Find Academic Supervisors

• Computer Vision
• Natural Language Processing
• Multimodal Machine Learning
• +3 more

Dr. Josiah Wang is a Visiting Professor in the School of Computer Science at the University of Sheffield. His research focuses on multimodal machine learning, particularly the intersection of computer vision and natural language processing. He has contributed to advancements in image captioning, object detection, and multimodal machine translation. His work emphasizes techniques like semi-supervised learning, distributional similarity, and visual-linguistic integration. Wang's research has been showcased in venues such as the ImageCLEF challenges, where he contributed to scalable concept annotation tasks and multimodal translation systems. His projects often address critical issues in content selection for image descriptions and the exploitation of visual semantics for improved language generation. While no specific awards or grants are listed, his involvement in organizing and participating in ImageCLEF tasks demonstrates his leadership in collaborative research initiatives. His work bridges theoretical machine learning with practical applications in visual language systems, reflecting a commitment to advancing both technical and interdisciplinary research.

Marcin WojtasińskiView profile

Assistant Professor

• Cognitive Psychology
• Religiosity Studies
• Art Appreciation
• +5 more

Marcin Wojtasiński is an Assistant Professor at the Department of Experimental Psychology, Institute of Psychology, Faculty of Social Sciences, John Paul II Catholic University of Lublin. His academic career at this institution spans multiple research domains. Assistant Professor (2025) Department of Experimental Psychology affiliation Faculty of Social Sciences membership John Paul II Catholic University of Lublin affiliation Dr. Wojtasiński's research interests intersect Cognitive Psychology , Religiosity Studies , and Art Appreciation . His work explores: Religious symbols' dual dimensions (opportunity vs. challenge) Canonical size phenomenon in memory drawing Flow states in board gaming Religious experience's protective role in migration Neural correlates of empathy and art perception Recent publications show increasing focus on network analysis and statistical modeling of complex psychological phenomena. His 2025 work includes vection research, skill-challenge dynamics, and sound predictability effects on attention. Collaboration patterns reveal frequent work with Małgorzata Tatala , Magdalena Szubielska , and other Polish psychologists. While no specific awards are mentioned, his publication record in Scientific Reports , Religions , and PLoS One demonstrates active scientific contribution.

Laurent DoyenView profile

Researcher

• Game Theory
• Automata Theory
• Formal Verification
• +7 more

Laurent Doyen is a CNRS Researcher at the Laboratoire Méthodes Formelles (LMF), ENS Paris-Saclay. He holds a PhD from the Université Libre de Bruxelles (2006) and an HDR from ENS Cachan (2012). His research focuses on formal methods, game theory, automata, and verification of quantitative and probabilistic systems. PhD: Université Libre de Bruxelles, 2006 HDR: ENS Cachan, 2012 Research Interests: Laurent's work centers on algorithms and tools for the verification and synthesis of reliable software, hardware, and embedded systems. His primary areas include game and automata theory, with a focus on discrete quantitative and probabilistic systems, timed and hybrid systems. He investigates synchronization, mean-payoff objectives, and imperfect information in games, contributing significantly to theoretical foundations and practical tools. The recent publications highlight a strong trend in stochastic games, synchronization in Markov decision processes, and quantitative verification. His work spans theoretical computer science, formal methods, and practical applications in system design, often appearing in top venues like LICS, ICALP, and CONCUR. Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: Laurent has advised several PhD students including Mahsa Shirmohammadi, Julien Reichert, Thomas Soullard, and Pranshu Gaba. He leads and participates in multiple research projects such as QuaVerif (PI), IFCPAR SMILeS (coPI), Cassting, ARiSE, and Quasimodo. He has been a Rutherford Visiting Fellow at the University of Warwick and is involved in various academic communities like GAMES, CFV, and GDR-IM. Labs and Teams: He is a key member of the Laboratoire Méthodes Formelles (LMF) at ENS Paris-Saclay and has been associated with the LSV (Laboratoire Spécification et Vérification) in the past. He contributes to the development of tools like Alaska and Alpaga for automata analysis and model checking.

