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Paul GrothView profile

Professor

• Intelligent Systems
• Data Provenance
• Data Integration
• +7 more

Paul Groth is a Professor of Algorithmic Data Science at the University of Amsterdam, where he leads the Intelligent Data Engineering Lab (INDElab). He earned his Ph.D. in Computer Science from the University of Southampton (2007) and has conducted research at institutions including the University of Southern California, Vrije Universiteit Amsterdam, and Elsevier Labs. Research Interests: His work centers on intelligent systems for managing large-scale, diverse contextualized knowledge, particularly in web and science applications. Key areas include data provenance , data integration , knowledge sharing , and semantic web technologies . He has contributed to standards like the W3C Provenance Interchange (PROV) and community initiatives such as altmetrics and FAIR data principles. Leadership Roles: Scientific Director of the University of Amsterdam’s Data Science Center Co-Scientific Director of two Innovation Center for Artificial Intelligence (ICAI) labs: AI for Retail (AIR) Lab (UvA-Ahold Delhaize collaboration) Discovery Lab (Elsevier-UvA-VU collaboration) Contributions: Previously led large-scale data integration projects in biomedicine, co-chaired the W3C Provenance Working Group, and co-authored foundational texts like "Provenance: an Introduction to PROV" and "The Semantic Web Primer: 3rd Edition".

• Explainable AI
• Computational Argumentation
• Artificial Intelligence
• +3 more

Xiang Yin is a Researcher at the Department of Computing, Faculty of Engineering, Imperial College London, specializing in Explainable AI (XAI) and Computational Argumentation (CA). His work focuses on Quantitative Bipolar Argumentation Frameworks (QBAFs) and their explainability. PhD in Computing, Imperial College London (2021-2025) MSc in Computer Technology, Beihang University (2017-2020) BSc in Computer Science and Technology, Hebei University of Technology (2013-2017) His research explores the integration of computational argumentation with AI systems to enhance transparency and interpretability. Key contributions include formal analysis of argumentation frameworks, counterfactual explanations, and attribution-based interpretability methods. Recent publications highlight his work on applying argumentation theory to large language models, truth-discovery mechanisms, and theoretical advancements in bipolar argumentation systems. These works span knowledge representation, machine learning, and human-AI collaboration frameworks. Xiang is affiliated with the Computational Logic and Argumentation group (CLArg) and the Argumentation-based Deep Interactive eXplanations (ADIX) project at Imperial College London. Contact: x.yin20@imperial.ac.uk .

Ebrahim BagheriView profile

Professor

• Artificial Intelligence
• Data & Society
• Information Behaviour
• +3 more

Professor Ebrahim Bagheri is a Tenured Full Professor at the University of Toronto's Faculty of Information and co-founder of Reviewerly, an AI-driven platform for scientific peer review. He holds editorial roles at IEEE Transactions on Network Science and Engineering and ACM Transactions on Intelligent Systems and Technology. His research focuses on ethical AI, information retrieval, and responsible AI development. He has secured over $16M in research funding and led initiatives such as the NSERC CREATE program on Responsible AI and the CFREF Bridging Divides Program. Education: PhD (prior institution not specified) Research Interests: Artificial Intelligence, Data & Society, Information Behavior, Social Media, Software & Systems. He emphasizes balancing technological innovation with societal benefits, addressing issues like algorithmic bias and ethical AI practices. Notable Awards: NSERC Synergy Award for Innovation (2019) recognizing industry-academia collaboration excellence. Grants: Includes projects on warranty design, knowledge graph mining, and robust neural retrieval techniques. Labs: Laboratory for Systems, Software and Semantics (LS3).

Wingyan ChungView profile

Professor

• Business Analytics
• Machine Learning
• Social Media Analytics
• +6 more

Dr. Wingyan Chung is a Professor of Computer Science in the Soules College of Business at The University of Texas at Tyler. He holds a Ph.D. in Management Information Systems from the University of Arizona, along with M.S. and BBA degrees from The Chinese University of Hong Kong. His expertise spans business analytics, machine learning, cybersecurity, and human-computer interaction, with a focus on applying these disciplines to social media analytics, data mining, and policy informatics. Education: Ph.D., MIS (University of Arizona); M.S., IT Mgmt (Chinese University of Hong Kong); BBA (Honors) (Chinese University of Hong Kong) Affiliations: Former faculty at University of Central Florida, UNC Western Carolina, University of Texas at El Paso; visiting appointments at University of Arizona, University of Hong Kong, and Chinese University of Hong Kong Dr. Chung has published over 120 refereed articles in top journals and conferences, including Journal of Management Information Systems , Information & Management , and IEEE Computer . His research focuses on social media analytics for policy surveillance (e.g., U.S. border security, public health), cybersecurity frameworks, and business intelligence systems. He has secured over $7M in research funding from NSF, DoD, DHS, and industry partners like Intel and P&G. His awards include Senior Member status from AAAI, ACM, and IEEE, and the Excellence in Research Award from UCF. His teaching spans courses in cybersecurity informatics, machine learning, and database systems, with a focus on integrating real-world applications into curricula. Key research labs include the Cyber Intelligence Lab (UCF) and Business Analytics Lab (UNC Western Carolina). He contributes to editorial roles at International Journal of Engineering Business Management and Informatics , and has served on NSF-funded initiatives like the Computing in Context Project.

