Find Academic Supervisors

• Educational Technology
• Instructional Psychology
• Scientific Reasoning
• +6 more

Jan Elen is a full Professor at KU Leuven, specifically within the Instructional Psychology and Technology department of the Faculty of Psychology and Educational Sciences. They are also a member of DigiSoc – KU Leuven Institute for Digital Society and LIVO – KU Leuven Institute for Educational Research. Jan Elen's research interests focus on: Educational Technology and Digital Learning Instructional Psychology and Knowledge Scientific Reasoning and Argumentation in Education Educational Curation and Resource Management Teacher Education and Professional Development Assessment Methods and Educational Measurement Current research projects include Sabbatperiode Jan Elen: Fundamenten voor onderwijskundig redeneren (2023-2024), ICT in de lerarenopleiding: affordance versus daadwerkelijk gebruik in geselecteerde lerarenopleidingen in Ethiopië (2023-2027), and Naar een complementariteit tussen leraar en GenAI in de rol van de leraar als ontwerper van leeromgevingen (2022-2026). Their recent publications demonstrate a strong focus on the intersection of educational psychology, technology integration, and instructional design, particularly examining how teachers and students interact with digital learning environments and develop scientific reasoning skills, with emerging work on generative AI applications in education. Jan Elen serves in various academic capacities: Observer of the POC Criminological Sciences Observer of the POC Rights Member (as ZAP) of the PPW Faculty Council Member of the Assessment Committee of the Faculty of Psychology and Educational Sciences Observer of the OC Master of Psychology: Theory and Research Jan Elen teaches courses related to Leren in maatschappelijk betrokken onderwijs (Learning in socially engaged education) and supervises student placements in psychology and educational sciences across multiple formats including distance learning.

• Sustainable Manufacturing
• Industry 4.0
• Industry 5.0
• +5 more

Morteza Ghobakhloo is a Senior Lecturer and Researcher at Uppsala University , affiliated with the Department of Civil Engineering and Industrial Engineering (Industrial Engineering) and the Institute for Research on Conflicts of Goals in Sustainable Social Transition . His email is morteza.ghobakhloo@angstrom.uu.se . He focuses on digital transformation, sustainability, and human-centric technologies. Research Interests: Morteza’s work bridges Industry 4.0/5.0 , Sustainable Manufacturing , and Generative AI applications. His studies explore blockchain, big data analytics, and smart technologies in supply chain resilience, energy efficiency, and organizational innovation. Article Trends: Recent publications highlight Industry 5.0’s role in sustainable supply chains, AI-driven healthcare optimization, and blockchain for socioenvironmental solutions. He employs hybrid methodologies like PLS-fsQCA, ANN, and simulation modeling across sectors including energy, healthcare, and tourism.

• Software Engineering
• Information Security
• Computer Systems
• +5 more

Hironori Washizaki is a Professor at Waseda University's School of Fundamental Science and Engineering, Department of Information and Computer Science, and serves as Director of the Global Software Engineering Laboratory. He also holds a visiting professorship at the National Institute of Informatics and serves as outside director at SYSTEM INFORMATION CO.,LTD. and eXmotion Co., Ltd. With a Doctorate in Information and Computer Science from Waseda University (2003), he has established himself as a leading researcher with 384 publications and an h-index of 36 according to Google Scholar. His research spans multiple domains including software engineering methodologies, security patterns, programming education, and the application of machine learning to software development. His work has significantly contributed to the fields of software patterns, quality assurance, and educational tools for programming. With over 20 years of academic experience, his career progressed from Research Associate (2002-2004) to Assistant Professor (2004-2008), Associate Professor (2008-2016), and Professor (2016-present). Washizaki's recent publications demonstrate a strong focus on applying AI and machine learning techniques to software engineering challenges, including prompt engineering patterns, program repair methods, and vulnerability assessment. His work bridges theoretical research with practical applications in both educational and industrial contexts, particularly in B2B software development and programming education for diverse age groups. KDDI Foundation Award (2022) Spirit of the Computer Society Award (2022) Distinguished Contributor, IEEE Computer Society (2022) IEEE Computer Society Golden Core Member (2022) Fellow, International Academy, Research, and Industry Association (2022) Computer Research Contribution Award, APSCIT (2016) Washizaki has served as chair of the IEEE CS Japan Chapter and SEMAT Japan Chapter, director of ACM-ICPC 2014 Asia Regional Tokyo Contest, and Convenor of ISO/IEC/JTC1/SC7/WG20. His editorial work includes positions at IEICE Transactions on Information and Systems and International Journal of Software Engineering and Knowledge Engineering. His leadership extends to programming education initiatives like SamurAI Coding, demonstrating his commitment to developing the next generation of software engineers.