Olaf DimigenView profile

Assistant Professor

• Visual Cognition
• Neuroscience
• Cognitive Psychology
• +3 more

Olaf Dimigen is a tenured Assistant Professor of Experimental Psychology at the University of Groningen, Netherlands. Previously, he held a visiting professorship for Biological Psychology at Humboldt University Berlin (2018-2022) and conducted research at the Max Planck Institute for Human Development. His research focuses on understanding how the brain integrates eye movements with visual perception and cognition, leveraging EEG/eye-tracking co-registration and advanced signal analysis techniques. Key affiliations: Faculty of Behavioural and Social Sciences, Department of Experimental Psychology Education: PhD in Psychology from Humboldt University Berlin (2014) Research interests include: Neural mechanisms of active vision Fixation-related potentials (FRPs) and microsaccade-related activity EEG artifact correction and deconvolution modeling Cross-linguistic reading processes Development of open-source analysis tools (EYE-EEG, UNFOLD, OPTICAT) Selected contributions: Pioneered methods for analyzing temporally overlapping EEG signals using deconvolution (UNFOLD toolbox) Optimized ICA-based ocular artifact removal for free viewing EEG (OPTICAT method) Discovered cognitive modulation of microsaccade-related potentials as attention markers His work bridges cognitive psychology, neuroscience, and computational methods, with applications in language processing, scene perception, and cultural neuroscience.

Bruce DraperView profile

Professor

• Artificial Intelligence
• Machine Learning
• Visual Learning
• +7 more

Bruce Draper is a Professor and Chair of the Department of Computer Science in the College of Natural Sciences at Colorado State University. His work bridges artificial intelligence, machine learning, and computer vision, with a strong emphasis on real-world applications involving visual data and intelligent systems. Research Interests: Draper's research centers on machine learning with a focus on visual learning, adversarial AI, and visual agents. He investigates how AI systems can perceive, interpret, and interact with visual environments through technologies like facial recognition, object tracking, augmented reality, and automated visual communication. His work addresses both the capabilities and vulnerabilities of modern AI, particularly in defending systems against adversarial attacks. Publication Trends: His recent scholarly output reflects a consistent trajectory in advancing computer vision and AI robustness. The articles span topics from adversarial defense mechanisms and visual agent autonomy to scalable learning frameworks and real-time video analysis. Collectively, they emphasize secure, efficient, and context-aware visual intelligence systems grounded in deep learning and representation learning. Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: While no students or grants are explicitly listed, his leadership role as department chair and prior experience as a DARPA program manager suggest extensive involvement in research funding, mentorship, and high-impact project direction. His background indicates likely supervision of graduate students and management of federally funded research initiatives in AI and computer vision. Labs and Teams: Although no specific lab or research group is named, his research scope implies leadership or affiliation with interdisciplinary teams working on AI security, computer vision, and augmented reality systems within the Department of Computer Science at CSU.

• Modeling
• Software Design
• Digital Twins
• +5 more

Antoine Beugnard is a Professor in the Department of Computer Science at IMT Atlantique (formerly Telecom Bretagne), located on the Brest campus, where he has served since December 2007. His academic journey began at ENST-Bretagne (1986), followed by a Doctorate in Computer Science from the University of Rennes 1 in 1993, and accreditation to supervise research in 2005. His educational background includes: Former student of ENST-Bretagne (1986) Doctorate in Computer Science from University of Rennes 1 (1993) Accreditation to supervise research (2005) Professor Beugnard's research centers on software modeling, particularly focusing on the meaning, notations, and properties like composition of models. Since 2017, he has applied his research to the Industry of the Future, specifically digital twins, participating in the "Digital Twin" working group of the Alliance Industrie du Futur. His work also explores static verification of names in heterogeneous languages, communication abstractions, component-based software engineering, and late-binding semantics in object-oriented languages. As a member of Lab-STICC (UMR 6285) and the P4S team, he contributes to the development of Openflexo for model federation. His recent publications demonstrate a strong evolution from foundational work on object-oriented languages and component models toward practical applications in Industry 4.0 contexts, with a pronounced focus on digital twin technology, model federation, and software engineering approaches to complex systems. The research trajectory shows increasing integration of theoretical modeling concepts with real-world industrial applications. Professor Beugnard has supervised numerous doctoral students throughout his career, guiding research in areas including model federation, digital twins, component-based software engineering, and socio-technical systems. His teaching philosophy emphasizes active learning with project simulations addressing both organizational and technical aspects of software development. He is responsible for the "Ingénierie Logiciel des Systèmes Distribués" (ILSD) thematic deepening program and teaches software engineering, UML design, object-oriented programming with Java, and fundamentals like concurrency, distribution, and design patterns. His approach centers on three principles: "explicitez" (make explicit the process and product at all levels), "adaptez" (adapt rules and methods to context), and "justifiez" (justify decisions and adaptations).