Eduardo AlonsoView profile

Professor

• Artificial Intelligence
• Reinforcement Learning
• Computational Neuroscience
• +2 more

Professor Eduardo Alonso is Director of the Artificial Intelligence Research Centre (CitAI) and Department Research Director at City St George's, University of London. His research bridges novel AI techniques with Explainable AI and Artificial General Intelligence, with significant focus on legal and ethical implications. Research spans: Computational neuroscience and evolutionary biology modeling Deep learning architectures for reinforcement learning Mathematical models of emergence in complex systems Industrial AI applications with societal impact Professor Alonso has secured over £1M in funding from Innovate UK, EU EIT-Digital, and US NSF grants. He currently supervises 13 PhD students working on ethical AI, reinforcement learning, and cybersecurity. Recent publications focus on transformer architectures for multi-agent systems, power grid optimization via GNNs, and adversarial robustness in security systems. Awarded the IEEE Computational Intelligence Society Spotlight Paper Award in 2013.

• Decision making
• Knowledge engineering
• Knowledge graphs
• +3 more

Marie-Hélène Abel is a Professor and Director of the Computer Science Department at the University of Technology of Compiègne (UTC), France. She leads the Heudiasyc laboratory (UMR CNRS 7253) and specializes in decision making, knowledge engineering, knowledge graphs, and multi-agent systems. Her work focuses on collaborative systems, organizational learning, and the integration of social data into decision support frameworks. She has been recognized with prestigious awards, including Senior IEEE membership (2017) and the 2017 K2 Trophy for participatory computing innovations. Her research emphasizes the development of systems like the MEMORAe platform, which supports learning ecosystems through collaborative environments and organizational memory. She collaborates internationally with institutions such as the Pontifical Catholic University of Paraná (Brazil) and industry partners including Thales, Alstom Transport, and PSA. Key projects include crisis simulation systems and ontology-based frameworks for Industry 4.0 collaboration. Abel’s academic contributions span over 50 peer-reviewed articles, with recent works addressing context-aware recommender systems, crisis management simulations, and teacher efficacy in digital education. Her leadership roles include directing research projects, overseeing the Computer Engineering Department, and advancing ethical AI practices through initiatives like the Sorbonne.AI network. Education: HDR (Habilitation à Diriger des Recherches) on 'Contribution of Organizational Memories in a Learning Context' (2007) Administrative Roles: Equality Officer, University Antiracism/Anti-Semitism Officer International Collaborations: Brazil, Egypt, and Tunisia

Ken KitsonView profile

Visiting Professor

• Causal Discovery
• Machine Learning
• Artificial Intelligence
• +3 more

Dr. Ken Kitson is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science. His research focuses on causal discovery, Bayesian networks, and machine learning methodologies. He specializes in developing algorithms that infer causal relationships from data, particularly through active learning frameworks that integrate human expert knowledge. Kitson's work emphasizes probabilistic graphical models and their applications in diverse fields such as healthcare, sports analytics, and epidemiology. He has contributed to improving the stability and accuracy of Bayesian network structure learning algorithms, addressing challenges like variable ordering instability and noisy data. His recent research trends highlight advancements in causal machine learning, including the use of large language models like GPT-4 to enhance causal discovery processes. His studies on sepsis and COVID-19 demonstrate practical applications of causal modeling in health informatics. Kitson's publications span algorithmic improvements, surveys of Bayesian network learning techniques, and empirical validations of methods in real-world scenarios. He holds a focus on bridging theoretical advancements with practical implementations in data-driven decision-making contexts.

