Josef Bigun is a Professor at the School of Information Technology, Halmstad University , Sweden, since 1998. His research focuses on Computer Vision, Biometric Signal Analysis, and Artificial Intelligence , with emphasis on periocular recognition, iris biometrics, and motion analysis . He has been recognized as a Fellow of the IAPR (2000) and Fellow of the IEEE (2003) , becoming Sweden's first IEEE Fellow in image analysis. Education : M.Sc. and Ph.D. from Linköping University (1983, 1988) Key Projects : EU projects (BBfor2, BIOSECURE, ACTS-M2VTS), Swedish VR/SSF projects, Swiss Fonds National projects His recent publications focus on CNN optimization, periocular biometrics, and cross-spectral recognition . He has developed innovative Continuous Examination systems using spiral codes for educational assessment and contributed to biometric standards for the European Association for Biometrics . Scientific Awards : Fellow of the IAPR (2000) Fellow of the IEEE (2003) Top 10 Scientist in Sweden (Scopus AI & Image Processing, 2024 Stanford study) Listed on research.com's top Computer Science researchers (Sweden) He has served on organizing committees for ICPR, ICIP, ICB conferences and co-founded the Audio and Video Based Person Authentication conference (now ICB). His work appears in journals like Pattern Recognition Letters and IEEE Image Processing .
Christine Largouët is a tenured Associate Professor in Computer Science at Institut Agro Rennes-Angers , where she leads the Computer Science Teaching Unit since 2006. She is an associate researcher with the DREAM team at IRISA (Institute for Research in Computer Science and Random Systems) and part of the LACODAM team at IRISA/INRIA. Her academic journey includes teaching positions at University of New-Caledonia (2003-2005) and research collaborations with INRAE, IRISA, and INRIA. HDR : Université de Rennes 1 (2019) PhD : Université de Rennes 1 (2000) Her research bridges Artificial Intelligence with Agroecology , focusing on: Learning Timed Behavioral Models Explainable AI for decision transparency Complex Systems Modelling and Analysis Data-Driven Decision Support frameworks Applications to Ecosystem Management and Precision Livestock Farming She employs formal methods like Timed Automata and Model-Checking to analyze agricultural systems and animal behavior patterns. Recent publications demonstrate her work on: Explainable AI interfaces for behavioral data interpretation Timed Automata for agricultural system modeling Machine Learning applications in swine nutrition and welfare Intrusion Detection Systems for agricultural IoT Environmental Modeling with formal verification Her methodological contributions include persistence-based discretization and role-adaptive explanation frameworks.
Tomi Janhunen is a Professor in Computing Sciences at Tampere University, specializing in knowledge representation, automated reasoning, and logic programming. He previously served as Adjunct Professor at Aalto University (2019–2024) and maintains a Doctor of Science (Tech.) degree. His research spans answer set programming, satisfiability checking, optimization, and distributed computation. PhD, Aalto University (Doctor of Science (Tech.)) Adjunct Professor of Computer Science (Aalto University, 2019–2024) His research focuses on Answer Set Programming (modularity, verification, optimization), Satisfiability Modulo Theories , Nonmonotonic Logics , and Computational Complexity . He integrates logic programming into real-world applications like preventive maintenance scheduling and AI security systems. Recent work includes translating logic programs into integer programming, developing probabilistic reasoning systems (Plingo), and creating interpretable classifiers for tabular data. His publications emphasize stable model semantics , optimization techniques , and constraint networks . He supervises M.Sc., Lic.Sc., and Ph.D. theses and has completed pedagogical studies. Janhunen actively reviews for journals like Artificial Intelligence Journal and ACM Transactions on Computational Logic .
