Find Academic Supervisors

• Educational Technology
• Computer Science Education
• Artificial Intelligence in Education
• +3 more

Joana Martinho Costa is an Associate Professor at Iscte-University Institute of Lisbon, where she serves as Director of the School of Applied Digital Technologies and coordinates degree programs in digital education. She leads the Distance Learning Implementation Project and is Principal Investigator of the EU-funded BRIDGE project addressing educational inequalities. Her research group affiliations include the ISTAR-Iscte Research Center where she serves as sub-coordinator of Information Systems research. Education: PhD in Education (Information and Communication Technologies), University of Lisbon (2019) Master's in Computer Science Education, University of Lisbon (2012) Bachelor's in Computer Engineering, New University of Lisbon (2010) Additional qualifications: Innovation in Education (Harvard, 2024), Distance Learning (ISCTE, 2022) Her research focuses on educational technology integration , particularly in computer science education, artificial intelligence applications in learning, and digital transformation of educational systems. She investigates technology-enhanced learning environments, programming pedagogy, computer-assisted assessment, and meta-analytical approaches to educational research. Current work explores AI-mediated learning processes and digital equity in education. Her publications demonstrate strong emphasis on meta-analytical studies of educational technologies, empirical investigations of digital learning environments, and interdisciplinary applications of data science in education. Recent work shows increased focus on pandemic-related educational adaptations and AI integration in pedagogical contexts. Awards & Honors: Santander Scholarship for Innovation in Teaching (2023) Iscte Research Awards (2020, 2021, 2022) She currently advises 3 Master's students and has supervised 12 completed Master's theses on topics including educational technology implementation, cybersecurity training, and AI in education. Leads multiple EU-funded projects including SYNCLUSIVE (labor market inclusion), ManagiDiTH (digital health transformation), and ATHENA (university digitalization). Directs research at the ISTAR-Iscte Center's Information Systems group and coordinates the Digital Education project. Leads international collaborations with universities in Finland, Greece, France and other EU countries.

David VinczeView profile

Researcher

• Fuzzy Systems
• Fuzzy Rule Interpolation
• Reinforcement Learning
• +9 more

David Vincze is a researcher at the Department of Precision Mechanics, Chuo University, Japan. Previously affiliated with the University of Miskolc, Hungary, he has taught courses in Operating Systems, UNIX System Administration, and Modern Information Technologies. His research spans computational intelligence, fuzzy systems, reinforcement learning, and human-robot interaction. PhD in Computer Science Current Position: Researcher, Chuo University Previous Affiliation: University of Miskolc David Vincze's research focuses on Fuzzy Systems , particularly Fuzzy Rule Interpolation (FRI) , and its integration with Reinforcement Learning (e.g., FRIQ-learning). He applies these methods to Human-Robot Interaction (HRI) , drawing inspiration from ethology and animal behavior. His recent work explores applications in Parent-Child Interaction Therapy (PCIT) , using social robots to support child development and family communication. He also investigates Operating Systems Security , especially Linux kernel mechanisms. His publications show a consistent trend in developing and optimizing FRI-based learning methods for real-time, embedded, and robotic applications. He has worked on performance optimization, parallelization, and knowledge injection in FRIQ-learning, aiming to make fuzzy reinforcement learning more efficient and applicable in dynamic environments. Scientific Contributions: Developed FRIQ-learning: Fuzzy Rule Interpolation-based Q-learning Applied ethological models to robot behavior Designed fuzzy automata for HRI Optimized fuzzy inference for real-time systems Explored indoor localization for HRI experiments Vincze has advised students such as Alex Tóth and has been involved in student supercomputing teams (ASC 2014–2017). He has developed various Linux tools, including security extensions for Apache, video filters for MPlayer, and drivers for TV tuner cards. He maintains active research profiles on ResearchGate, Google Scholar, and Scopus. He leads projects in Mihoko Niitsuma's Lab at Chuo University, focusing on social robotics, behavior modeling, and real-time interaction systems.

