathematics, Numerical Analysis, Gaussian Processes, Positive Definite Kernels Key Trends: His recent work explores probabilistic numerics, Bayesian quadrature, kernel methods for uncertainty quantification, and approximation theory in reproducing kernel Hilbert spaces. Email: Toni.Karvonen@lut.fi

Sandro StuckiView profile

Lecturer

• Programming Languages
• Formal Methods
• Type Systems
• +4 more

Sandro Stucki is a Lecturer in the Department of Computer Science and Engineering at Chalmers University of Technology. He has previously worked as an applied scientist at Amazon and as a postdoctoral researcher at Chalmers University of Technology and the University of Gothenburg. He completed his doctoral studies at the Programming Methods Laboratory (LAMP) at EPFL under the supervision of Professor Martin Odersky. His research focuses on programming languages, with particular interest in formal methods, type systems and theory, and the semantics and implementation of domain-specific languages. He applies formal methods and type theory to problems in privacy and security, investigates type safety of Scala and related type systems, and develops type soundness proofs using Agda. His work also includes designing domain-specific languages for modeling probabilistic and stochastic systems, especially biochemical systems. He has contributed to the Scala ecosystem by developing a GNU/Emacs mode for Kappa, a modeling language for systems biology. His recent publications demonstrate strong expertise across programming language theory, formal verification, privacy-preserving systems, and applications in systems biology. His work bridges theoretical foundations with practical applications, particularly in the Scala programming language ecosystem. Stucki is actively involved in academic service, having served on program committees for numerous conferences including ECAI, GPCE, NWPT, and as a steering committee member for the Scala Symposium series. He has also organized several academic events, including the Scala Symposium 2016. He currently teaches courses on Data Science and AI, Fundamentals of Program Development, and Neuro-symbolic AI at Chalmers/Gothenburg University, and has previously taught courses on Parallel Functional Programming, Principles of Concurrent Programming, and Types and Programming Languages.

Jinlong WuView profile

Professor

• Physics-Informed Machine Learning
• Bayesian Inference
• Turbulence
• +7 more

Professor Jinlong Wu is a faculty member in the Department of Mechanical Engineering at the University of Wisconsin-Madison. His research focuses on developing efficient and reliable data-driven modeling and simulation techniques for complex dynamical systems, with applications in multi-scale, multi-physics, and chaotic systems. He holds a PhD from Virginia Tech (2018), an MS from Southeast University (2014), and a BS from Southeast University (2011). Education: PhD in Mechanical Engineering, Virginia Tech, 2018 MS in Mechanical Engineering, Southeast University, 2014 BS in Mechanical Engineering, Southeast University, 2011 His research interests include Physics-Informed Machine Learning, Bayesian Inference, Turbulence, Computational Fluid Dynamics, Climate Dynamics, and Uncertainty Quantification. He has developed frameworks like CGKN and ChronoLLM, advancing data assimilation and digital twin technologies. Recent publications highlight contributions to stochastic closure modeling, ensemble Kalman inversion, and neural operator-based approaches for dynamical systems. His work emphasizes bridging machine learning with traditional computational methods to tackle challenges in turbulence, climate, and robotics. Awards: 2018 Paul E. Torgersen Graduate Student Research Excellence Award 2018 Pratt Fellowship 2017 SIAM Travel Award 2016 APS DFD Travel Award 2014 Pratt Fellowship Teaching includes courses such as M E 363 (Fluid Dynamics) and M E 890 (PhD Research and Thesis). No listed advisees are available. His work aligns with interdisciplinary collaborations in computational mechanics and environmental modeling.

• Control Systems
• Fault Detection and Isolation (FDI)
• Electric Drives and Electromechanical Systems
• +3 more

Sanja Antić, PhD , is an Associate Professor in the Department of Electrical Engineering at the Faculty of Technical Sciences Čačak , University of Kragujevac , Serbia. She has been a faculty member since 2001, teaching a broad range of control-systems and electrical-engineering courses. Education PhD in Electrical Engineering, Faculty of Electrical Engineering, University of Belgrade (2016) Dissertation: “Application of Model-Based Failure Detection Methods in Electro-Mechanical Systems” MSc in System Control, Faculty of Technical Sciences Čačak (2009) Thesis: “Simulation and Realization of Voltage-Current Control of a DC Micro Motor” Dipl. Ing. in Industrial Power Engineering, Faculty of Technical Sciences Čačak (2000) Thesis: “Application of Fuzzy Logic in Estimating the Dynamic Model of a DC Motor” Research Interests Her research centres on model-based fault detection and isolation (FDI) for electromechanical systems, with particular emphasis on permanent-magnet DC motors and their associated power-electronic amplifiers. She develops structured and directional residual techniques combined with parameter-estimation algorithms to detect actuator, sensor and multiplicative faults. Additional interests include energy-efficient electric drives , digital control systems , torque-ripple reduction in induction motor drives , and the creation of remote and virtual laboratories for engineering education. Scientific Awards & Recognition “Vuk Karadžić” diploma for exceptional academic performance (primary & secondary school) Top graduate of the 1999/2000 academic year, Faculty of Technical Sciences Čačak Projects & Funding Principal Investigator, Serbian Ministry of Education project “Modernisation of three compulsory courses in Electrical Machines 2, Automatic Control and Electric Drives” (2020-2021) Team member, TEMPUS project “Building Network of Remote Labs for strengthening university-secondary vocational schools collaboration” (2013-2016) Participated in national technological-development projects on energy efficiency of electric motor drives (2011-2014) and prototype development of a 4-axis CNC welding machine (2011-2012) Teaching & Educational Contributions Dr Antić has designed and delivered courses in Automatic Control , Digital Control Systems , Control of Electromotive Drives , Fundamentals of Electrical Engineering and Electric Drives . She co-authored three textbooks and numerous workbooks and practicums that integrate remote-experiment platforms, thereby fostering hands-on learning aligned with Industry 4.0 concepts.