• Employment
• Human Resources
• Management
• +9 more

Holly Patrick Thomson is a Lecturer in the Business School at Edinburgh Napier University , where she contributes to research and teaching in management, human resources, and social informatics. She is affiliated with the Centre for Social Informatics and the Centre for Business Innovations and Sustainable Solutions , focusing on the dynamics of creative industries and freelance work. PhD research and supervision in organizational identity, labour precarity, and digital communities. Active involvement in interdisciplinary projects bridging computing and management. Regular participation in academic workshops and conferences. Her research interests center on creative freelancers , online occupational communities , and peer advice systems . She investigates how digital platforms enable solidarity and knowledge exchange among precarious workers, particularly in the wake of the pandemic. Her work combines qualitative methods such as netnography with applied design research to develop tools supporting freelance well-being and professional development. Recent publications highlight trends in digital transformation within creative sectors, organizational identity in cultural institutions, and labour ethics in industries like craft beer and journalism. Her articles reflect a strong interdisciplinary approach, integrating management theory, sociology, and information science. She has received research funding from the Arts & Humanities Research Council , the British Academy , and the University of Edinburgh . Notable projects include: Scoping an AI Powered Peer Advice System for Creative Freelancers (AHRC, £5,000) A New Generation of Peer Advice Systems – Prototyping the Freelance Advisor App (British Academy, £9,998) Creatives in Crisis (University of Edinburgh, £5,000) Holly supervises PhD students including Alan Brooke , Murad Karimi , and Paul Langford , and collaborates with researchers across disciplines such as computing and sociology. She is involved in lab-like project teams developing digital tools for peer advice and data collection on freelance working conditions. Her work aims to bridge academic research with practical policy implications for creative labour.

Simone Paolo PonzettoView profile

Assistant Professor

• Knowledge acquisition
• Natural Language Processing
• Semantic Web technologies
• +5 more

Simone Paolo Ponzetto is an Assistant Professor (Juniorprofessor) at the University of Mannheim since 2013, affiliated with the Research Group Data and Web Science. His research focuses on Semantic Web technologies, Natural Language Processing (NLP), and knowledge acquisition, particularly leveraging collaboratively built resources like Wikipedia. Prior to Mannheim, he held postdoctoral roles at Sapienza University of Rome and research positions at the University of Heidelberg and Stuttgart. His work includes pioneering projects like BabelNet, a multilingual semantic network. Ponzetto earned his PhD in Computational Linguistics from the University of Stuttgart, with interdisciplinary contributions to coreference resolution, semantic relatedness, and ontology learning. Research Interests: Unsupervised/weakly-supervised knowledge extraction Multilingual ontology learning and semantic networks Lexical semantics (word sense disambiguation, semantic similarity) Discourse semantics (coreference resolution, coherence modeling) Professional Contributions: Guest editor for a Artificial Intelligence Journal special issue on AI and Wikipedia Area chair for EMNLP-CoNLL 2012 and EACL 2014 Program committee member for ACL, AAAI, and other top conferences Lab/Team: Active in the Research Group Data and Web Science at Mannheim, advancing AI and NLP applications in collaborative knowledge systems.