Yinong Chen serves as a Teaching Professor at Arizona State University's School of Computing and Augmented Intelligence within the Ira A. Fulton Schools of Engineering. With over two decades of academic experience since joining ASU in 2001, he maintains active roles in research, teaching, and professional service. His institutional affiliations include continuous editorial board memberships with Simulation Modeling Practice and Theory (Elsevier), International Journal of Simulation and Process Modeling, and Journal of Systems and Software. Dr. Chen's educational background includes a Ph.D. in Computer Science from the University of Karlsruhe/Karlsruhe Institute of Technology (1993), an M.S. from Chongqing University (1984), and a B.S. in Software Engineering from the same institution (1982). Prior to ASU, he served as Lecturer and Senior Lecturer at the University of the Witwatersrand (1994-2000) and completed postdoctoral research at LAAS-CNRS in France. His research spans service-oriented computing, visual programming environments, and computer science education innovation. Chen has developed the VIPLE (Visual IoT/Robotics Programming Language Environment) framework, which integrates robotics, IoT, and AI education through visual programming. Current work focuses on GPU-accelerated cloud services, AI-enhanced educational tools, and smart city applications. His recent publications reveal a strong emphasis on applying service-oriented principles to emerging domains like quantum machine learning education and assistive technologies. Professional service includes editorial responsibilities for four major journals and continuous committee involvement at ASU since 2004, particularly in freshman mentorship and computing resource management. His grant history demonstrates sustained funding from Intel for robotics education initiatives and multiple NSF-related projects focused on service-oriented computing pedagogy. Arizona Robotics Challenge Initiative (Intel, 2007-2010) Visual Programming for IoT in Engineering Education (Intel, 2012-2013) Preparing High School Teachers for Service-Oriented CS Education (NSF, 2007-2011) Chen directs the VIPLE research group, which develops visual programming tools for robotics and IoT education. Current projects include the ASU Instructor Assistant GPT and multi-modal systems for blind obstacle detection. His work bridges theoretical service-oriented computing with practical educational applications, maintaining strong industry partnerships while advancing computer science pedagogy.
Arkaitz Zubiaga is a Senior Lecturer (Associate Professor) at Queen Mary University of London, where he co-leads the Social Data Science lab and serves as Director of Graduate Studies. He is also part of the leadership team of the Centre for Human-Centred Computing. His research sits at the intersection of Computational Social Science and Natural Language Processing, focusing on developing NLP and LLM methods for processing social media and Web data to tackle societal harms. Zubiaga's research interests concentrate on addressing problematic issues with damaging societal effects, including hate speech, misinformation, inequality, biases, and other forms of online harm. He investigates how LLMs can be misused for malicious purposes such as spreading misinformation, generating abusive content, or exacerbating societal biases. His work emphasizes detecting and addressing irresponsible AI use where content is falsely claimed to be human-generated. His publication record shows a clear trend toward addressing bias in detection systems, particularly in cyberbullying detection where swearing bias has been identified as a critical issue. His recent work explores zero-shot and few-shot learning approaches for cross-lingual applications, stance detection, and claim verification. The research spans multiple disciplines including computational linguistics, social computing, and AI ethics, with a growing focus on multimodal approaches and longitudinal model evaluation. 2024 OSNEM best survey award for work on session-based cyberbullying detection Zubiaga actively mentors PhD students, with Peiling Yi recently passing her viva (April 2025) and welcoming new PhD students Alaa Bazaid and Ali Khairallah. He serves as senior area chair for ACL 2025 and leads the HYBRIDS MSCA Doctoral Network. His work demonstrates a strong commitment to developing responsible AI systems that can detect and mitigate online harms while addressing critical issues of bias and fairness in computational approaches.
Michael A. Livermore is a Professor at the University of Virginia School of Law, where he joined the faculty in 2013. He teaches courses on environmental law, regulation and legal technology, and is recognized as a leading expert in cost-benefit analysis for regulatory evaluation. Livermore frequently collaborates on interdisciplinary projects with researchers across economics, computer science, neurology, and the humanities. Livermore earned his J.D. magna cum laude from NYU Law, where he was a Furman Scholar, was elected to the Order of the Coif, and served as a managing editor of the Law Review. After law school, he spent a year as a fellow at NYU Law's Center on Environmental and Land Use Law before clerking for Judge Harry T. Edwards on the U.S. Court of Appeals for the D.C. Circuit. Prior to joining UVA, he was the founding executive director of the Institute for Policy Integrity at New York University School of Law. Professor Livermore's research focuses on environmental law, cost-benefit analysis, and the application of data science techniques to legal texts. He is considered one of the early scholars in the emerging field of computational legal studies, using natural language processing and other computational methods to analyze legal materials. His work bridges traditional legal scholarship with cutting-edge data science approaches, creating new methodologies for understanding law and legal institutions. Livermore hosts the Online Workshop on the Computational Analysis of Law, a global forum for scholars working at this intersection. Livermore is a leading expert on cost-benefit analysis in regulatory contexts, authoring influential books including "Reviving Rationality: Saving Cost-Benefit Analysis for the Sake of the Environment and Our Health" (2020) and "Retaking Rationality: How Cost-Benefit Analysis Can Better Protect the Environment and Our Health" (2008). His recent scholarship increasingly focuses on the intersection of artificial intelligence and law, examining how language models can be applied to legal tasks and how to measure their performance in legal reasoning. As a prolific scholar, Livermore has published dozens of academic works in top law journals as well as peer-reviewed legal, scientific, and social science journals. His 2024-2023 publications demonstrate a clear trend toward computational legal studies, with multiple papers on language model interpretability, judicial analysis using computational methods, and the application of AI to legal tasks. These works span disciplines including legal scholarship, computer science, economics, and empirical legal studies. Livermore is a senior fellow of the Administrative Conference of the United States, reflecting his expertise in administrative law and regulatory process. His scholarship has significantly influenced debates about regulatory policy, particularly regarding environmental regulation and the proper role of cost-benefit analysis. Throughout his career, Professor Livermore has demonstrated a commitment to interdisciplinary collaboration and methodological innovation. His early work established him as a leading voice in environmental regulation and cost-benefit analysis, while his more recent scholarship has positioned him at the forefront of the emerging field of computational legal studies. He has participated in dozens of regulatory proceedings on diverse issues ranging from climate change to prison safety, bridging academic scholarship with practical regulatory challenges.
