Seungbae Kim is an Assistant Professor at the Bellini College of Artificial Intelligence, Cybersecurity, and Computing at the University of South Florida. He directs the Connected Social Artificial Intelligence Lab (CSAIL) and co-directs the Artificial Intelligence Research Group (AIR) within the university. Previous Affiliation: Postdoctoral Researcher at UCLA's Communication Department under Professor Jungseock Joo. Education: PhD in Computer Science from UCLA, affiliated with the Scalable Analytics Institute (ScAi) under Professor Wei Wang. His research integrates graph-based learning , multimodal learning , and generative AI to advance responsible AI applications in healthcare, mental health, and social systems. Key methodologies include Graph Neural Networks (GNNs) , diffusion models , and cross-modal alignment . Recent publications focus on depression detection , algorithmic fairness , and social media analytics . While his work explores AI for social good , no scientific awards are explicitly mentioned. His research groups emphasize interdisciplinary collaboration between AI , healthcare , and social sciences .
Dr. Moritz Herrmann is a postdoc researcher and Reproducibility & Open Science Transfer Coordinator at the Munich Center for Machine Learning (MCML). He is affiliated with the Biometry in Molecular Medicine working group led by Prof. Anne-Laure Boulesteix at Ludwig-Maximilians-Universität München, and contributes to initiatives like the LMU Open Science Center , Open Science Initiative in Statistics (OSIS) , and Open Science Initiative in Medicine (OSIM) . Ph.D. in Statistics from LMU (2022), M.Sc. in Statistics (2018), and B.Sc. in Mathematics/Sports Science (2014) His research focuses on Empirical Machine Learning , Manifold Learning , and Metascience , with emphasis on epistemological foundations and reliability in ML research. He advocates for open science practices and data literacy, as outlined in his ICML 2024 position paper on rethinking empirical ML research. As a member of the Empirical Machine Learning research focus group and the Statistical Learning and Data Science Chair , Herrmann bridges statistical methodology with biomedical applications. His work spans outlier detection, cluster analysis, and reproducibility frameworks, reflected in his recent publications in journals like Biometrical Journal and Data Mining and Knowledge Discovery .
Matej Vitek is an Assistant and researcher at the Faculty of Computer and Information Science, University of Ljubljana, where he is a core member of the Computer Vision Laboratory (CVL). His work centers on advancing biometric security through innovative computer vision techniques, with primary focus on lightweight sclera recognition systems. His academic journey includes: BSc in Computer Science and Informatics and Mathematics and Physics (2015), University of Ljubljana MSc in Computer Science and Informatics and Mathematics and Physics (2018), University of Ljubljana PhD in Computer and Information Science (2024), University of Ljubljana Vitek's research expertise spans computer vision, biometrics, and deep learning, with specialized focus on sclera recognition. His methodology emphasizes developing computationally efficient models suitable for mobile deployment while addressing critical challenges like model bias and segmentation accuracy. Past explorations include quantum computing circuits and game development, demonstrating interdisciplinary versatility. Current work integrates anomaly detection for deepfake identification and explainable AI frameworks for biometric systems. Publication trends reveal consistent advancement in sclera biometrics through large-scale collaborative efforts, including benchmarking competitions and novel dataset creation. His work demonstrates progression from foundational segmentation studies toward optimized lightweight architectures and bias mitigation strategies, reflecting the field's evolution toward practical, ethical deployment. Vitek actively contributes to two major ARRS-funded projects: J2-50065 'DeepFake DAD' (2023-2026) developing anomaly detection methods for deepfake identification, and J2-50069 'MIXBAI' (2023-2026) creating interpretable mechanisms for explainable biometric AI. Previously, he participated in the P2-0214 Computer Vision research program (2019-2024) and consulting initiatives. As a CVL laboratory member, he collaborates within a specialized biometrics team led by Prof. Peter Peer (supervisor) and Prof. Vitomir Štruc (co-supervisor), working alongside researchers Peter Rot, Žiga Emeršič, and Blaž Meden. His technical environment combines academic research with practical implementation challenges in resource-constrained settings.