Danijel SkočajView profile

Professor

• Computer Vision
• Machine Learning
• Deep Learning
• +3 more

Danijel Skočaj is Full Professor at the University of Ljubljana, Faculty of Computer and Information Science , and serves as Head of the Visual Cognitive Systems Laboratory . He is an internationally recognized researcher in computer vision, machine learning, and cognitive robotics , with a strong focus on deep-learning solutions for real-world visual perception tasks and their ethical implications. Education: While specific degrees are not listed in the text, Professor Skočaj’s 2002 “Best PhD paper award” confirms he holds a PhD in the relevant field. Research Interests: His work spans Computer Vision & Pattern Recognition Deep Learning & Neural Networks Cognitive Robotics & Autonomous Navigation Visual Anomaly & Surface-Defect Detection AI Ethics & Societal Impact of AI These interests manifest in both theoretical advances and practical systems deployed in industry and public infrastructure. Publication Trends: Recent papers (2020-2024) emphasize deep-learning architectures for defect detection, robotic grasping, autonomous navigation, traffic-sign recognition, and 3-D anomaly detection , demonstrating a clear trajectory toward robust, real-time, and data-efficient visual intelligence. Awards & Honors: Prometheus of Science Award 2021 (Slovenian Science Foundation) Golden Plaque, University of Ljubljana 2020 ARRS National Award for Exceptional Scientific Achievement 2011 & 2022 Multiple Best-Paper awards at ERK conferences (2013, 2017, 2019) Top-downloaded paper recognition, Journal of Intelligent Manufacturing 2020 Grants & Projects: He currently leads or co-leads five major 2025-2028 national and EU projects (RTFM, SMASH, COMET, RoDEO, MUXAD) totaling several million Euros, focusing on advanced computer vision, machine learning for science & humanities, autonomous systems, and explainable AI. Past leadership includes EU FP7 CogX, GOSTOP, ViLLarD, and many ARRS programmes. Laboratory & Team: The Visual Cognitive Systems Laboratory hosts a dynamic group of doctoral and master’s students working on cutting-edge perception systems. The lab’s open-source low-cost robotic platform and datasets are widely adopted for education and research.

Jamal AtifView profile

Professor

• Artificial Intelligence
• Responsible AI
• Privacy Preservation in Machine Learning
• +4 more

Jamal Atif is a Professor at Paris-Dauphine University and holds multiple significant leadership positions including Project Manager for 'Data Science and Artificial Intelligence' at the Institute of Information Sciences and their Interactions (INS2I) of the CNRS, Deputy Scientific Director of 3IA PRAIRIE, Head of the MILES team/project at LAMSADE (UMR CNRS-Université Paris-Dauphine), Co-leader of the Transverse Artificial Intelligence Program at PSL University, and Director of the Dauphine Numérique program. Professor Atif's primary research focuses on the foundations of responsible artificial intelligence, with specific expertise in privacy preservation in machine learning, robustness of deep learning algorithms to malicious attacks, causality, and explainability. His work bridges theoretical foundations with practical applications in security and reliability of AI systems. He has developed innovative approaches to address adversarial vulnerabilities in machine learning models and has made significant contributions to privacy-preserving techniques in data analysis. His publication record demonstrates a consistent focus on robust and trustworthy AI systems, with recent work exploring differential privacy in clustering, adversarial robustness, and explainable AI. The research spans theoretical foundations in logic and knowledge representation to practical applications in finance, healthcare, and computer vision. His publications appear in top-tier venues including Machine Learning journal, Neural Information Processing Systems, and International Joint Conferences on Artificial Intelligence. Scientific Awards: Recipient of two awards from the North American Society of Radiology for his thesis work Professor Atif has co-supervised or is currently supervising around fifteen doctoral students, demonstrating his commitment to mentoring the next generation of AI researchers. His leadership extends to directing major institutional programs including Dauphine Numérique and the Transverse Artificial Intelligence Program at PSL University, where he shapes strategic research directions in AI. He leads the MILES team/project at LAMSADE, which focuses on foundational aspects of machine learning and artificial intelligence. The team's research spans theoretical aspects of learning algorithms to practical applications requiring robust and reliable AI systems, with particular emphasis on security and privacy considerations in modern machine learning deployments.