Jesus Serrano-Guerrero is an Associate Professor in the Department of Information and Technologies Systems at the University of Castilla-La Mancha, Spain, where he has been employed since January 5, 2006. His expertise spans computer science with a focus on information processing, search engines, sentiment analysis, and recommender systems. He actively contributes to the academic community as a reviewer for leading journals in his field including Information Sciences, Information Fusion, and Knowledge-based Systems. Ph.D in Computer Science from University of Castilla-La Mancha (Information Technologies and Systems) Dr. Serrano-Guerrero's research primarily focuses on the intersection of soft computing, information retrieval, and sentiment analysis. His work explores fuzzy logic applications in document processing, recommender systems grounded in aspect-based sentiment analysis, and multimodal approaches for skills assessment. He has made significant contributions to healthcare informatics through emotion detection from health narratives and hospital recommendation systems based on patient feedback. His research demonstrates a consistent trajectory from foundational work in fuzzy systems to applied solutions in healthcare and education domains. His recent publications (2023-2025) reveal a strong focus on healthcare applications of AI, including emotion detection from patient narratives, hospital recommendation systems, and healthcare service evaluation. He has increasingly integrated multimodal approaches and explainable AI techniques into his work, with growing emphasis on practical applications in real-world healthcare settings. His research shows a clear evolution from theoretical fuzzy systems to applied solutions addressing concrete problems in healthcare and education. Applied Intelligence (2 reviews) Artificial Intelligence Review (1 review) Complex & Intelligent Systems (3 reviews) Computers in Biology and Medicine (1 review) Information Sciences (1 review) International Journal of Intelligent Systems (2 reviews) Soft Computing (6 reviews) He has secured substantial research funding throughout his career, with 14 major grants from 2005 to present. His funded projects include SAFER (Analysis and Validation of Software and Web Resources), Métodos rigurosos para el Internet del Futuro, and several projects focused on fuzzy logic applications in information systems. His research group, SMILe Research Group, appears to be actively engaged in multiple interdisciplinary projects bridging computer science with healthcare and education applications. Dr. Serrano-Guerrero leads the SMILe Research Group, which focuses on soft computing applications for information retrieval, sentiment analysis, and recommender systems. The group has developed several notable platforms including BioEmoDetector for emotion detection from health narratives and Itzamna for multimodal transversal skills assessment. Their work demonstrates strong industry and healthcare sector collaborations, particularly in Spain.
Goran Frehse is a Professor at ENSTA Paris where he leads the Semantics of Hybrid Systems (SSH) team and serves as Director of the Computer Science and Systems Engineering Unit (U2IS). His work bridges theoretical computer science with practical engineering applications, focusing on verification methods for complex cyber-physical systems. Dr. Frehse's research interests center on the safety verification of cyber-physical systems, particularly those incorporating artificial intelligence. His expertise spans formal methods for hybrid systems (combining discrete events with continuous dynamics described by differential equations), model checking , reachability analysis , statistical verification techniques , and safe reinforcement learning . His work addresses critical challenges in verifying systems where traditional testing approaches are insufficient due to complexity and safety requirements. Analysis of his recent publications (2021-2024) reveals a growing emphasis on integrating machine learning techniques with formal verification methods. His research increasingly focuses on data-driven reachability analysis , verification of AI-based control systems , and explainable AI safety , reflecting the field's evolution toward addressing the verification challenges posed by modern AI-integrated systems. His work maintains strong connections to practical applications while advancing theoretical foundations. As a research advisor, Dr. Frehse has supervised multiple PhD students including Abdelmouaiz Tebjou, Gwendal Priser, and David Brellmann. His research has been supported through significant projects such as IPL Modeliscale (2017-2021), UnCoVerCPS (2015-2018), and industrial collaborations including a project with DENSO Automotive Deutschland focused on control design verification for complex systems. Dr. Frehse leads the Semantics of Hybrid Systems research team within U2IS, which develops foundational methods and practical tools for system verification. His team's work has direct applications in safety-critical domains including autonomous vehicles, industrial robotics, and defense systems, where formal guarantees of system behavior are essential.