Dr. Helia Farhood is an Honorary Senior Research Fellow at the School of Computing, Macquarie University, specializing in Artificial Intelligence, Machine Learning, and Image Processing with applications in educational technology and object recognition. Her academic qualifications include a PhD in Computer Systems and Artificial Intelligence from the University of Technology Sydney (awarded November 2021) and a Master's degree in Computer-AI from Amirkabir University of Technology (Tehran Polytechnic, awarded September 2013). Dr. Farhood's research spans interdisciplinary AI applications, with significant contributions in student outcome prediction using generative adversarial networks, explainable AI through LIME heatmaps, and image-based storytelling systems. Her work integrates machine learning with educational data mining to enhance creativity assessment and learning analytics, while maintaining strong technical focus on 3D reconstruction and object recognition. Analysis of her 16 publications (2020-2025) reveals three dominant research trajectories: (1) AI-driven educational analytics for student performance prediction, (2) advanced image processing techniques for object recognition and 3D reconstruction, and (3) systematic reviews establishing methodological foundations in presentation attack detection and image-based storytelling. Her recent work increasingly emphasizes explainability and ethical considerations in AI deployment. Dr. Farhood has participated in externally funded research projects, including the 2022 project "Estimating the Number of Tyres in Stockpiles" (October-December 2022). No information is available regarding students she has advised. No information is available about specific research laboratories or teams led by Dr. Farhood.
Lena Jaeger is an Associate Professor of Digital Linguistics at the University of Zurich, where she leads research at the intersection of linguistics, computational cognitive science, and machine learning. She joined the Chair of Computational Linguistics at UZH in July 2020 after establishing a Machine Learning Junior Research Group at the University of Potsdam, funded by the German Federal Ministry of Education and Research. Her educational background spans multiple disciplines: she earned an MA in Chinese Language and Culture (Sinology) from the University of Freiburg im Breisgau, Tongji University Shanghai, Beijing Language and Culture University, and Université Paris 7 Denis-Diderot; followed by an MSc in Experimental and Clinical Linguistics at the University of Potsdam; and completed her doctorate in cognitive science at the same institution. Notably, she also earned a bachelor's degree in computer science during or after her doctoral studies. Professor Jaeger's research focuses on investigating cognitive mechanisms underlying human language processing using experimental psycholinguistics, computational modeling, and machine learning methods. Her current work develops machine learning techniques for analyzing eye-tracking data to understand cognitive processes reflected in eye movement behavior. This interdisciplinary approach combines insights from linguistics, cognitive science, and artificial intelligence to create models that bridge human and machine language understanding. Her recent publications reveal a strong trend toward developing eye-tracking methodologies, creating multilingual corpora, and applying machine learning to understand reading behavior and language processing. Her work spans from fundamental research on cognitive mechanisms to practical applications in educational technology, medical diagnostics, and AI development. Best student late breaking work award for Reporting Eye-Tracking Data Quality: Towards a New Standard Best short paper award for Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models Professor Jaeger actively supervises multiple PhD students across computational linguistics, machine learning, and phonetics disciplines. Her research group collaborates extensively on large-scale projects like the MultiplEYE initiative, which establishes standards for multilingual eye-tracking data collection. She has secured significant research funding, including a Machine Learning Junior Research Group grant from the German Federal Ministry of Education and Research before moving to UZH. Her laboratory work centers on eye-tracking methodologies, developing tools like pymovements for eye movement data processing, and creating comprehensive corpora such as MECO (Multilingual Eye-Movement Corpus), MultiplEYE, and CoLAGaze. These resources support cross-linguistic research on reading behavior and language processing across diverse populations.
