Marie-Ange Remiche is a researcher at the Faculty of Computer Science, Université Libre de Bruxelles. Her work spans stochastic modeling , queueing theory , and educational technology , with a focus on fluid queues, Markov processes, and user experience in digital learning tools. Active in network performance analysis and pedagogical innovation , including projects like Conception d'une Application Numérique en Ligne d'Aide à l'Apprentissage and Cartographie et Mutualisation des ressources numériques en Mathématiques . Key collaborations with researchers like Marie De Vleeschouwer-Dieudonne and contributions to conferences such as EPEW, ASMTA, and QPES. Her recent publications address fluid queue dynamics, web configurator usability, and fairness in probabilistic systems. She supervises PhD students and participates in conference committees and peer review activities.
Prof. Josef Niebauer is a Professor at the University of Salzburg, affiliated with the University Institute for Molecular Sports and Rehabilitation Medicine. His research focuses on heart rehabilitation, physical activity promotion, and digital health interventions in cardiovascular care. He leads and contributes to projects such as Connect2Move and SWEATY HEARTS, aiming to improve cardiac rehabilitation models and long-term patient outcomes. Research interests include preventive cardiology, secondary prevention of cardiovascular disease, and the application of digital tools in healthcare. Over 443 publications and 7 projects highlight his contributions to understanding cardiovascular risk factors and innovative rehabilitation strategies. He actively organizes conferences like the Salzburg Digital Health and Prevention Days and the DA-CH Sports Cardiology Symposium. Notable work includes evaluating Austrian phase III cardiac rehabilitation models and exploring the role of digital health in promoting physical activity. Collaborations with institutions like Ferrari Company demonstrate his interdisciplinary approach to occupational health.
Hitomi Yanaka is an Associate Professor (tenured) at the University of Tokyo and Team Leader of the Explainable AI Team at RIKEN. Her research focuses on Natural Language Processing (NLP), Artificial Intelligence, and formal semantics, with a specialization in logical inference systems and compositional semantics. She holds a Ph.D. in Engineering from the University of Tokyo and has been recognized with prestigious awards including the Young Scientists Award from the Ministry of Education and Forbes JAPAN's Women In Tech TOP30 in 2024. Her academic roles include leadership in the "覚醒" project and affiliations with ACL, JSAI, and ANLP. Yanaka's work bridges theoretical linguistics and computational methods, contributing to semantic analysis, bias detection in LLMs, and multimodal reasoning. She teaches courses on computational linguistics and logic at the University of Tokyo, and her research spans over 150 publications in top-tier conferences like ACL, NLP, and Cognitive Science Society. Award highlights include the 2024 文部科学大臣表彰 (Ministry of Education Award), 2023船井研究奨励賞, and multiple best paper awards. Her lab, Yanaka Laboratory, focuses on explainable AI, ethical AI, and advancing NLP through logical frameworks. Current projects include analyzing social biases in Japanese LLMs and developing neuro-symbolic systems for multimodal tasks.
Lina Lundgren is a Senior Lecturer at Halmstad University, affiliated with the School of Business, Innovation and Sustainability and serving as Acting Head of the Engineering & Innovation department. Her research focuses on Health Technology, Health Innovation, Information-driven care, and Biomechanics, with a strong emphasis on AI-driven decision support systems in healthcare. She actively leads projects within the CAISR Health research profile and the National Research School of Health Innovation, supervising three doctoral students in Health and Lifestyle, and Innovation Science. Her teaching spans Mechanical Engineering, Sustainable Design, Innovation, and Biomedicine with a specialization in exercise physiology. Key research initiatives include developing AI-based tools to reduce hospital readmissions for heart failure patients and enhancing digital transformation in healthcare systems. She has published extensively on topics ranging from AI in mental health decision support to biomechanical analyses of sports injuries and performance. Recent work highlights interdisciplinary collaboration, particularly in aligning technological innovations with clinical needs through rigorous implementation science frameworks. Her projects emphasize stakeholder engagement, usability analysis, and ethical considerations in healthcare technology adoption.
