Dr. TCHUENTE Dieudonné is an Associate Professor in the Department of Information, Operations and Decision Sciences at TBS Education. His research bridges artificial intelligence, sustainability, and operations management through practical applications in energy efficiency, supply chain resilience, and social media analysis. Department: Information, Operations and Decision Sciences University: TBS Education Email: d.tchuente@tbs-education.fr Research Focus : Dr. Tchuente's work explores AI-driven solutions for sustainable development, including building energy retrofits , real estate valuation , and vaccine supply chains . His studies on explainable AI provide actionable insights for corporate emissions reduction and aeronautics optimization. Publications Trends : His recent work (2022-2024) emphasizes AI ethics , climate accountability , and industry transformation across domains like real estate, aviation, and public health. Key methodologies include SHAP analysis , meta-learning , and crowdsourcing frameworks . Scientific Awards : FNEGE Grant - for research on connected vehicle infrastructure
Anthony Kelly serves as a Postdoctoral Research Fellow in the Department of Electronic and Computer Engineering within the Faculty of Science and Engineering at the University of Limerick, Ireland, with his office located in E2-006. His affiliation spans both engineering and healthcare domains through interdisciplinary research initiatives. His research demonstrates dual expertise in artificial intelligence applications for healthcare and advanced power electronics. In healthcare AI, he develops interpretable mental health models, diabetes management chatbots, and comorbid condition interventions with emphasis on clinician trust and safety evaluation. In power systems, he pioneers digital control techniques for DC-DC converters, FPGA power management, and machine learning-integrated circuit designs. This bifurcated focus reveals a strategic transition from hardware-centric research (2005-2019) toward AI-health convergence (2024-2025). Analysis of his 15 most recent publications shows a pronounced shift toward healthcare AI since 2024, with 80% of current work addressing mental health modeling, diabetes chatbots, and comorbid condition management. Earlier publications (2009-2019) consistently focused on power electronics innovations including current-sharing algorithms, adaptive controllers, and FPGA-based systems, establishing foundational expertise later applied to healthcare technology development.
Ana Lucic is an Assistant Professor in Artificial Intelligence at the University of Amsterdam , with a joint appointment between the Institute for Logic, Language and Computation and the Informatics Institute . Her research focuses on interpretable machine learning applications for scientific discovery and societal impact. Formerly at Microsoft Research AI for Science and Partnership on AI PhD in Explainable Machine Learning from University of Amsterdam (2022) BSc/MSc in Mathematics from McMaster University Research Highlights: Develops mechanistic interpretability methods for deep learning architectures. Created Aurora , a foundation model for Earth system forecasting outperforming traditional operational models in air quality prediction and tropical cyclone tracking. Pioneers Clifford-Steerable CNNs for geophysical data analysis. Actively hiring PhD students for AI transparency research . Collaborative Networks: Contributions to ELLIS Summer School and ICML workshops . Collaborates with Microsoft Research AI for Science team on climate-related ML projects. Involved in organizing TerraBytes workshop at ICML 2025. Recent Advancements: Key role in publishing Aurora model in Nature (2025), demonstrating superior performance in Earth system forecasting. Supervises Ege Erdogan , new PhD student focused on mechanistic interpretability. Actively contributes to open-source AI development through GitHub repositories and technical discussions.
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.
Dr. Timo Speith is a fixed-term Lecturer at the University of Bayreuth's Faculty of Cultural Studies, affiliated with the Professorship for Philosophy, Computer Science and Artificial Intelligence. He holds a B.A. in Philosophy and M.Sc. in Computer Science from Saarland University, where he also completed his doctoral studies in Philosophy. His research focuses on machine ethics, machine explainability (XAI), and non-speculative AI systems, alongside interests in ancient philosophy and the philosophy of science. He has contributed to interdisciplinary work on XAI evaluation methods, fairness in AI systems, and explainable hardware (XHW). Speith has organized workshops on explainable systems requirements engineering and co-authored a manifesto outlining XAI 2.0 challenges. His work bridges philosophy, computer science, and ethics to address AI transparency and accountability. Education: B.A. Philosophy, Saarland University M.Sc. Computer Science, Saarland University Ph.D. in Philosophy, Saarland University (2023 dissertation) Research Interests: Machine Ethics, XAI Methodology, AI Transparency, Philosophy of Science, Ancient Philosophy. Grants and Collaborations: Active in international workshops (e.g., RE4ES), IEEE conferences, and collaborations with institutions like Saarland University and the University of Bayreuth's Philosophy Institute. His work addresses practical and theoretical challenges in AI explainability across hardware and software systems. Lab/Team Affiliation: Associated with the Philosophy, Computer Science, and Artificial Intelligence research group at the University of Bayreuth, focusing on interdisciplinary AI ethics and technical implementation.