Prof. Erhardt Barth is the Deputy Director at the Institute of Neuro- and Bioinformatics (INB), University of Lübeck . His research focuses on Computer Vision and Machine Learning , drawing inspiration from biological vision systems to develop hybrid human-machine solutions. Education : PhD in Electrical Engineering (1994), Technical University of Munich Positions : Research Associate (Munich), Visiting Fellow (Melbourne), Klaus-Piltz Fellow (Berlin), NASA Vision Science Group member His work bridges computer vision and biological vision , exploring how neural mechanisms can inform algorithm design. Recent projects include deep learning applications in medical imaging (CT scans, radiographs) and bio-inspired neural architectures like FP-Nets and Min-Nets. Publications span medical diagnostics , image processing , and network efficiency . Key trends include explainable AI in healthcare, compact network design , and unsupervised learning techniques. Scientific distinctions : Recipient of the Schloessmann Award (Max Planck Society, 2000) Held prestigious Klaus-Piltz Fellowship (Institute for Advanced Study, Berlin) Prof. Barth has pioneered technology transfer initiatives, founding companies like GazeCom and gestigon while advancing automated diagnostic frameworks in clinical settings.
Nicolas ANCIAUX is a Research Director at INSA Centre Val de Loire and affiliated with LIFO (Laboratoire d'Informatique Fondamentale d'Orléans). His research focuses on privacy-enhancing technologies, data security, and applied cryptography, with emphasis on telework monitoring, LLM vulnerabilities, and secure data management systems. He collaborates extensively with institutions like Inria and international researchers. Research Interests: His work spans computer security, privacy engineering, and machine learning security. Key areas include: Privacy-enhancing technologies (PETs) for data minimization and consent Detection of privacy violations in telework environments via energy trails Membership inference attacks on large language models Secure extensible data systems with vulnerability mitigation Publications Focus: Recent articles (2024-2025) explore LLM security flaws, energy-based activity detection, and PET implementations. Themes include adversarial robustness, behavioral analytics, and technical-legal alignment in AI systems. Affiliations: Primary institutional links are with INSA Centre Val de Loire and LIFO. Collaborative networks include researchers from Université d'Orléans, Universidad Carlos III de Madrid, and Inria teams.
Mohamed Abomhara is a Senior Researcher and Head of Department at the Department of Information Security and Communication Technology, Norwegian University of Science and Technology (NTNU) , Faculty of Information Technology and Electrical Engineering. He serves as Discipline Leader for the MRI PET (Multidisciplinary Research group on Privacy and data protEcTion) research group. Research Interests His expertise includes: GDPR compliance and privacy-by-design Risk assessment and secure system design AI ethics and social-cyber risk mitigation Healthcare digital transformation security Border control technology ethics Recent Publications His 2024-2025 research focuses on multilingual hate speech detection , border control technology acceptance , and privacy protections in national identification systems , bridging AI ethics, cybersecurity, and regulatory compliance. Earlier work (2016-2022) addresses: Cybersecurity in digital substation infrastructure Secure collaborative healthcare information sharing Blockchain-based GDPR compliance AI-driven social media analysis
Raghavendra Ramachandra is a Professor at the Department of Information Security and Communication Technology , NTNU (Norwegian University of Science and Technology) . His research focuses on Biometrics , Deep Learning , and Image Processing , particularly addressing Morphing Attack Detection and Presentation Attack Detection in biometric systems. Current Projects: SALT (2022-2026) : Developing privacy-preserving face biometric authentication with morphing attack detection. OffPAD (2022-2025) : Creating cryptographic tools and fingerprint attack detection techniques. SWAN (2015-2020) : Mitigating presentation attacks in biometric access control. Research Interests: Biometric security, deep learning for fake face detection, smartphone-based biometric authentication, and morphing attack countermeasures. Publication Trends: His recent work explores advanced neural network architectures for attack detection (e.g., attention networks, vision transformers), synthetic dataset generation, and multi-sensor biometric verification. Technical Expertise: Combines computer vision, machine learning, and cryptographic security for robust biometric systems. Contact: raghavendra.ramachandra@ntnu.no