• Programming Languages
• Concurrency
• Embedded Systems
• +4 more

Dr. Emil Sekerinski is a Professor in the Department of Computing and Software at McMaster University. He holds the position of Undergraduate Advisor for Computer Science (Jan 1, 2025 – June 30, 2025). His expertise spans programming logic (verification, refinement), concurrency (components, implementation), embedded systems (modelling, analysis), and programming languages (run-time, compilation). He is actively involved in research clusters focused on Digital & Smart Systems. His research interests emphasize formal methods in computer science, concurrency theory, parallel algorithms, and embedded systems design. He has contributed to advancing methodologies for software quality assurance and system reliability through formal specifications and automated verification techniques. Recent work includes efficient synchronization mechanisms for object-oriented programming languages and formal development of delay-tolerant multicast protocols for wireless sensors. Dr. Sekerinski has organized and participated in major conferences such as FMTea'24 (Co-Chair) and ICTAC'24 (Program Committee). His teaching includes advanced courses like CAS 781 Advanced Topics: Advanced Compiler Design and Optimization and SFWR ENG 3BB4 Concurrent System Design . He maintains an active presence in academic outreach through LinkedIn, Instagram, and other platforms. His work bridges theoretical foundations with practical applications, including environmental sensor data analysis and holistic embedded system development. Despite no explicit mention of awards, his contributions to formal methods education and research are widely recognized in his field.

Lars SchützeView profile

Researcher

• Compiler Construction
• Homomorphic Encryption
• Quantum-Classical Computing
• +3 more

Dr. Lars Schütze is a researcher at the Chair for Compiler Construction within the Faculty of Computer Science at Dresden University of Technology (TU Dresden). Holding a PhD in Computer Science from TU Dresden, he currently serves as a PostDoc specializing in domain-specific compilers for verifiable Full Homomorphic Encryption (vFHE) and hybrid quantum-classical computing systems. His academic credentials from TU Dresden include: Bachelor's degree in Computer Science Master's degree in Computer Science PhD in Computer Science (awarded February 2025) Schütze's research centers on advanced compiler design for emerging computational paradigms. His foundational work explores context-oriented and role-based programming languages, focusing on runtime optimization and dispatch mechanisms. Recent efforts pivot toward post-quantum security through homomorphic encryption compilers and hybrid quantum-classical computing frameworks. His research bridges theoretical language design with practical compiler implementation, emphasizing verifiable security and performance efficiency in next-generation computing environments. Publication analysis reveals a clear evolution from context-oriented programming (2017-2020) toward cryptographic compiler development (2022-2025). Early work optimized role-based dispatch systems, while recent publications establish compiler frameworks for Fully Homomorphic Encryption using MLIR infrastructure. His research consistently addresses performance bottlenecks in dynamic language features while transitioning toward quantum-resistant cryptography solutions. Scientific Awards: No awards documented in source materials Dr. Schütze supervises student theses in Homomorphic Encryption and Quantum-Classical Computing frameworks, offering projects spanning Bachelor to Master levels. His research is funded through institutional projects including (verifiable) Full Homomorphic Encryption and Hybrid Quantum-Classical Computation, though specific grant details remain undisclosed. He actively develops compiler infrastructure for encrypted computation and quantum-classical orchestration. As core personnel in TU Dresden's Chair for Compiler Construction, Schütze contributes to the RoSI project (role-based software infrastructures) and leads current initiatives in vFHE. His team collaborates on building domain-specific compiler toolchains that address quantum computing threats through post-quantum cryptographic solutions while advancing hybrid execution models for emerging hardware architectures.