• Digital Humanities
• Medieval Manuscript Studies
• History of Cultural Heritage Collections
• +4 more

Dr Toby Burrows is a Senior Honorary Research Fellow in the School of Humanities at The University of Western Australia. He holds a BA from the University of Western Australia, an MA from the University of London, a PhD from the University of Western Australia, and a Graduate Diploma in Library Studies from the Western Australian Institute of Technology. His primary research focuses on digital humanities, medieval manuscript studies, and the history of cultural heritage collections. Burrows has directed major projects such as the internationally funded 'Mapping Manuscript Migrations' (2017–2020) and the ARC Linkage Project 'Collecting the West' (2016–2021). He has also held visiting fellowships at institutions including the University of Pennsylvania and King's College London. His expertise includes Linked Open Data, medieval manuscript curation, and the design of e-research infrastructure. Current projects include the 'Mobilising Dutch East India Company collections' (2023–2027) and the Humanities Networked Infrastructure (HuNI). Burrows has contributed to over 100 peer-reviewed publications and datasets, including works on computational historical research and the role of serendipity in knowledge organization systems. He has served on editorial boards for journals like Journal of Open Humanities Data and advised on open access policies for European research. Burrows' grants include Australian Research Council funding for projects on digital humanities infrastructure and cultural heritage digitization. He has also managed significant data archives, such as the Western Australian node of the Australian Data Archive. His work aligns with UN Sustainable Development Goals related to cultural preservation and knowledge sharing.

• Statistical Relational Learning
• Machine Learning
• Probabilistic Graphical Models
• +5 more

Lise Getoor is a Professor in the Department of Computer Science at the University of California, Santa Cruz, within the Baskin School of Engineering. She previously held positions at the University of Maryland, College Park and has established herself as a leading researcher in statistical relational learning and neuro-symbolic artificial intelligence. Her research focuses on developing principled approaches to reasoning and learning with rich, relational data. She has made significant contributions to probabilistic soft logic, statistical relational learning, knowledge graph construction, and the integration of symbolic and neural approaches to AI. Her work bridges theoretical foundations with practical applications in areas including social network analysis, sustainable recommendations, cyberbullying detection, and fair machine learning systems. Analysis of her recent publications reveals a strong trend toward neuro-symbolic integration, with increasing focus on combining neural networks with logical reasoning frameworks. Her research demonstrates consistent innovation in developing scalable algorithms for structured prediction across diverse application domains while addressing critical issues of fairness and interpretability in AI systems. Professor Getoor has advised numerous PhD students who have become active contributors to the field, including Connor Pryor, Charles Dickens, Eriq Augustine, and Varun Embar. Her research has been supported by multiple grants from major funding agencies, though specific details aren't provided in the current data. She leads research in the Statistical Relational AI (StaRAI) Lab at UC Santa Cruz, where her team develops frameworks that combine logical reasoning with probabilistic modeling to address complex real-world problems requiring both structured knowledge and statistical learning.

Vinitra SwamyView profile

Researcher

• Explainable AI
• Machine Learning for Education
• Multimodal Learning
• +4 more

Vinitra Swamy is a Postdoctoral Researcher at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the ML4ED Lab (Machine Learning for Education) and the MLO Lab (Machine Learning and Optimization Group). She holds a PhD in Computer Science from EPFL and a Master's and Bachelor's from UC Berkeley, graduating at 20 as the youngest recipient in UC Berkeley's history. Her research focuses on explainable AI, human-centric machine learning, and education technology. She has held roles as a lead engineer at Microsoft AI (ONNX framework) and served as a lecturer at UC Berkeley and UW Seattle. Vinitra's work bridges technical innovation with societal impact, exemplified by projects like MEDITRON-70B (medical LLMs) and iLLuMinaTE (actionable explanations for students). She has received multiple awards, including the 2024 G-Research PhD Prize and Rising Stars in Data Science recognition. Key contributions include interpretable neural architectures (InterpretCC), bias analysis in LLMs, and multimodal learning systems (MultiModN). Her research emphasizes practical applications in education and healthcare, with a focus on user-centered design and ethical AI practices.

• Digital Humanities
• Chinese History
• Text Analysis
• +2 more

Donald Sturgeon is an Assistant Professor in Computer Science at Durham University, with affiliations to the Institute of Medieval and Early Modern Studies. His research develops computational methods for analyzing and digitizing premodern Chinese texts, including OCR systems and linked data frameworks. Recent work focuses on crowdsourced knowledge bases for Chinese history. He regularly presents at digital humanities conferences including DH Tokyo and Heidelberg University workshops.