Corey Clark, Ph.D., serves as Deputy Director of Research and Assistant Professor in the Department of Computer Science and Engineering at Southern Methodist University's Lyle School of Engineering. He leads the Human and Machine Intelligence (HuMIn) Game Lab, pioneering research at the intersection of gaming, artificial intelligence, and human computation to solve large-scale problems in healthcare, education, and national security. Dr. Clark holds a Ph.D., M.S., and B.S. in Electrical Engineering from The University of Texas at Arlington, where he graduated Magna Cum Laude for his undergraduate degree. His doctoral research focused on nanoscale modeling and simulation techniques for Molecular Beam Epitaxy and Chemical Vapor Deposition of exotic materials. His research spans Artificial Intelligence, Game Development, Human Computation, Distributed Computing, Machine Learning, Computational Biology, and Educational Technology. Clark is renowned for transforming commercial video games into distributed computing platforms through human computation, enabling breakthroughs in medical diagnostics and educational technology. His technical innovations in HTML5/JavaScript multithreading have been featured at major international game conferences. Analysis of Clark's 15 most recent publications (2022-2024) reveals a dominant focus on generative AI applications integrated with gaming mechanics, particularly for knowledge graph enhancement, medical diagnostics, and computational thinking education. Key trends include explainable AI systems, human-AI collaboration through gameplay, blockchain applications, and privacy-preserving machine learning, demonstrating consistent innovation in applying game-based approaches to real-world problems. Dr. Clark has secured over $1.7 million in competitive research funding, including: Human Computation for Ocular Tomography Analysis ($62,924) with Retina Foundation Cryptocurrency Transactional Analysis via Gaming ($60,000) with Raytheon Game-Based Adult Literacy (XPrize) ($480,000) Flexible Electronics for Airborne Laser ($820,000) with Missile Defense Agency Networked C4ISR Chip ($850,000) with US Army As CTO for Dallas-based game technology companies, Clark has helped raise over $12 million in startup funding. His HuMIn Game Lab currently develops immersive gameplay systems for cancer treatment research using crowdsourced computation and machine learning, with recent projects including therapeutic discovery platforms and blockchain-based task completion systems.
Peter Peer is a Full Professor at the University of Ljubljana's Faculty of Computer and Information Science, where he leads the Computer Vision Laboratory. He serves as Executive Editor for ICT Express , Area Editor for IEEE Access and IET Biometrics , and coordinates dual-degree programs with Kyungpook National University. His administrative roles include membership in the Faculty Board of Directors (2018-present) and Senate (2021-present), and he previously served as Vice-Dean for Economic Affairs (2018-2022). His research spans computer vision and biometrics , with specialization in privacy-enhancing technologies, deep learning applications, and multimodal recognition systems. Key focus areas include: Face/sclera/ear biometric recognition and segmentation Deepfake detection and media forensics Generative models for data privacy Efficient model optimization techniques Publication analysis shows strong emphasis on biometric security (65% of recent works), privacy-preserving AI (25%), and generative modeling (10%), with applications spanning surveillance, forensics, and human-computer interaction. Awards highlight leadership in international biometric competitions and recognition for high-impact publications. Significant scientific honors include: NIST FATE evaluation winner (2025) Top 3 placements in ACM/IEEE biometric competitions (2023-2024) IEEE Transactions top-downloaded articles (2022-2024) European Association for Biometrics awards (2021-2024) He mentors 10+ PhD students working on biometric recognition, privacy preservation, and deep learning applications. Research is supported by national grants including DeepFake DAD (2023-2026) and MIXBAI (2023-2026), focusing on explainable AI and deepfake detection. Leads the Computer Vision Laboratory with international collaborations across Europe and Asia.
Dr. Sanaul Hoque is a Senior Lecturer in Secure Systems Engineering at the University of Kent's School of Engineering and Digital Arts, where he also serves as Course Lead for Electronic and Computer Engineering. His research focuses on biometric security systems, computer vision, and pattern recognition applications with over 79 publications documented in the Kent Academic Repository. Dr. Hoque's primary research interests include: Computer Vision and Pattern Recognition Biometrics and Security Systems Document Analysis and Modeling Encryption and Secure Systems Multi-Expert Fusion Techniques EEG-based Biometric Recognition His extensive publication record spanning over two decades demonstrates expertise in developing innovative biometric security solutions. Recent work focuses on neural network applications for security systems, explainable presentation attack detection, and EEG-based biometric recognition. His research shows a consistent trajectory toward more sophisticated and explainable biometric security systems that address real-world challenges in authentication and identification, with his most recent publication appearing in 2025. Dr. Hoque has maintained productive research collaborations with colleagues including Farzin Deravi, Konstantinos Sirlantzis, and Gareth Howells, resulting in numerous co-authored publications across various domains of biometric security. His work has appeared in reputable journals including Sensors, IEEE Access, and Pattern Analysis and Applications. As Course Lead for Electronic and Computer Engineering, Dr. Hoque plays a significant role in curriculum development and student mentorship within the engineering program at Kent. His expertise bridges theoretical computer science with practical security applications, particularly in biometric systems and secure document modeling.