Jason Weber serves as an Adjunct Lecturer in the School of Human Sciences at The University of Western Australia, contributing to the UWA Tech & Policy Lab. His work bridges sports science, data analytics, and technology governance with a specialized focus on Australian Rules Football. He holds a doctorate and has established expertise in biomechanics, machine learning applications, and ethical data frameworks within professional sports contexts. His research spans sports biomechanics (ground contact time, reaction forces), machine learning (neural networks for athlete tracking), and data governance. He integrates computer vision, social network analysis, and inertial measurement units to develop field-based performance evaluation methods. Key interests include validity/reliability testing of measurement techniques and ethical implications of athlete data collection in professional sports. Recent publications (2022-2024) demonstrate a concentrated focus on Australian Rules Football analytics using neural networks for location tracking and social network analysis for strategy clustering. His work consistently addresses the intersection of technology, ethics, and policy in sports data management, with increasing emphasis on real-time field applications and governance frameworks. Scientific Awards: No specific awards, fellowships, or medals are documented in the available information. Dr. Weber actively engages in policy-oriented collaborations, co-organizing a 2020 working group on professional sport data collection and participating in the 'Calling the Game' public event on sports technology governance. These initiatives highlight his role in shaping ethical data practices, though no formal student advising or grant funding details are provided. He operates within the UWA Tech & Policy Lab's interdisciplinary environment, collaborating with sports scientists, computer vision experts, and legal scholars. His current work emphasizes practical field applications of AI in sports performance analysis and developing governance models for athlete data systems.
Chirath Hettiarachchi is a Research Fellow at the ANU School of Computing, specializing in closed-loop healthcare applications. His work focuses on integrating reinforcement learning, machine learning, and control systems to improve medical technologies like artificial pancreas systems. He is actively involved in interdisciplinary research to address challenges in diabetes management, cybersecurity of medical devices, and scalable health informatics solutions. Research Interests include: Reinforcement Learning applications in medical systems Machine Learning for physiological signal analysis Artificial Pancreas System design and security Signal processing for health monitoring Biomedical engineering innovations His recent articles highlight advancements in glucose control algorithms, sensor data processing, and system security. Notable trends include: Development of modular reinforcement learning frameworks for diabetes treatment Integration of homomorphic encryption to secure medical devices Scalable platforms for sensor data preprocessing While no scientific awards are explicitly listed, his research has garnered significant citations and peer-reviewed recognition. He is registered to supervise research students and collaborates on projects involving clinicians and lived experience experts to bridge theoretical and practical healthcare challenges.
Coloma Ballester is a Full Professor of Applied Mathematics at the Department of Engineering, Universitat Pompeu Fabra (Barcelona), where she coordinates the Intelligent Multimodal Vision Analysis (IMVA) research group. Her work bridges mathematical theory and practical applications in visual data analysis. Her research encompasses: Core methodologies : Variational methods, geometric models, PDEs, non-local approaches, and data-driven learning Applications : Image inpainting, segmentation, object recognition, video interpolation, motion analysis, and computational photography Current focus : Multimodal scene understanding integrating vision, language, and audio for sign language translation, sports analytics, and media production PhD Supervision Current advisees: Pritam Mishra (2022–present) Marcelo Sánchez-Ortega (2022–present) Adam Phillips (2023–present) Marcel Granero (2024–present) Laia Tarrés (2024–present) Notable former students: Adrià Arbués-Sangüesa (Senior Data Scientist, Zelus Analytics) Patricia Vitoria (Machine Learning Engineer, Apple) Vanel Lazcano (Associate Professor, Universidad Mayor)
James R. Glenn is a Senior Lecturer in the Department of Computer Science at Yale University's Faculty of Arts and Sciences. He teaches multiple computer science courses including systems programming, algorithms, data structures, and computational intelligence for games. His research focuses on stochastic games , particularly on computing optimal strategies and using artificial intelligence techniques to create near-optimal strategies when optimal solutions are infeasible. He also investigates mathematical problems related to '3-free sets' and methods for constructing large sets. His work bridges theoretical computer science with practical applications in game theory and algorithm design. Dr. Glenn teaches courses including CPSC 223 (Data Structures), CPSC 323 (Systems Programming), CPSC 365 (Algorithms), and CPSC 474/574 (Computational Intelligence for Games). His teaching spans foundational computer science concepts to advanced topics in game AI. Current research focuses on stochastic game strategies Expertise in algorithms and computational intelligence Extensive teaching experience across various CS topics His publications reflect his interests in game theory, algorithms, and educational tools, with work spanning from theoretical mathematics to practical software implementations. Dr. Glenn maintains an active teaching schedule with regular office hours in Dunham Lab 429 at Yale University.