Maura R. Grossman is a Research Professor at the University of Waterloo, specializing in High-Recall Information Retrieval, AI ethics, and legal technology. Her work focuses on ensuring comprehensive information retrieval in high-stakes contexts like electronic discovery in law, healthcare data curation, and medical evidence synthesis. She explores the intersection of artificial intelligence and legal systems, particularly addressing challenges posed by AI-generated evidence and deepfakes in judicial processes. Education: J.D. (Georgetown University Law Center, 1999), Ph.D. (Adelphi University, 1984), M.A. (Adelphi University, 1982), A.B. (Brown University, 1980). Research interests include AI accountability in courts, responsible data science practices, and improving electronic discovery methodologies. Recent work analyzes AI’s role in legal proceedings, ethical AI frameworks, and healthcare data governance. Her publications emphasize validating technology-assisted review (TAR) systems and evaluating generative AI impacts on marginalized communities. Her articles highlight trends in AI’s legal implications, healthcare data sharing protocols, and the need for transparent algorithmic systems in justice contexts. She contributes to TREC tracks, advancing high-recall retrieval techniques for legal and medical document analysis. Notable projects include developing frameworks for unbiased health data sharing and analyzing AI’s effects on marginalized writers. Her work underscores interdisciplinary collaboration between law, computer science, and healthcare to address emerging technological challenges.
Rhema Linder is a Teaching Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. His academic journey includes a BS in Computer Science and Mathematics from LeTourneau University (2009) and a PhD in Computer Science from Texas A&M University (2019). Prior to his current role, he served as a postdoctoral researcher at the PAIRS lab at UT Knoxville. His research focuses on Human-Computer Interaction (HCI), AI Art, Information Visualization, and Creative Cognition. He explores how AI and software systems can enhance creative productivity, particularly in collaborative online environments. His work integrates theories from creative cognition, social science, and HCI to design tools for engineering, design, and scholarship. Rhema has held internships at Adobe Research and Microsoft Research, contributing to projects in data science and human-computer interaction. His GitHub repositories include open-source projects like kivy-games, demonstrating his engagement with software development and educational tools. His research interests emphasize multidisciplinary approaches, blending art, technology, and social science to innovate in creative online spaces. He is actively involved in the PAIRS lab, focusing on advancing understanding of human-AI collaboration and information management systems.
Dr. Tillman Weyde is a Reader in the Department of Computer Science at City, University of London , where he has been employed since 2021. He leads the Machine Intelligence and Media Informatics Research Group and is a member of the Machine Learning Group . Prior to this, he served as Senior Lecturer (2009–2021) and Lecturer (2005–2009) at City, and worked as a researcher at the University of Osnabrück (2001–2005), coordinating the MUSITECH project. His academic background includes PhD in Music Technology (2002), MSc in Computer Science (1999), and MSc in Mathematics, Music, Philosophy & Pedagogy (1994), all from the University of Osnabrück. Research Focus: Machine learning and signal processing methods for data analysis with applications in finance, audio, NLP, music, health, security, and education. His recent work emphasizes inductive biases in neural networks for rule-learning, extrapolation, generalization, and interpretability. Grants & Projects: Principal Investigator for the AHRC-funded Digital Music Lab (2012–2017) and Integrated Audio-Symbolic Model of Music Similarity (2017–present). Co-investigator in Innovate UK and EPSRC projects on safer gambling ( Advancing Consumer Protection , 2015–2018) and Raven (2012–2021). Collaborations: Affiliated with the Institute of Cognitive Science (Osnabrück), Intelligent Systems Research Laboratory (Reading), and the MPEG Ad-Hoc Group on Symbolic Music Representation. Awards: Co-author of the 2000 Comenius Medal-winning educational software Computer Courses in Music Ear Training and co-editor of the Osnabrück Series on Music and Computation . Publications: Over 150 peer-reviewed works including conference papers, journal articles, and book chapters, focusing on interdisciplinary applications of machine learning in music, health, and finance. Students: Supervised 13 PhD students across topics like grammar bias in neural networks, emotion recognition from audio, extrapolation behavior in neural networks, relation-based patterns, legal text parsing, and more.