Claudia Poch Pérez BotijaView profile

Researcher

• Cognitive Neuroscience
• Working Memory
• Neurophysiology
• +5 more

Claudia Poch Pérez Botija is a researcher affiliated with the School of Language and Education at Universidad Nebrija. Her work focuses on Psychobiology , particularly in cognitive neuroscience and neurophysiological mechanisms of memory and attention. Doctorate: Universidad Autónoma de Madrid (2015) Thesis: Neural oscillatory activity associated with attention orientation in working memory Supervisors: Dr. Pablo Campo Martínez-Lage, Dr. Luis Carretié Arangüena Key research areas include working memory dynamics , emotional processing , neural oscillations (alpha/gamma), and language cognition . Her studies often employ EEG/ERP techniques to explore memory-perception interactions and bilingual cognitive control. Recent publications reveal trends in: Pattern separation mechanisms during memory encoding Cross-modal integration in illusory perception Emotional primacy effects in memory recognition Lesion studies of temporopolar regions in epilepsy patients Neurogenetic influences on fear conditioning (D2 receptor variants) Alpha/gamma oscillation modulation in attentional processes She contributes to understanding mnemonic discrimination , semantic memory impairment in neurological conditions, and neural correlates of bilingualism . Her work bridges basic cognitive mechanisms with clinical applications in temporal lobe epilepsy. Active in the CINC Nebrija Research Center on Cognition and CEDI Research Group , she investigates interrelations between cognition, education, and individual differences.

Chi-Chun ChouView profile

Professor

• Accounting Information Systems (AIS)
• Blockchain Technology
• Machine Learning
• +7 more

Chi-Chun Chou is a Professor of Accounting in the School of Business at California State University, Monterey Bay (CSUMB). He holds a Doctorate from National Cheng-Chi University in Taiwan and has a unique interdisciplinary background in computer science and accounting. His research focuses on advancing Accounting Information Systems (AIS), particularly in areas such as blockchain, machine learning, data analytics, and XBRL standards. He has published five top-tier peer-reviewed articles in the last five years, including in Journal of Information Systems and Computers & Industrial Engineering , with four ranked as A journals by the ABDC. Dr. Chou has served as an Editorial Board member of the Journal of Information Systems (2023–2025) and as Domain Editor of the Journal of Emerging Technologies in Accounting . He has guest-edited special issues on blockchain (2019–2020) and AI/ChatGPT in accounting (2023–2024). With over 60 manuscript reviews in five years, his contributions validate his leadership in AIS research. He teaches courses such as Auditing, Intermediate Accounting, and Business Analytics, integrating cutting-edge technologies like Python and large language models into his curriculum. Education: Doctorate in Accounting, National Cheng-Chi University, Taiwan. Research Focus: Blockchain applications, smart contracts, continuous auditing, XBRL, and AI-driven accounting processes. Editorial Roles: JIS Editorial Board (2023–2025), JETA Domain Editor, and Guest Editor for multiple special issues. His work bridges computer science and accounting, emphasizing transparency, automation, and innovation in financial reporting systems. Collaborations with professionals and international scholars further highlight his commitment to advancing AIS methodologies.