• Logic and its applications in computer science
• Solving combinatorial problems
• Scheduling
• +4 more

Mateu Villaret Auselle is a Professor at the Universitat de Girona in the Department of Computer Science, Applied Mathematics and Statistics. He is affiliated with the Logic and Artificial Intelligence Research Group (LAI). Degree in Computer Science from UPC (1996) Doctor in Computer Science from UPC (2004) His research focuses on logic and its applications in computer science, particularly in solving combinatorial problems, scheduling and planning. He has been actively involved in the Logic and Artificial Intelligence Research Group (LAI). He has held teaching and management roles at Universitat de Girona since 1999, following a brief period at UPC (1996-1997) and UdG scholarship holder status (1997-1999).

Joseph M. HellersteinView profile

Professor

• Databases
• Distributed Systems
• Programming Languages
• +3 more

Joseph M. Hellerstein is the Jim Gray Professor of Computer Science at the University of California, Berkeley, within the College of Engineering. His research spans databases, distributed systems, and programming languages, with a focus on data-centric systems. He actively bridges academia and industry as co-founder of startups like Aqueduct and Trifacta, Faculty Fellow at Sutter Hill Ventures, and former Director of Intel Research, Berkeley. Research Interests: His work addresses foundational challenges in data systems, including cloud programming models, distributed dataflow frameworks, and verified synthesis of coordination mechanisms. He integrates human-computer interaction, AI systems, and security into data-centric computing paradigms. Selected Publications (2021-2025): These span choreographic programming for distributed dataflow, equality saturation techniques, CRDT synthesis, and declarative cloud models. Keywords include distributed systems, formal verification, Rust, and compiler design. Scientific Awards: ACM SIGMOD Codd Innovations Award ACM Fellow Sloan Fellowship Six Test of Time Awards Advising: He oversees graduate students in the Berkeley Data Systems group, including Nithin Chalapathi, Audrey Cheng, David Chu, and Zongheng Yang. His team collaborates with labs such as EPIC, Sky, and Slice at UC Berkeley.

• Logic Programming
• Computational Logic
• Abstract Interpretation
• +3 more

Baudouin Le Charlier is a researcher with a focus on computational logic, programming languages, and system security. He has led multiple research projects, including sequential analysis under Unix, automated IT resource monitoring, and abstract interpretation for declarative languages. His work spans 1992–2008, with a strong emphasis on logic programming and formal semantics. Key Research Areas: Logic Programming, Abstract Interpretation, Data Stream Processing, Unix Systems, and Programming Language Interoperability. Notable Projects: Asax (Sequential Analysis), Survauto (IT Monitoring), COMPULOG-Net (Computational Logic Network), ABILE (Abstract Interpretation Tools). His 15 most recent publications (1995–2008) highlight trends in declarative semantics, interoperability mechanisms, and distributed audit analysis. These works often intersect with constraints, tools, and formal modeling.

Tiark RompfView profile

Associate Professor

• Programming Languages
• Compilers
• Software Engineering
• +13 more

Tiark Rompf is an Associate Professor in the Department of Computer Science at Purdue University, where he has been a faculty member since Fall 2014. He is the founder and director of the Purdue Center for Programming Principles and Software Systems (PurPL), a research group focused on next-generation software systems that integrate data-driven algorithms, also known as "Software 2.0". His academic background includes a PhD from École Polytechnique Fédérale de Lausanne (EPFL), an MS from Universität zu Lübeck, and a BS from Universität Bremen, all in computer science. His research spans programming languages, compilers, systems, databases, machine learning, and AI. He is a leading figure in meta-programming, domain-specific languages (DSLs), and compiler design, particularly through his work on Lightweight Modular Staging (LMS). His interdisciplinary work bridges theory and practice, aiming to make high-level programming languages performant and scalable. The most recent publications highlight his focus on advanced type systems (e.g., reachability types), query languages (e.g., Flan, Rhyme), compiler IRs, and the integration of machine learning and data management systems. These works appear in top venues like POPL, OOPSLA, ICFP, SIGMOD, and ECOOP, indicating sustained influence and innovation in programming language research. Among his scientific contributions, he has no explicitly listed awards in the provided text, but his leadership in founding PurPL and his extensive publication record in premier conferences are significant recognitions of his impact. He advises a group of active researchers and students, many of whom are co-authors on his recent papers. His lab, PurPL, fosters collaborative research in programming principles and software systems, often involving meta-programming, differentiable programming, and system-level optimizations. He has led or co-led multiple collaborative projects, including work on Flare (a native compiler for Apache Spark), HACCLE (secure multi-party computation), and AutoGraph (imperative-style differentiable programming).