Tuukka RuotsaloView profile

Associate Professor

• Brain-Computer Interfaces
• Human-Information Interaction
• Recommender Systems
• +4 more

Tuukka Ruotsalo serves as Associate Professor in the Machine Learning Section at the Department of Computer Science, University of Copenhagen. His research bridges human cognition with computational systems through brain-computer interfaces and physiological computing. As Academy Research Fellow at University of Helsinki (2019-2024), he maintained dual institutional affiliations while leading cutting-edge work in neuro-linguistic modeling and affective relevance. His research focuses on brain-computer interfaces for information retrieval , where he pioneers methods to decode cognitive states from neural signals to improve search systems. Key areas include affective relevance modeling that integrates emotional states into search algorithms, and neuro-linguistic reconstruction that translates brain activity into language. His work on fairness-relevance tradeoffs in recommender systems established Pareto frontier evaluation frameworks now widely adopted in ethical AI research. Recent publications demonstrate how physiological signals like EEG and galvanic skin response can create more adaptive human-information interaction systems. Ruotsalo's scientific recognition includes the prestigious Academy Research Fellow position. His publications in IEEE Transactions on Human-Machine Systems , Journal of the Association for Information Science and Technology , and Communications Biology reveal growing interdisciplinary impact. His advising spans cognitive neuroscience and machine learning students, with notable collaborations across the SCIENCE AI Centre. Current projects include the TreeSense initiative for remote sensing of global tree resources and development of quantum-inspired neural architectures. His lab leverages the department's powerful compute cluster for large-scale physiological data analysis.

• Technology Management
• Innovation
• Digital Transformation
• +4 more

Professor Karim R. Lakhani is the Dorothy & Michael Hintze Professor of Business Administration at Harvard Business School (HBS), specializing in technology management, innovation, digital transformation, and AI. He leads initiatives like the Laboratory for Innovation Science at Harvard and co-founded the Digital, Data, and Design (D^3) Institute. His research explores open innovation, crowdsourcing, and AI-driven business models, with over 150 peer-reviewed publications. Lakhani holds a PhD from MIT and has taught in HBS's MBA, executive, and online programs. His work bridges academia and industry through partnerships with NASA, Harvard Medical School, and private firms. Education: PhD in Management, MIT SM in Technology and Policy, MIT Bachelor's in Electrical Engineering and Management, McMaster University Research Interests: Lakhani's work focuses on leveraging crowds, open-source communities, and contests to solve complex challenges. He explores how digital technologies reshape industries, emphasizing AI's role in redefining business models. His studies on innovation ecosystems and organizational behavior highlight strategies for competitive advantage in the age of AI. Key Contributions: He co-authored Competing in the Age of AI , a seminal work on AI-driven enterprise transformation. His research on blockchain and digital ubiquity has informed global business strategies. Lakhani's initiatives, including the NASA Tournament Lab, demonstrate practical applications of academic research in real-world innovation. Recognition: Aga Khan Foundation International Scholarship Doctoral Fellowship from Canada's Social Science and Humanities Research Council Advising & Grants: Lakhani advises executives on digital transformation through HBS programs like Competing with Big Data. His grants fund projects on AI ethics, innovation contests, and organizational learning. He has co-developed courses on digital strategy and innovation, blending theory with actionable insights. Labs & Teams: He leads the Crowd Innovation Lab and co-chairs HBS's Business Analytics Program. His collaborations span academia, government, and industry, fostering interdisciplinary problem-solving and scaling innovation.

• Artificial Intelligence and Law
• Computational Argumentation
• Legal Reasoning
• +5 more

Professor Henry Prakken is a leading researcher in Artificial Intelligence and Law at Utrecht University's Department of Computer Science. He holds a prominent position in the Responsible AI research group and has been instrumental in advancing computational models of legal argumentation. With an extensive publication record spanning over three decades, Prakken has established himself as a central figure in the intersection of law and artificial intelligence. Prakken's research interests focus on computational models of argumentation, legal reasoning systems, and the application of AI to legal domains. His work bridges theoretical foundations with practical applications, particularly in case-based reasoning, Bayesian networks for legal evidence, and formal argumentation frameworks. He has made significant contributions to understanding how computational models can represent and analyze legal reasoning processes, with particular attention to precedent-based reasoning and argument strength. His publication record demonstrates consistent research productivity with numerous high-impact papers in venues like Artificial Intelligence, Artificial Intelligence and Law, and proceedings of major conferences including ICAIL and COMMA. Recent work has expanded into evaluating generative AI's legal reasoning capabilities and developing more sophisticated models of argument strength and acceptability. Past president of the International Association for AI & Law (IAAIL) Past president of the JURIX Foundation for Legal Knowledge-Based Systems Past president of the steering committee of the COMMA conferences on Computational Models of Argument Editorial board member of Artificial Intelligence and Law Associate editor of Artificial Intelligence (2017-2022) Prakken has played a significant role in mentoring and shaping the field through his leadership positions and editorial work. His research has influenced both theoretical developments in computational argumentation and practical applications in legal technology, contributing to the growing field of responsible AI in legal contexts.