Ashish Chouhan is a Full-time Doctoral Researcher at the Data Science Research Group , Institute of Computer Science , Heidelberg University , Germany. He previously worked as an Academic Researcher at SRH Hochschule Heidelberg (2020-2023) and has been an Extern Doctoral Researcher at Heidelberg University since 2021. B.Sc. (2014) from Rashtrasant Tukadoji Maharaj Nagpur University , India M.Sc. (2020) in Applied Computer Science from SRH Hochschule Heidelberg His research focuses on Natural Language Processing with special emphasis on Retrieval Augmented Generation frameworks, Question-Answering Systems , and Corpus Management and Exploration . He has made significant contributions through his publications at major conferences including: SIGIR'25 - ClusterChat for corpus exploration LREC-COLING 2024 - LexDrafter for legislative documents EMNLP'22 - EUR-Lex-Sum dataset for legal summarization Chouhan actively contributes to academic governance through: Program Committee membership at RegNLP@COLING 2025 and NLLP@EMNLP series Reviewing for Artificial Intelligence and Law Journal (2022-2025) As Lecture Assistant and Co-supervisor , he has guided numerous students on projects involving: Answer Generation QA Systems Conversational Dataset Generation Corpus Exploration Medical Record Analysis
Professor Gianluca Demartini is a Professor in Data Science and an ARC Future Fellow at the School of Electrical Engineering and Computer Science, Faculty of Engineering, Architecture and Information Technology at the University of Queensland, Australia. He also serves as an affiliate of the Centre for Enterprise AI. His research focuses on human-in-the-loop artificial intelligence systems with applications for public good, bridging structured knowledge graphs and unstructured text analytics to address societal challenges. Dr. Demartini earned his Ph.D. in Computer Science from Leibniz University of Hannover in Germany in 2011, with a focus on Semantic Search. His academic journey includes positions as a Lecturer at the University of Sheffield (UK), post-doctoral researcher at the eXascale Infolab at the University of Fribourg (Switzerland), visiting researcher at UC Berkeley, junior researcher at the L3S Research Center (Germany), and intern at Yahoo! Research (Spain). His research interests span four major interconnected domains: Misinformation (studying human interaction with misinformation and AI-based mitigation strategies), Crowdsourcing and Human Computation (improving efficiency of human-in-the-loop systems), Big Data Analytics (designing scalable algorithms for large datasets), and AI for Public Good (applying AI for societal and environmental benefits). His work consistently addresses real-world challenges in information quality, human-AI collaboration, and ethical technology deployment. Analysis of Professor Demartini's recent publications reveals a clear trajectory toward addressing misinformation through sophisticated human-AI collaboration frameworks, with increasing emphasis on cognitive aspects of fact-checking, data bias management, and strategic application of large language models. His research bridges theoretical advances in information retrieval with practical applications for societal challenges, particularly in media literacy, online safety, democratic discourse, and environmental conservation. Professor Demartini has received numerous prestigious awards recognizing the quality and impact of his work: Best Paper Award at ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR) in 2023 Best Paper Award at AAAI Conference on Human Computation and Crowdsourcing (HCOMP) in 2018 Best Paper Awards at European Conference on Information Retrieval (ECIR) in 2016 and 2020 Best Demo award at International Semantic Web Conference (ISWC) in 2011 Honorable Mention Award at CSCW 2020 (Top 2% of submissions) As an active supervisor, Professor Demartini currently guides PhD students working on cutting-edge topics including Retrieval Augmented Generation, Human-in-the-Loop Decision Systems for Online Safety, Human-Centred Artificial Intelligence for Democracy, and Bias in Data Pipelines. His research program is generously funded through multiple major grants: ARC Future Fellowships (2025-2028): PBIAS - A Principled Approach to Data Bias Management Swiss National Science Foundation (2022-2025): Large-Scale Political Participation: Issue Identification, Deliberation, and Co-creation ARC Training Centre for Information Resilience (2021-2026) Previous funding from Wikimedia Foundation, Meta, Google, and Facebook for projects on misinformation detection and human-AI collaboration Professor Demartini's work sits at the critical intersection of human computation, information retrieval, and AI ethics. Through extensive collaborations with industry partners including Facebook, Google, Microsoft, Yahoo!, IBM, SAP, and The National Archives (UK), he has developed practical systems that address real-world challenges in misinformation detection, data quality, and human-AI collaboration. His research group actively explores how to make AI systems more transparent, accountable, and beneficial for society through principled human-in-the-loop approaches that leverage both machine intelligence and human expertise.