• Logic Programming
• Abstract Interpretation
• Program Analysis
• +7 more

J. M. Howe is a Professor in the Department of Computer Science at City, University of London, with an active research career spanning over 25 years from 1997 to the present. With an ORCID identifier 0000-0001-8013-6941, Howe has established himself as a prominent researcher in formal methods, logic programming, and the intersection of symbolic and neural approaches to artificial intelligence. Howe's research interests center on abstract interpretation, program analysis, constraint solving, and more recently, neural-symbolic AI. His early work focused on logic programming and abstract domains, particularly systems of two variables per inequality (TVPI), which has remained a consistent thread throughout his career. In recent years, he has expanded into explainable AI, rule extraction from neural networks, and the development of the Neural Multi-Space (NeMuS) framework for integrating symbolic and neural approaches. His research has practical applications in program verification, security (particularly cross-site scripting detection), and agricultural technology. Analysis of Howe's publication trajectory reveals a consistent focus on foundational programming language theory that gradually evolved toward machine learning applications while maintaining strong theoretical underpinnings. His work shows increasing collaboration with researchers in neural networks while preserving his expertise in formal methods, creating a distinctive research niche at the intersection of symbolic and connectionist AI approaches. Howe has made significant contributions to the understanding of widening operators in abstract interpretation, constraint solving techniques, and methods for extracting interpretable rules from black-box neural networks. His research demonstrates both theoretical depth and practical applicability across multiple domains including software verification, security analysis, and computer vision applications. His collaborative work spans multiple institutions, with frequent collaborations with researchers like A. King, M. Brain, F. Mereani, and E. Robbins. This pattern of collaboration demonstrates his integration within both the formal methods and machine learning research communities.

• Program Analysis
• Software Verification
• Debugging
• +13 more

Manuel V. Hermenegildo is a Distinguished Professor at the IMDEA Software Institute and a Full Professor in the Department of Artificial Intelligence at the Technical University of Madrid (UPM). He is the founding Scientific Director of IMDEA Software and leads the CLIP Lab, a joint research unit between UPM and IMDEA Software focused on computational logic, programming languages, and parallelism. His career spans leadership in national research policy, academic positions in Spain and the U.S., and extensive international collaboration. PhD in Electrical and Computer Engineering, University of Texas at Austin (1986) M.S. in Electrical and Computer Engineering, University of Texas at Austin (1984) M.S. in Electrical Engineering, Technical University of Madrid (1981) His research centers on global program analysis, verification, debugging, and optimization for functional and non-functional properties such as time, memory, and energy. He has pioneered work in abstract interpretation, partial evaluation, constraint and logic programming, parallelizing compilers, and computational logic. His methodologies support the development of safe, efficient, and reliable software systems. The integration of static and dynamic analysis, resource-aware compilation, and assertion-based debugging are recurring themes in his work. His recent publications emphasize incremental and modular analysis, assertion checking, energy-aware computing, and verification via Horn clauses. Trends show a strong focus on making static analysis more practical, scalable, and integrated into real-world development workflows, particularly for logic and declarative languages. He frequently publishes in top venues such as TPLP, SCP, TOPLAS, and POPL. ACM Fellow Julio Rey Pastor Prize (2006) Aritmel Prize (2005) Elected member, Academia Europaea (2010) Test of Time Award, ICLP 2017 Best Paper Awards at ICSOC 2011 and ICLP 2005 Fulbright-Hays Scholarship Hermenegildo has supervised numerous PhD students and postdocs, many of whom are now leading researchers. He has been principal investigator in over 50 research projects funded by the EU, Spanish government, NSF, and industry partners. His leadership extends to national research policy, where he directed Spain’s National Research Directorate and launched the Ramón y Cajal fellowship program. He has served on tenure and evaluation committees for institutions worldwide and is active in shaping research agendas through roles in Informatics Europe and EIT Digital. He leads the CLIP Lab, which develops advanced program analysis tools and systems such as the Ciao programming environment, CiaoPP static analyzer, and LPdoc documentation generator. The lab fosters collaboration across academia and industry, focusing on next-generation program development tools, verification, and resource-aware computing.