Sameer SinghView profile

Professor

• Machine Learning
• Natural Language Processing
• Model Interpretability
• +3 more

Sameer Singh is a Professor of Computer Science at the University of California, Irvine's Donald Bren School of Information and Computer Sciences. He also holds affiliations with Linguistics and EECS departments. His research primarily focuses on the robustness and interpretability of machine learning algorithms, along with models that reason with text and structure for natural language processing. Dr. Singh received his PhD from the University of Massachusetts, Amherst in 2014, an MS in Computer Science from Vanderbilt University in 2007, and a BEng in Electrical Engineering from the University of Delhi in 2004. His research interests span machine learning robustness, natural language processing, model interpretability, and knowledge representation. Singh investigates how to make AI systems more reliable and understandable, particularly focusing on testing methodologies for NLP models and developing techniques to improve model behavior. His work bridges theoretical understanding with practical applications in AI safety and reliability. Analysis of Singh's recent publications reveals a strong focus on language model interpretability, bias detection, and model robustness. His work explores how language models process information, where they fail, and how to make them more reliable. A significant portion of his recent research examines the limitations of multimodal models, language model alignment techniques, and addressing social biases in AI systems. Dr. Singh has received numerous prestigious awards including the Kavli Fellowship from the National Academy of Sciences, the NSF CAREER award, UCI Distinguished Early Career Faculty award, and the Hellman Faculty Fellowship. His papers have won multiple awards including at KDD 2016, ACL 2018, EMNLP 2019, AKBC 2020, and ACL 2020. His research group has secured substantial funding from major organizations including the Allen Institute for AI, Amazon, NSF, DARPA, Adobe Research, Hasso Plattner Institute, NEC, Base 11, and FICO. Singh previously served as an Allen Fellow at the Allen Institute for AI (2021-2023) and is currently a co-founder and CTO of Spiffy AI in Seattle. He completed postdoctoral research at the University of Washington after earning his PhD. Dr. Singh maintains an active presence in the AI community through his work on projects like AutoPrompt and Checklist, which have become influential tools for testing and interpreting NLP models. His research continues to shape how the field approaches model evaluation and interpretability.

Arijit KhanView profile

Associate Professor

• Graph Neural Networks
• Blockchain
• Data Management
• +5 more

Arijit Khan is an Associate Professor in the Department of Computer Science at Aalborg University, Denmark. He leads the Data Engineering, Science and Systems group and is affiliated with the Technical Faculty of IT and Design. His research focuses on Graph Neural Networks , Blockchain , Data Management , and AI interpretability . He is the Principal Investigator (PI) of a major project on Data Management, Fundamental Algorithms, and Machine Learning for Emerging Problems in Large Networks (2022–2027). Research Interests : Graph Data Management & Machine Learning Blockchain Transaction Analysis Large Language Model + Knowledge Graph Synergies Healthcare AI (e.g., ICU glucose prediction) Explainable AI for Graph Neural Networks Research Trends : His publications emphasize neuro-symbolic systems , uncertain graph analysis , and AI-driven blockchain insights . Recent work bridges large language models with knowledge graphs and explores GPU performance optimization via shader code analysis. Awards & Grants : No explicit awards listed, but his active research grants include a 5-year project on large network analysis with interdisciplinary applications in life and health sciences. Funding emphasizes algorithmic innovation and data science integration. Labs/Teams : Head of the Data Engineering, Science and Systems research group, focusing on AI for societal impact ('AI for the People') and scalable graph data systems. Collaborations span blockchain analytics, healthcare informatics, and GPU architecture design.