Tsvetomila Mihaylova is a Postdoctoral Researcher in the Department of Computer Science at Aalto University , specializing in machine learning and human-robot interaction. Her work bridges theoretical advancements in neural networks with practical applications in autonomous systems. Fields of Interest : Latent structure learning, autonomous driving, visual-language models, and natural language processing Email : tsvetomila.mihaylova@aalto.fi Research Focus : • Latent structure modeling using discrete and undirected neural networks • Autonomous driving safety through conflict simulation and trajectory prediction • Cross-modal integration in robotic vision-language systems Publication Trends : Recent work explores 2025 in structured neural architectures for autonomous vehicles and 2024 in human-robot interaction quality evaluation, with foundational contributions to natural language processing fact-checking systems since 2019 .
Lucy Lu Wang is an Assistant Professor at the University of Washington Information School, where she leads the Language Accessibility Research (LARCH) Lab. She holds adjunct appointments in the Paul G. Allen School of Computer Science & Engineering, Biomedical Informatics & Medical Education, and Human Centered Design & Engineering. Additionally, she serves as a Research Scientist at the Allen Institute for AI (Ai2). Dr. Wang is an NLP and health informatics researcher focusing on designing and building language technologies to improve access to and understanding of information, particularly in high-expertise domains like science and healthcare. Her work has produced techniques and tools that enhance access to scholarly content, synthesize scientific evidence, and support better health decision-making. She specializes in creating AI systems that make complex information more accessible, with particular attention to accessibility for visually impaired users and improved health communication. Her recent publications demonstrate strong trends across natural language processing for scientific literature, accessibility research, and health informatics. The work shows consistent focus on developing practical tools that bridge the gap between advanced AI capabilities and real-world information needs, particularly in scholarly and healthcare contexts. Many projects address critical issues of accessibility, reliability, and usability of AI systems for diverse user populations. Dr. Wang actively mentors a diverse group of students across multiple departments, including PhD students in the iSchool, HCDE, and CSE. She has successfully guided numerous students who have gone on to positions at institutions like UIUC, Meta, Microsoft, and Netflix. Her research is generously supported by multiple grants and gifts from Google, the National Institutes of Health, Microsoft, and various University of Washington funding programs, focusing on AI-driven accessibility solutions, healthcare applications, and research translation. At the LARCH Lab, Dr. Wang leads a team working on designing, building, and evaluating language technologies that make information more accessible and understandable for everyone, with special emphasis on high-expertise domains such as healthcare and science communication.
Gregory Blanc is a Lecturer in SCN at Telecom SudParis, a leading French graduate school of engineering specializing in digital technologies. His academic work focuses on network security, with particular expertise in intrusion detection systems, software-defined networking security, and machine learning applications for cybersecurity. Dr. Blanc's research spans several critical areas in modern cybersecurity: Development of reinforcement learning frameworks for DDoS mitigation while preserving quality of service Analysis of adversarial attacks against machine learning-based security systems Privacy considerations in intrusion detection systems Security challenges in 5G networks and Internet of Things environments Methods for reproducible evaluation of security tools and frameworks His recent publication record demonstrates consistent contributions to top venues in the cybersecurity field. Over the past two years (2024-2025), he has published 15 papers across journals and conferences, reflecting active research engagement. His work shows strong international collaboration, with co-authors from institutions across Europe and Asia. Dr. Blanc is involved in significant research projects, including the SuperviZ initiative focused on security supervision and orchestration, indicating ongoing research funding and institutional support for his work.
Thomas Vogel is a postdoctoral researcher at the Software Engineering Group within the Institute of Computer Science at Humboldt-Universität zu Berlin . From October 2021 to September 2022, he served as a stand-in professor for Empirical Software Engineering at Paderborn University. He earned his Ph.D. summa cum laude in 2018 from the University of Potsdam under the Hasso Plattner Institute, specializing in model-driven engineering of self-adaptive systems. He graduated with distinction in Information Systems from the University of Bamberg .