Seth Copen GoldsteinView profile

Associate Professor

• Programmable Matter
• Claytronics
• Reconfigurable Computing
• +6 more

Seth Copen Goldstein is an Associate Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. His research spans multiple interdisciplinary domains focusing on ensembles - large collections of interacting agents. He has made significant contributions to reconfigurable computing, molecular-scale devices, programmable matter, and more recently, the impact of technology on labor markets and alternative monetary systems. Education: PhD in Computer Science, University of California at Berkeley BS, Princeton University Goldstein's research agenda has evolved from reconfigurable computing to molecular-scale devices and programmable matter, and most recently to social technology and alternative monetary systems. His early work focused on compiling high-level programming languages directly into configurations for computing ensembles of gates. He then investigated molecular-scale circuits and programmable matter - ensembles of computing elements that can be programmed to change physical properties. Since returning from a startup, he has shifted focus to ensembles of people, studying technology's impact on labor markets and innovation, and developing social technologies to reduce poverty and inequality. His publication trends show a consistent focus on modular robotics, distributed systems, and programmable matter. The research spans theoretical foundations, hardware implementation, and programming languages for large-scale ensembles. Recent work emphasizes energy efficiency, distributed algorithms, and novel programming paradigms for heterogeneous systems. The interdisciplinary nature of his work connects computer science, electrical engineering, and social sciences. Research Leadership: Claytronics project - creating ensembles of cooperating submillimeter robots Development of Meld and LDP programming languages for ensemble systems Phoenix project - computing without processors Building on Local Trust (BoLT) initiative Goldstein has advised numerous PhD students who have gone on to positions at major technology companies including Microsoft Research, Sun Microsystems, and NEC Labs. His work has been supported by significant research grants focusing on modular robotics, programmable matter, and distributed computing systems. He has taught core computer science courses including Compiler Design, Introduction to Computer Systems, and Cloud Computing.

• 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).

Boon Thau LooView profile

Professor

• Distributed Systems
• Networking
• Blockchain
• +2 more

Boon Thau Loo is the RCA Professor in the Department of Computer and Information Science at the University of Pennsylvania , with a secondary appointment in Electrical and Systems Engineering . He leads the NetDB@Penn research group and is an active member of the Distributed Systems Lab and the Penn database group . As the Senior Associate Dean for Graduate Education and Global Initiatives , he oversees academic programs and international collaborations. PhD in Computer Science (2006, UC Berkeley) M.S. in Computer Science (2000, Stanford University) B.S. in Computer Science with highest honors (1999, UC Berkeley) Boon Thau Loo's research spans Distributed Systems , Blockchain , and Networking . Key areas include Byzantine Fault Tolerance , Smart Contracts , Network Provenance , and Cloud Infrastructure . His work addresses challenges in Internet-scale Query Processing , Data Management in Disaggregated Environments , and Security for Distributed Systems . Recent publications focus on Adaptive Blockchain Systems , BFT Consensus , Smart Contract Optimization , and Distributed Transaction Processing . He explores Reinforcement Learning for Consensus , Data-dependent Order-Fairness , and Practical Declarative Networking to enhance scalability and security. His accolades include the Lindback Award for Distinguished Teaching (2022), Spira Award for Excellence in Teaching (2021), and Best Paper at EDBT (2020). He has been recognized for NSDI Outstanding Paper (2024) and International Student Advocate of the Year (2024). Boon Thau Loo has mentored 17 PhD students , 5 of whom have won dissertation awards . He co-founded two startups: Gencore Systems (Netsil) , acquired by Nutanix Inc. (2018), and Termaxia , acquired by Frontiir (2020). He oversees Penn Engineering's 6,000 graduate students and has launched initiatives like MCIT Online , MSE-DS Online , and MSE-AI Online .