• Data Mining
• Machine Learning
• Graph Mining
• +9 more

Michalis Vazirgiannis is a Professor in the Department of Informatics at the Athens University of Economics and Business (AUEB), specializing in data mining and machine learning with applications in web and social network analysis. His work bridges theoretical algorithms and real-world scalability challenges. Education: Bachelor Degree in Informatics, National and Kapodistrian University of Athens, 1986 Master (M.Sc.) in Robotics, National and Kapodistrian University of Athens, 1988 Master (M.Sc.) in Knowledge Based Systems, Heriot Watt University, Edinburgh, 1989 Ph.D. in Informatics, National and Kapodistrian University of Athens, 1994 Research Focus: Professor Vazirgiannis pioneers clustering algorithms with subjective/objective validation, distributed feature selection for evolving graphs, and temporal link analysis for dynamic page ranking. His research addresses critical gaps in semi-supervised learning for large-scale web and social networks, emphasizing dimensionality reduction and ranking predictability in temporal contexts. Publication Trends: His 2007-dominated publications reveal a strategic shift toward graph-based web mining, with recurring themes of distributed processing (P2P similarity search), clustering validity frameworks, and semantic web personalization. The work consistently targets scalability bottlenecks in real-world network data. Scientific Recognition: ERCIM Post-doctoral Scholarship (2001) Marie Curie European Scholarship (2006) Leadership & Collaboration: As ERASMUS coordinator for AUEB's Informatics Department and editorial board member of Intelligent Data Analysis journal, he bridges academia and industry. His EU project leadership (FP6 SQO-OSS, Marie Curie NGWeMiS) and program committee roles (IEEE/ICDM 2008, ECML/PKDD 2008) highlight his influence in data mining standardization. International collaborations span INRIA, Fraunhofer, Max Planck, and IBM Research. Technical Innovation: His patent contributions and invited lectures at ECML/PKDD 2006/SIAM/SDM 2006 demonstrate applied impact, particularly in web personalization engines (SEWeP) and evolving graph analytics.

• Parallel Programming
• GPU Programming
• Image Processing
• +5 more

Sándor Szénási is a Professor at the Department of Informatics within the Faculty of Economics and Informatics at J. Selye University, where he serves as the person responsible for the Applied Informatics study program. With over two decades of academic experience, he has established himself as a leading researcher in parallel programming, GPU programming, and image processing, with recent expansion into machine learning applications. Eötvös Loránd University, Faculty of Science and Informatics (2001-2004): Information technology teacher Budapest Polytechnic, John von Neumann Faculty of Information Technology (1997-2001): B. Engineer in Information Technology Óbuda University (2010-2013): PhD in Applied Informatics Habilitation at Óbuda University (2019): Information Science and Technology Professor inauguration at Óbuda University (2022) Szénási's research spans computational methods with practical applications across multiple domains. His early work focused on parallel and GPU programming for image segmentation and heat transfer simulation. More recently, he has integrated machine learning techniques with traditional computational approaches, particularly in metaheuristic optimization, speech processing, and inverse problem solving. His interdisciplinary research bridges computer science with transportation safety, manufacturing, and medical applications. His recent publications reveal a clear evolution toward hybrid computational approaches that combine machine learning with traditional algorithms. There is a strong emphasis on optimization techniques, particularly metaheuristics enhanced with machine learning components. His work spans diverse application areas including speech emotion recognition, autonomous vehicle control, additive manufacturing, and heat transfer simulation, while maintaining a core focus on computational efficiency and parallel processing. Szénási has been actively involved in multiple EFOP-funded research projects including 'Improvement of higher education institutes for better teaching quality and accessibility,' 'Dynamics and control of autonomous vehicles,' and 'Solving the Inverse Heat Conduction Problem with Machine Learning.' His collaborative work with researchers like Gábor Kertész, Zoltán Vámossy, and Imre Felde demonstrates his commitment to interdisciplinary research.

Mandy MaguireView profile

Professor

• Developmental Neurolinguistics
• Language Development
• Socioeconomic Status and Brain Development
• +3 more

Mandy Maguire is the Robinson Family Professor and Assistant Professor in the Department of Speech, Language, and Hearing at the University of Texas at Dallas (UTD), affiliated with the School of Behavioral and Brain Sciences. She directs the Center for Children and Families and leads the Developmental Neurolinguistics Laboratory. Her research focuses on how socioeconomic status (SES) and home environments influence brain and language development in children. She holds a B.A. in Psychology from Pennsylvania State University (1998), a Ph.D. in Developmental Psychology from Temple University (2003), and completed postdoctoral work at the University of Louisville (2005). Her key research areas include the neural mechanisms of word learning, the impact of poverty on language acquisition, and the role of neural oscillations (e.g., theta, beta, gamma frequencies) in cognitive processes. She has secured significant grants, including NSF awards totaling over $1.3 million, to study SES disparities in language development and to support culturally responsive research training. Dr. Maguire has received notable awards, including the UTD Distinguished Teacher in Diversity (2018) and the Møller Undergraduate Teaching Award (2022). Her work is published in journals like Developmental Cognitive Neuroscience and Brain and Language . She mentors students, such as Julie Schneider (now at LSU), and collaborates with institutions like Children’s Hospital of Philadelphia and the Sandbox Learning Company. Labs/Teams: Developmental Neurolinguistics Laboratory, Center for Children and Families Grants: NSF REU Site (2023–2023), NSF SES-Vocabulary Grant (2023), NIH Brain-Tracking Grant (2010–2012)