Dr. Kevin Hernandez Diaz is a Postdoctoral Researcher at Halmstad University's School of Information Technology, specializing in computer vision and biometrics. His work bridges theoretical advances in deep learning with practical applications in security, industrial safety, and healthcare. His primary research interests include: Ocular and periocular biometrics for unconstrained environments Deep learning model optimization for edge/mobile deployment Computer vision solutions for industrial safety (PPE/fire detection) Explainable AI for biometric verification systems Cross-spectral recognition and synthetic data generation Analysis of his 15 most recent publications (2020-2024) reveals consistent innovation in making biometric systems robust for real-world deployment. His work shows strong emphasis on practical constraints like computational efficiency, pandemic adaptations, and industrial environmental challenges, with increasing focus on explainability and privacy preservation. His scientific contributions include: Novel architectures for lightweight face recognition (SqueezerFaceNet) Explainable verification frameworks for biometric systems Industrial safety applications using YOLOv4 Thermal-based healthcare monitoring solutions Cross-spectral periocular recognition techniques Dr. Hernandez Diaz maintains active research collaboration within Halmstad University's computer vision community and participates in industry-focused projects addressing real-world computer vision challenges. His doctoral work established new benchmarks for ocular recognition in unconstrained settings, and his current research continues to push boundaries in efficient and robust biometric systems.
ChengXiang Zhai is the Donald Biggar Willett Professor in Engineering at the University of Illinois at Urbana-Champaign , holding appointments in the Department of Computer Science , Carl R. Woese Institute for Genomic Biology , and the Department of Statistics . He leads research in intelligent information systems, with a focus on information retrieval, data mining, NLP, and machine learning. His TIMAN research group and DAIS explore applications in healthcare, education, and scientific discovery. He develops MOOCs on text retrieval and mining, and has published extensively on topics including LLM alignment, user simulation, and multimodal systems. Key Research Areas : Intelligent search engines, explainable AI, human-AI collaboration, biomedical informatics Recent Trends : LLM economics, knowledge overshadowing, just-in-time recommendation systems He has received the ACM Fellow title, SIGIR Salton Award , and multiple teaching honors including Rose Award and Graduate Mentoring Award . He serves as series editor for Springer Information Retrieval Book Series .
Dr. Angelos Chatzimparmpas is an Assistant Professor in the Department of Information and Computing Sciences at Utrecht University's Faculty of Science, specializing in the Visualization and Graphics subgroup. His research focuses on developing visual analytics systems to enhance understanding and trust in machine learning models, particularly in the domain of Explainable AI (XAI). He maintains active collaborations with institutions including Northwestern University where he completed his postdoctoral research. Dr. Chatzimparmpas earned his educational credentials through an impressive international trajectory: BSc and MSc in Informatics and Telecommunications Engineering from the University of Western Macedonia, Greece (2017), PhD in Computer and Information Science from Linnaeus University, Sweden (completed February 2023), followed by a postdoctoral position in Computer Science at Northwestern University, USA (completed March 2024). His research interests span Information Visualization, Human-Computer Interaction, and Machine Learning with a specialized focus on Explainable AI. He investigates visual exploration of machine learning models' inner workings, model uncertainty quantification, evaluation of visualization systems using deep learning architectures, and detection of AI-generated images (deepfakes). His work bridges theoretical machine learning concepts with practical visualization techniques to make complex AI systems more transparent and trustworthy for human users. Analysis of his recent publications reveals a clear trajectory focusing on visual analytics for machine learning interpretability. His work systematically addresses challenges in understanding ensemble learning methods, dimensionality reduction techniques, and deep learning models through innovative visualization approaches. A significant portion of his research targets the growing problem of AI-generated content, developing methods to distinguish authentic from synthetic media. His publications appear in top-tier venues including IEEE Transactions on Visualization and Computer Graphics, Computer Graphics Forum, and CHI Conference proceedings. Dr. Chatzimparmpas teaches several advanced courses including Data Science Colloquium, Game Programming, Optimization and Vectorization, and Visual Analytics for Big Data. His teaching directly reflects his research expertise, providing students with cutting-edge knowledge in visualization and machine learning integration. He is actively involved with the Utrecht Platform for Applied Data Science and contributes to research in Applied Data Science, Game Research, and Human-centered Artificial Intelligence. His work demonstrates strong interdisciplinary connections between computer science, cognitive science, and domain-specific applications requiring trustworthy AI systems.