• Natural Language Processing
• Machine Learning
• Medical Language Models
• +4 more

Syrielle Montariol is a Researcher and Course Lecturer at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Natural Language Processing Lab (NLP) under the School of Computer and Communication Sciences (IC). She holds a postdoctoral position and teaches courses related to computational linguistics and AI applications. Her research focuses on advancing NLP, medical language models, multimodal learning, and AI ethics. She works in the INR 240 office and maintains collaborations across EPFL's academic divisions. Research Interests: Her work spans interpretability of AI systems, cross-modal reasoning, medical domain adaptation, sustainability text analysis, and the societal impact of AI. Recent projects include developing explainable models (e.g., global mixture-of-experts frameworks) and benchmarking tools like Vinabench for visual narratives. Publications: Her recent work addresses critical challenges in AI, including vulnerability of higher education to LLMs, medical language model adaptation (Meditron), and robust geo-localization systems. Key themes include ethical AI, multimodal learning, and domain-specific NLP applications. Labs & Teams: She contributes to the NLP lab's initiatives on visual-language models and collaborates with interdisciplinary teams on projects like PAN-RSVQA for remote sensing and PICLe for low-resource NER systems.

Daniel GermanView profile

Professor

• Software Engineering
• Software Evolution
• Open Source Software Development
• +4 more

Dr. Daniel German is a Professor in the Department of Computer Science at the University of Victoria, part of the Faculty of Engineering and Computer Science. He holds a PhD from the University of Waterloo, specializing in software engineering and open source ecosystems. His research focuses on software evolution, open source development practices, intellectual property issues in software systems, and licensing compliance challenges in modern AI/ML environments. German has contributed extensively to understanding dependency management, developer workflows, and legal aspects of software development. His work includes seminal studies on code provenance tracking (e.g., Cregit), library dependency management, and the sociotechnical dynamics of open source communities like the Linux kernel and GitHub ecosystems. German has explored critical topics such as licensing inconsistencies in software projects, ethical implications of AI-generated code, and the integration of open source components into proprietary systems. He is actively involved in software engineering education, examining how students engage with open source projects and the challenges of maintaining code quality in large-scale distributed systems. German’s research has practical implications for both academic and industrial software development practices, addressing real-world issues like security vulnerabilities in dependency chains and the legal risks of AI training data usage.

Zhijing JinView profile

Assistant Professor

• Large Language Models (LLMs)
• Natural Language Processing (NLP)
• Causal Inference
• +7 more

Zhijing Jin is an Assistant Professor at the University of Toronto and a postdoc at the Max Planck Institute for Intelligent Systems, working with Bernhard Schölkopf. She is also a faculty member at the Vector Institute and an ELLIS advisor. Her research focuses on Causal Reasoning with LLMs , Moral Reasoning in LLMs , and AI Safety , with contributions to AI for Science and NLP for Social Good. She leads the Jinesis AI Lab , which explores causal LLMs, multi-agent systems, and ethical AI applications. Education: PhD in Computer Science from Max Planck Institute (Germany) and ETH Zurich (Switzerland) Bachelor’s degree from University of Hong Kong, with visiting semesters at MIT and National Taiwan University Research Interests: Her work bridges causal inference and NLP, addressing robustness, interpretability, and ethical alignment of LLMs. Key projects include Corr2Cause (causal reasoning), GovSim / MoralSim (multi-agent LLMs), and frameworks like NLP4SG for social impact. She advocates for causal mechanisms to tackle AI safety and societal challenges. Recent Articles: Recent work explores political bias in LLMs, ethical dilemmas in multi-agent systems, and causal foundations for trustworthy AI. These studies emphasize real-world applications, such as healthcare NLP and policy analysis. Awards & Recognition: 3 Rising Star Awards 2 Best Paper Awards at NeurIPS 2024 Workshops Fellowships from Open Philanthropy and Future of Life Institute Grants & Mentorship: Funded by NSERC, Schmidt Sciences, and the Cooperative AI Foundation. She mentors ~20 students globally, including PhD candidates in multi-agent LLMs, causal LLMs, and AI safety. The Jinesis Lab offers remote mentorship across institutions like UofT, ETH Zurich, and University of Michigan. Labs & Collaborations: Active collaborations include MPI-IS, Vector Institute, and ETH Zurich’s AI Center. Her lab emphasizes open science, with tools like MoralLens and RouterAttack released publicly.