Oh-Young Song is a Professor in the Department of Software at Sejong University's Ocean Humanities College, where he has been a faculty member since 2006. He also serves as a Researcher at the eXtended Reality Research Center and directs the Sejong University Graphics Lab, which focuses on natural phenomena simulation and related applications in special effects and animation. Sejong University, Department of Software (2006-present) Adobe Systems, Visiting Professor (2013-2014) Seoul National University, Automation and Systems Research Institute (2004-2006) Song earned his educational credentials from Seoul National University: B.S. (1998), M.S. (2000), and Ph.D. (2004), all in Electrical Engineering and Computer Science. His research spans computer graphics, VR/AR/MR, physics-based animation, fluid simulation, and deep learning . His work bridges theoretical computer graphics with practical applications in healthcare, IoT, and special effects. Song has developed physics-based animation techniques used in movies and games, with particular expertise in fluid simulation where he holds a US patent for simulating detailed fluid movements using derivative particles. His recent research increasingly integrates AI and deep learning approaches with traditional computer graphics techniques. Analysis of his 15 most recent publications (2021-2024) reveals a strategic expansion of his research into healthcare applications, with significant work in medical image analysis, gastrointestinal disease detection, and secure patient data transmission. His research shows strong interdisciplinary collaboration, particularly between computer graphics and medical informatics, with consistent publication in high-impact journals across computer science and healthcare domains. Song's scholarly achievements include 49 research outputs with 1922 citations and an h-index of 20. His most notable scientific achievement is the US-patented technique for simulating detailed fluid movements using derivative particles, with related papers published in ACM Transactions on Graphics. US Patent for Method of Simulating detailed movements of fluids using derivative particles 49 research publications across computer graphics, VR/AR, and medical AI domains 1922 total citations with h-index of 20 As an educator, Song teaches courses including Advanced Computer Graphics, Algorithms and Practice, and Physics Coding for General Public. He advises graduate students through Master's thesis research and Graduation Research and Career courses. His lab, the Sejong University Graphics Lab, actively collaborates with industry on physics-based animation techniques for special effects and animation in movies and games. Current research directions indicate growing emphasis on integrating extended reality technologies with healthcare applications, particularly in medical simulation and diagnostic support systems.
Professor Song Oh-young is affiliated with Sejong University as a faculty member in the Ocean Humanity College , Department of Software . He leads the Sejong University Graphics Lab , which focuses on physics-based animation for movies/games and natural phenomena simulation. PhD in Computer Science from Seoul National University (2004) Postdoctoral Fellow at Seoul National University (2004-2006) Current research spans Computer Graphics , Medical AI , IoT Security , and Deep Learning Applications in healthcare and agriculture. His recent publications (2023-2024) emphasize AR/VR for emergency training , medical imaging , and IoT optimization . The Graphics Lab actively collaborates with industry on special effects and simulation techniques. He has contributed to 3D graphics , chaotic encryption , and real-time fluid dynamics over two decades.
Faiza Allah Bukhsh is an Associate Professor specializing in Artificial Intelligence, Data Mining, Process Mining, Health Informatics, Cybersecurity, and Ethical AI. Her work bridges technical innovation with societal impact, particularly in healthcare systems analysis, telecommunications resilience, and ethical data governance. Digital Society Institute TechMed Centre Datamanagement & Biometrics Her research focuses on Explainable AI , Process Mining , and Privacy Assurance in healthcare systems, with recent work on AI music perception, sepsis treatment analysis, and privacy-utility trade-offs. Key article trends include: AI in music and creative domains Process Mining for healthcare insights Explainable Machine Learning workflows Privacy-preserving analytics Telcom infrastructure resilience