Manuel SerranoView profile

Professor

• Programming Languages
• Compiler Design
• Web Programming
• +7 more

Manuel Serrano is a senior research scientist at Inria (French Institute for Research in Computer Science and Automation) affiliated with University of Côte d'Azur, based at the Sophia Antipolis research center in the indes team. He has been active in the programming languages research community since at least 2000, serving in numerous leadership roles across major conferences. His research focuses on programming languages, particularly JavaScript and Scheme implementations, compiler design for dynamic languages, web programming, and synchronous reactive programming. His work bridges theoretical language design with practical implementation concerns, with a strong emphasis on performance optimization of dynamic languages. He has made significant contributions to the understanding and improvement of JavaScript compilation techniques, particularly through his work on ahead-of-time compilation. His publication record shows consistent output in top venues including PLDI, OOPSLA, ICFP, and ECOOP, with a recent focus on JavaScript optimization, sealed classes, type system verification, and synchronous reactive programming through his HipHop.js language. His work demonstrates a progression from foundational Scheme compilation (Bigloo) to modern web technologies and JavaScript optimization. He has served as General Chair for the Programming conference in 2018, Program Co-Chair for Programming 2024-2025, and is currently serving as PC Chair for ICFP 2026. His conference service spans over two decades across numerous programming language venues including ICFP, SPLASH, ECOOP, and specialized workshops on Scheme and dynamic languages. As a creator of multiple programming tools and languages, he has developed Bigloo (an optimizing Scheme compiler), Hop (a language for web 2.0 programming), HipHop (an asynchronous programming language), and Flyspell (an on-the-fly Emacs spell checker). His Cold project with University of Montreal focuses on compilation and optimization techniques for dynamic languages like JavaScript and Python.

• Logic
• Philosophical Logic
• Formal Epistemology
• +5 more

Francesco Berto is Professor of Philosophy at both the University of St Andrews (Department of Philosophy) and the University of Amsterdam (Institute for Logic, Language and Computation - ILLC). At the ILLC, he holds primary affiliation with the Formal Semantics & Philosophical Logic research group and secondary affiliation with Epistemology & Philosophy of Science. His work bridges formal logic, ontology, and epistemology with significant contributions to contemporary philosophical debates. Berto specializes in logic, ontology, and formal epistemology with particular focus on hyperintensionality, topic-sensitive semantics, belief revision, counterfactual reasoning, and the logic of imagination. His research develops what he terms 'thick content' theories where propositions have both truth-conditional and topical components. This approach addresses limitations in traditional possible worlds semantics by incorporating subject matter as a fundamental aspect of meaning and knowledge representation. His recent publications (2019-2025) reveal a coherent research trajectory examining how topicality affects logical reasoning across multiple domains. Berto's work demonstrates increasing integration of formal logic with cognitive science, particularly in modeling realistic human reasoning rather than idealized logical competence. Key contributions include formal models of framing effects, the logic of imagination as reality-oriented mental simulation, and solutions to the problem of logical omniscience through topic-sensitive approaches. Berto's scholarly influence extends through his extensive publication record in top philosophy and logic journals including Mind, Journal of Philosophical Logic, Synthese, and Noûs. His work has been cited over 2,400 times according to academic metrics, indicating substantial impact on contemporary philosophical logic and formal epistemology. As an active researcher, Berto supervises PhD candidates and participates in collaborative projects at the intersection of logic, language, and computation. His current work continues to develop formal frameworks that better capture the nuances of human cognition while maintaining rigorous logical standards. His research program represents a significant contribution to bridging the gap between idealized logical systems and the messy reality of human thought processes.