• Traditional Chinese Medicine (TCM)
• Data Mining
• Medical Informatics
• +4 more

Associate Professor Josiah Poon is affiliated with the School of Computer Science at the University of Sydney. His research focuses on applying data mining and IT techniques to Traditional Chinese Medicine (TCM), particularly analyzing herbal combinations for effective treatments. He collaborates with institutions in China to improve TCM evidence and has contributed to clinical data analysis, medical informatics, and multimodal AI systems. Teaching includes courses such as INFO1003 (Foundations of IT) and INFO9003 (IT for Health Professionals). Current research students are Rina CABRAL (Multimodality Representation), Yan LI (Long Document Comprehension), and Xiaobin LU (Financial Decisions). Research highlights include developing algorithms to quantify TCM efficacy, analyzing complementarity in herbal combinations, and applying machine learning to healthcare data. Notable projects include a randomized controlled trial on pneumococcal vaccination (2021) and a Google-funded multimodal health detection system (2020). Key areas of expertise span TCM informatics, medical data analytics, and AI-driven healthcare solutions. His work bridges Eastern/Western medicine through computational methods, emphasizing evidence-based practices in TCM.

• Natural Language Processing
• Text Generation
• Machine Learning
• +1 more

Zihao Fu is a Researcher at the Oxford Internet Institute (OII), University of Oxford, where he served as a Postdoctoral Researcher from March 2024 to April 2025, focusing on the Trustworthiness Auditing for AI project. Previously, he was a Research Associate (PostDoc) at the University of Cambridge's Language Technology Lab under Prof. Nigel Collier. His research emphasizes Natural Language Processing, Text Generation, Machine Learning, and Biomedical Applications. Education includes a Ph.D. from The Chinese University of Hong Kong (supervised by Prof. Wai Lam) and a visiting student period at Tsinghua University's NLP Lab. He has substantial experience with large-scale distributed algorithms in Alibaba Cloud's PAI platform. Research interests span NLP, biomedical applications, and AI ethics, with notable contributions to datasets like BAND and frameworks like OxonFair. His work addresses challenges in text generation repetition, parameter-efficient fine-tuning, and algorithmic fairness.

• Healthcare Informatics
• Artificial Intelligence
• Machine Learning
• +3 more

Maqbool Hussain is a Senior Lecturer in Computer Science at the College of Science and Engineering. His research focuses on healthcare informatics, artificial intelligence (AI) applications in medical decision-making, and the development of knowledge-based systems for aerospace and healthcare sectors. He has contributed significantly to areas such as digital twin frameworks for critical care workflows, clinical decision support systems, and semantic interoperability in healthcare. Research Interests Hussain’s work spans multiple disciplines, including: Healthcare Informatics and AI-driven medical solutions Knowledge Graphs and Large Language Models (LLMs) in healthcare and aerospace Digital twins for optimizing critical care and aerospace maintenance Data-driven clinical decision support systems Medical data security and interoperability standards Machine learning for text annotation and classification Recent Research Trends Recent publications highlight his focus on integrating advanced technologies like LLMs and digital twins into healthcare and aerospace contexts. His work addresses challenges in critical care workflow optimization, kidney disease diagnosis, and aerospace product maintenance through innovative knowledge graph approaches. He also explores the application of transformer-based models for text analysis and the secure exchange of medical data using biometric methods. Labs and Teams Hussain is affiliated with the College of Science and Engineering, contributing to interdisciplinary research teams focused on healthcare technology, aerospace systems, and AI applications. His collaborations span academic and industry partners, emphasizing practical solutions for healthcare informatics and aerospace engineering challenges.

Sandra ZillesView profile

Professor

• Computational Learning Theory
• Artificial Intelligence
• Machine Learning
• +3 more

Sandra Zilles is a Professor and Canada Research Chair (Tier 1) in Computational Learning Theory at the University of Regina's Department of Computer Science. She holds adjunct appointments at the University of Waterloo and collaborates with the Alberta Machine Intelligence Institute (Amii). Her research focuses on theoretical computer science and artificial intelligence, particularly interactive learning models, formal language theory, and heuristic search algorithms. Her research integrates computational learning theory, formal language theory, and discrete artificial intelligence structures. Key interests include: Machine teaching with limited data Learnability of pattern languages and automata Graph-theoretic approaches in AI Her work bridges theoretical frameworks with applications in medical imaging, bioinformatics, and game theory. Zilles has received numerous honors including: NSERC Canada Research Chair (Tier 1, 2022-2029) Royal Society of Canada College membership Best Paper Awards (KI 2012, ALT 2003, COLT 2002) She mentors over 50 students and postdocs through her research group. Current projects explore symbolic automata, collaborative learning, and geometric teaching models. Her lab maintains international collaborations with institutions in Germany, Canada, and the US.