Dan RothView profile

Professor

• Machine Learning
• Natural Language Understanding
• Computational Learning Theory
• +4 more

Dan Roth is the Eduardo D. Glandt Distinguished Professor at the University of Pennsylvania in the School of Engineering and Applied Science , Department of Computer and Information Science . His research integrates machine learning and inference methods to advance natural language understanding, focusing on semantic modeling, information extraction, and constrained conditional models. Research Interests : Dan’s work spans machine learning , inference methods , and their applications in natural language processing , including semantic role labeling, co-reference resolution, and textual entailment. He emphasizes incidental supervision and declarative learning-based programming via tools like LBJava and Saul. Article Trends : Recent publications highlight multimodal learning , LLM uncertainty quantification , and temporal table reasoning , with keywords like Computer Science , Artificial Intelligence , and Medical Informatics . Subfields include vision-language models , hybrid SQL-LLM systems , and multilingual guardrails . Scientific Awards : DARPA Information Science and Technology Advisory Panel (2020) IJCAI John McCarthy Award (2017) Fellow of AAAS (2014) University of Illinois College of Engineering Advising Award (2013) Fellow of ACL (2012) Fellow of ACM (2011) Best Student Paper at CoNLL (2011) Best Paper at Army Science Conference (2010) Fellow of AAAI (2009) Xerox Faculty Research Award (2005) Advising and Grants : Dan has advised PhD students such as Sihao Chen and Haoyu Wang . His projects include BLINK (multimodal LLMs) and DiverseAgentEntropy (LLM uncertainty quantification). Labs and Teams : Dan leads the Cognitive Computation Group , which focuses on computational theories of intelligence and their implementation in machine learning and NLP systems.

• Condensed Matter Physics
• Nanomaterials Science
• Solar Energy Conversion
• +3 more

Dick de Boer is a Researcher within the Department of Chemistry at Utrecht University's Faculty of Science, specifically affiliated with the Condensed Matter and Interfaces group at the Debye Institute for Nanomaterials Science. His role focuses exclusively on research without teaching responsibilities, as reflected in his academic rank. Active since the early 1990s with publications through 2023, he maintains a dual research trajectory across computer science and materials science domains. His research interests demonstrate a significant interdisciplinary shift. Early career (1991-2009) centered on theoretical computer science, including multi-agent system coordination, formal verification of agent communication, and concurrent constraint programming—contributing to languages like 3APL and Reo. Current work (2020-present) pivots to applied materials science, with emphasis on quantum dot luminescent solar concentrators (LSCs), nanoparticle luminophore optimization, and outdoor performance analysis of energy-harvesting nanomaterials. Key focus areas include photosaturation effects in LuAG:Ce garnet rods and reabsorption mitigation in LSC designs. Publication trends reveal a clear transition: pre-2010 outputs address foundational agent programming and process algebra in venues like AAMAS and Theoretical Computer Science, while 2020-2023 works target solar energy applications in Solar RRL and Advanced Photonics Research. Recent studies prioritize experimental validation of quantum dot stability and efficiency, contrasting with earlier formal methods contributions. Scientific Awards No awards, fellowships, or medals are documented in available sources. Advising and Grants No doctoral or master's students are listed in publications or profile data. Research funding sources are not referenced in provided texts. Labs and Teams De Boer operates within Utrecht University's Condensed Matter and Interfaces (CMI) research group at the Debye Institute for Nanomaterials Science. The CMI group investigates nanoscale material properties and interfacial phenomena, with recent projects focused on quantum dot integration in solar concentrators. Collaborations span chemistry (e.g., de Mello Donega), physics (e.g., Vanmaekelbergh), and engineering teams (e.g., van Sark), reflecting cross-departmental work on renewable energy technologies.

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.

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.