Andreas Oberweis is a Professor at the Karlsruhe Institute of Technology (KIT), Germany, with a distinguished career in business process management, information systems, and process modeling. His work spans interdisciplinary collaborations, focusing on integrating formal methods like Petri nets with practical applications in digital identity, privacy, and educational technology. Key research areas include Business Process Management, Information Systems, Petri Net Modeling, Semantic Web, and Process Mining. He has contributed to methodologies like MEUSec for enhancing user experience and security in digital identity wallets, and frameworks for automated assessment of modeling competencies. Publications highlight innovations in process model similarity, social network coordination, and 3D business process visualization. His recent work emphasizes user-centric security and multimodal mobility optimization. Collaborations involve institutions like KIT, University of Rome, and Queensland University of Technology, with co-authors such as Peter Loos, Ralf Reussner, and Agnes Koschmider. He has developed tools like INCOME2010 for process-oriented systems and contributed to open models for enterprise systems. Despite no explicit awards listed, his 18+ years of continuous publication in top venues (BPM, EMISA, Modellierung) underscore his academic impact.
Dr. Ajda Pretnar Žagar is a researcher at the University of Ljubljana's Faculty of Computer and Information Science and the Institute for Contemporary History. With a PhD in Anthropology from the University of Ljubljana (2021), she bridges computational methods with social sciences through interdisciplinary research. University of Ljubljana Institute for Contemporary History Her research focuses on: Quantitative Anthropology Computational Social Sciences Machine Learning Applications Algorithmic Futures Bioinformatics Recent projects include: REIMAGINE ADM (2022-2025): Reimagining public values in algorithmic futures DALI4US (2024-2026): Data Literacy for upper primary schools xAIM (2021-2024): Explainable AI in healthcare
Dr. Vangelis Marinakis is an Assistant Professor at the School of Electrical and Computer Engineering (ECE) of the National Technical University of Athens (NTUA). His academic background includes an Electrical and Computer Engineering degree and a PhD in Decision Support Systems for Sustainable Energy Planning from NTUA. PhD in Decision Support Systems for Sustainable Energy Planning (NTUA) Electrical and Computer Engineer (NTUA) His research focuses on designing methodologies for intelligent energy management across Smart Homes, Buildings, Cities, and Districts, leveraging technologies like IoT, AI, and Big Data. He has contributed to over 25 European (Horizon Europe, H2020) and national projects, with more than 50 journal publications and book chapters. Key research areas include Decision Support Systems , Energy Efficiency , and Renewable Energy Integration . He has led research in AI-driven energy forecasting, federated learning for privacy-preserving data models, and blockchain applications in energy markets. His work explores the intersection of Smart Grids , Building Informatics , and Climate Resilience . Dr. Marinakis has developed frameworks for: Decarbonization-as-a-Service in building renovations Scalable Big Data architectures for smart buildings Multi-criteria optimization of EV charging stations Explainable AI in energy decision-making Climate resilience assessment for urban housing
Zahid Islam is a Professor of Computer Science and Associate Dean (Research) at Charles Sturt University's Faculty of Business, Justice and Behavioural Sciences (FoBJBS). He leads the Data Science and Engineering Research Unit (DSERU) and the Cyber Security CRC initiatives. His research focuses on Data Mining, Cyber Security, Privacy-Preserving Techniques, and Machine Learning applications in real-world domains such as healthcare and agriculture. Key roles include: Associate Dean (Research), FoBJBS (since 2023) Director, DSERU (2019–2023) Theme Lead & Academic Lead, Cyber Security CRC (2021–2023) Research interests span: Data Mining: Classification, Clustering, Federated Learning, Missing Value Analysis Cyber Security: Malware Detection, Adversarial Attacks, IoT Security Applications: Healthcare, Agriculture, Smart Farming, Supply Chain Recent work includes developing frameworks for ransomware detection (RADAR dataset), collaborative learning in healthcare (CL3), and privacy-preserving techniques like UD-LDP. His cyber security research has earned the 2021 Cyber Security Researcher of the Year Award (AISA). Key grants include: $5M+ external funding for cybersecurity projects AI for Mental Health Support (2025) Adaptive Federated Learning Grant (2024) Labs/Groups: Data Mining Research Group (DaMRG), Machine Vision and Digital Health (MaViDH), Cyber Security Research Group (CSRG)