Professor Simon Laws is a leading academic in Translational Genomics at Edith Cowan University’s School of Medical and Health Sciences. He serves as the inaugural Director of the Centre for Precision Health, a strategic research center, and leads the Collaborative Genomics and Translation Group. His work bridges genomics, epigenetics, and lifestyle factors to improve health outcomes through personalized interventions. Professor of Translational Genomics, Edith Cowan University (2020–present) Director, Centre for Precision Health (2021–present) Associate Professor, Edith Cowan University (2011–2020) Deputy Chief Scientific Officer, CRC for Mental Health (2011–2017) Simon Laws’ research focuses on the genomic architecture of Alzheimer’s disease and cognitive decline, examining genetic and epigenetic risk profiles, methylation patterns, and the interaction of genomics with lifestyle. His work extends to human performance, aiming to optimize training and prevent injury through genetic insights. His research employs advanced biological technologies and analytical methods to develop precision health strategies. The most recent publications (2024–2025) highlight a strong focus on plasma biomarkers (e.g., p-tau217), polygenic risk scores, DNA methylation, and the interplay between vascular health, genetics, and neurodegeneration. His work frequently involves large-scale collaborations and cohort studies such as the AIBL study, with publications appearing in high-impact journals like Nature Communications , Alzheimer's & Dementia , and EBioMedicine . Vice-Chancellor's Award for Excellence in Research (2019) Professor Laws is actively involved in advising and collaborative research across Australia and internationally. He has participated in numerous studies examining the role of exercise, sleep, diet, and genetic variants in brain health and cognitive aging. He is affiliated with key professional societies including the Australasian Neuroscience Society and the Alzheimer’s Association’s ISTAART. His research is supported by institutional and collaborative funding, enabling large-scale genomic and translational studies. He leads the Collaborative Genomics and Translation Group within the Centre for Precision Health, fostering interdisciplinary research and innovation in precision medicine. The team focuses on integrating multi-omics data with clinical and lifestyle factors to develop early detection and intervention strategies for neurodegenerative diseases.
Matt Bailey serves as the Presidential Professor of Analytics & Operations Management at Bucknell University, holding a concurrent faculty fellowship at the Dominguez Center for Data Science. His academic appointment resides within the Department of Analytics and Operations Management where he teaches core business analytics courses. His educational foundation includes: PhD in Industrial and Operations Engineering from the University of Michigan (2002) MS in Industrial and Operations Engineering from the University of Michigan (1997) BS in Mathematics from Purdue University (1996) Dr. Bailey's research program integrates Operations Research with Business Analytics to develop Decision Support Systems addressing complex resource allocation challenges. His work demonstrates exceptional cross-domain applicability across education systems, healthcare delivery, energy markets, and athletic management, consistently employing advanced optimization techniques to solve real-world problems under uncertainty. Publication trends reveal sustained focus on translating theoretical operations research into practical decision tools, with recent work emphasizing machine learning integration in business contexts while maintaining foundational strengths in stochastic modeling and game-theoretic approaches across multiple sectors. His scholarly impact is recognized through: Poets and Quants Top 50 Undergraduate Business Professors (2020) Best Application Paper Award from IIE Transactions (2010) Best Applied Paper in Operations Engineering from Institute of Industrial Engineers (2009) No student advising relationships or research grant details are documented in the provided materials. As a Dominguez Center for Data Science faculty fellow, he contributes to interdisciplinary data science initiatives bridging analytics methodologies with domain-specific applications across the university ecosystem.