Jingjie Li is a Lecturer (Assistant Professor) in the School of Informatics at the University of Edinburgh, where he conducts interdisciplinary research at the intersection of computer systems, cybersecurity, and human-computer interaction. He is a member of the Institute for Computing Systems Architecture and the School of Informatics Ethics Committee. Ph.D. in Computer Engineering, University of Wisconsin-Madison (2017–2023) B.Eng. (R&D) with First-Class Honours, Australian National University (2015–2017) B.Sc., Beijing Institute of Technology (2013–2015) His research focuses on user-centric security and privacy , measuring human behavior in digital systems , and efficient human-machine interfaces . He investigates risks in emerging technologies such as smart homes, AR/VR, and AI systems, aiming to make them safer and more human-centric. His work combines technical innovation with behavioral insights to design practical privacy controls, measure digital risks, and build efficient computing platforms. The recent publications highlight a strong trend in privacy transparency , AI explainability , smart home and AR/VR security , and hardware-software co-design . His work frequently appears in top-tier venues including IEEE S&P, USENIX Security, ACM CHI, and ISCA, reflecting a consistent focus on both technical depth and human factors. Notable scientific awards include: ACM CHI Best Paper Award (2019) Facebook Trustworthy Products in AR, VR, and Smart Devices Award (2021) CPS Rising Star, NSF (2022) Generative AI Laboratory Seedcorn Award, University of Edinburgh (2024) Qualcomm Innovation Fellowship Finalist (2019, 2021) Jingjie Li actively supervises PhD students including Jiuming Jiang and Karen Jiamin Zheng, and co-supervises Lawrence Piao and Temima Hrle. He has received research support through fellowships such as the UW–Madison Chancellor’s Opportunity Fellowship and has collaborated globally with institutions including Max Planck Institute, Visa Research, and CSIRO. He serves on the program committees of major conferences like ACM CCS, USENIX Security, and ACM CHI. He leads a dynamic research team focused on systems security and human-centered computing, hosting undergraduate researchers and mentoring students through projects in privacy, AI transparency, and hardware security. His lab fosters interdisciplinary collaboration and real-world impact through community engagement, such as the 'Hack Your Age' workshop with intergenerational participants.
Justus Bogner is a researcher at the Institute of Software Engineering (ISTE) and leads the division for Software Engineering for AI- & Microservice-Based Systems (SE4AI&MS) at the University of Stuttgart. His work focuses on empirical software engineering , microservices , AI-based systems , and software evolvability . He has contributed extensively to understanding architectural migration, technical debt in AI systems, and quality assurance methodologies. His research spans various subfields including service-oriented architecture , RESTful API design , design patterns , and software maintainability metrics . Recent publications highlight empirical comparisons between JavaScript and TypeScript, frameworks for microservice migration, and systematic studies on AI engineering challenges. Bogner's work often bridges academic research with industry practices through collaborations with IEEE and Springer, though no specific awards or student advisories are mentioned in the provided text.
Peter Lewis is an Associate Dean (Research and Graduate Studies) and Associate Professor at Ontario Tech University's Faculty of Business and Information Technology, holding a Canada Research Chair in Trustworthy Artificial Intelligence. He specializes in socio-technical systems, self-aware computing, and AI ethics. His work bridges foundational AI research with practical applications in industry, emphasizing trust, collective action, and socially intelligent systems. Education PhD in Computer Science, University of Birmingham, UK MSc in Natural Computation, University of Birmingham, UK BSc in Computer Science, University of Leicester, UK PGCert in Learning and Teaching Higher Education, Aston University, UK Research Interests His research focuses on trustworthy AI, self-aware systems, and the societal implications of AI. Key areas include explainable AI for accessibility, ethical AI governance, and modeling social institutions. He co-edited Self-Aware Computing Systems and serves on editorial boards for IEEE journals. Publications & Awards Over 75 peer-reviewed papers in journals/conferences Winner of the 2020 Aston Achievement Award for Business Engagement Best Paper Award at IEEE SASO 2018 Labs & Collaborations He leads research in the Business Analytics & AI group, collaborating on projects with industry partners. His work addresses real-world challenges in AI ethics, sustainability, and human-AI collaboration.