Michal Slupczynski is a PhD student and research assistant at the Chair for Computer Science 5 (Informatik 5) within the Department of Computer Science, Faculty of Mathematics, Computer Science and Natural Sciences at RWTH Aachen University. He earned his Master’s degree in Software Systems Engineering from the same institution in 2020 and has been actively contributing to research and teaching since December 2020. His research interests include human-computer interaction (UI/UX, personal fabrication), DevOps/MLOps/LLMOps, workflow automation, decentralized P2P communities, augmented and virtual reality (especially collaborative environments), interactive storytelling, agile project management, digital transformation, gamification, and games. He applies these interests in the context of educational technology, psychomotor skill acquisition, and learning analytics. His recent publications reflect a strong focus on sensor-based motion analysis, AI-driven feedback systems, immersive learning environments, and explainable AI. He has contributed to advancements in sports motion analysis using the Kabsch algorithm, cloud-based feedback for psychomotor training, citation recommendation chatbots, and MLOps pipelines for educational data. Michal is actively involved in teaching, offering courses such as Datenbanken und Informationssysteme and seminars on Social Computing, Web Science, and High-Tech Entrepreneurship. He has supervised numerous thesis projects on topics ranging from LLMs in maintenance and legacy system modernization to blockchain escrow systems and gamification in education. He is engaged in key research projects including MILKI-PSY (Multimodal Immersive Learning with AI for Psychomotor Skills), TrainSpot (Train-the-Trainer HotSpot), Blockchain4DatenMarktplatz.NRW, and las2peer, highlighting his work in decentralized systems and technology-enhanced learning. He has also served as an editor for workshop proceedings and presented at international conferences such as EC-TEL and CollabTech.
Prof. Dr. Daphne van de Bongardt is a Professor of Relational and Sexual Development, Education, and Health at Erasmus University Rotterdam's Department of Psychology, Education and Child Studies. She leads the Erasmus Love Lab, an interdisciplinary research initiative focusing on intimate relationships, love, and sexuality. Her work emphasizes mixed-methods approaches, including longitudinal studies, interviews, and observational techniques. Key research interests include youth sexual development, psychosexual health interventions, and the role of social contexts like family, peers, and digital media. Van de Bongardt has held significant administrative roles, including Department Director of DPECS and chair of the Young Erasmus Academy. She actively bridges academia and public discourse through media engagements (TEDx, Universiteit van Nederland) and advisory roles (Netherlands Sexual Wellbeing Foundation). Notable achievements include the Van Emde Boas-van Ussel Prize 2025 and grants from NWO and ZonMw for projects like Move Up! and T@CKLE, addressing sexual health and violence prevention among youth. Her research explores topics such as parental influence on lying behavior, resilience in youth exposed to family violence, and predictors of problematic pornography use. The Erasmus Love Lab serves as a platform for transdisciplinary collaboration, emphasizing co-creation with young people to inform policy and education.
Iyad Obeid is an Associate Professor in the Department of Electrical and Computer Engineering at Temple University's College of Engineering, with a secondary appointment in Bioengineering. He serves as Chair of the department and is a leading researcher in neural and biomedical signal processing. His research interests include: Neural engineering Neural and biomedical signal processing Biomedical data analytics Neurofeedback Brain-computer interfaces Traumatic brain injury Virtual reality for cognitive and physical therapy His recent publications indicate a strong focus on neural instrumentation, brain-computer interfaces, and applying signal processing and AI to neurological data, particularly EEG. His work bridges engineering, neuroscience, and clinical applications. He has received significant recognition for his work and teaching: National Science Foundation CAREER Award Lindback Award for distinguished teaching Dr. Obeid has secured research funding from major agencies including the NSF, NIH, and DARPA. He leads the Neural Instrumentation Lab and is the co-founder of the Neural Engineering Data Consortium (NEDC). He teaches courses such as Signals and Systems, Engineering Computation, and Probability and Random Processes at both undergraduate and graduate levels.
