Xavier Serra is a Full Professor at the Department of Engineering at Universitat Pompeu Fabra (UPF), Barcelona. He is the founder and director of the Music Technology Group (MTG), and leads the UPF-BMAT Chair on AI and Music. He also coordinates the Master in Sound and Music Computing and serves as President of the Phonos Foundation. His research focuses on audio signal processing, sound and music computing, and computational musicology, emphasizing open science and open innovation. Education: BSc in Biology, University of Barcelona (1981) Master in Music, Florida State University (1983) PhD in Computer Music, Stanford University (1989) Research Interests: Audio Signal Processing Data-Driven and Knowledge-Driven Methodologies Music Information Retrieval Cultural Music Analysis (e.g., Carnatic/Turkish/Andalusian Music) Music Education Technology Notable Projects: CompMusic (ERC Advanced Grant, 2010-2017): Multicultural computational music analysis Open datasets: Freesound, Saraga, FSD50K Technologies: Reactable, Vocaloid, Essentia API Recent Trends in Articles: Focus on AI-driven audio processing (neural fingerprints, generative models), cross-cultural music analysis, and explainable music difficulty estimation. Awards: ERC Advanced Grant (2010) for CompMusic Project. Labs/Teams: Director of MTG, Phonos Foundation, and UPF-BMAT Chair. Active in open-source projects and international collaborations.
Dr. George Stamou is a Professor at the School of Electrical and Computer Engineering of the National Technical University of Athens (NTUA), serving as Director of the Artificial Intelligence and Learning Systems Laboratory (AILS). His expertise spans knowledge representation, machine learning, neural networks, and semantic technologies. He leads interdisciplinary initiatives such as the postgraduate program 'Data Science and Machine Learning' (2018–2022). Research Interests: Focuses on knowledge graphs, interpretable AI, semantic web applications, and multimodal learning. His work integrates formal logic systems (e.g., description logics) with modern deep learning techniques, addressing challenges in explainability, bias detection, and ethical AI applications. Publications: Over 150 articles in AI journals/conferences with an h-index of 34 (Google Scholar). Notable contributions include datasets like CHORDONOMICON (music analysis), GOSt-MT (gender bias in MT), and methodologies for counterfactual explanations in machine learning. Awards & Committees: Active in W3C and RuleML standardization bodies. Co-organized major AI conferences. Recognized for contributions to semantic interoperability and knowledge-based systems. Labs & Teams: Directs AILS-NTUA lab and collaborates with CISRI (Computer & Information Systems Research Institute). Engages in EU projects like CultureLabs (cultural heritage digitalization) andsmarty4covid (health data analysis).
Yonatan Bisk is an Assistant Professor at Carnegie Mellon University (CMU) in the School of Computer Science , with dual appointments in the Language Technologies Institute and Robotics Institute . His research bridges Natural Language Processing (NLP) with robotics, focusing on grounded and embodied language understanding. Assistant Professor, Language Technologies Institute, CMU (2021–Present) Courtesy Appointment, Robotics Institute, CMU Research Themes : Language as a social codification of embodied experience Interpretable multimodal model training Human-robot collaboration frameworks Embodied question-answering systems Selected Trends : His recent publications show increasing focus on cross-modal attention mechanisms (Vid2Robot), error detection in toolchains (Tools Fail), and theory-of-mind reasoning in language agents (SOTOPIA). Multimodal integration spans vision, audio, and robotic control contexts (ANAVI). Labs & Collaborations : Founder of CLAW Lab (Connecting Language to Action and the World) Collaborations with Microsoft Research, Meta Inc, and CMU's REAL (Robotics, Embodied AI, Learning) community
Yuvraj Agarwal is a Professor in the School of Computer Science at Carnegie Mellon University , where he leads the SYNERGY Labs . His research focuses on Systems and Networking with emphasis on Embedded Systems , Security , and Energy Efficiency in computing environments. He has been recognized with the NSF Expeditions in Computing Award for Computational Decarbonization research and multiple best paper awards. Education : PhD in Computer Science from University of California, San Diego Research Leadership : Founder/Director of SYNERGY Labs; Executive Director of NSF Expeditions in Variability (2010-2013) Research Trajectory : Recent publications highlight his work on Privacy-preserving smart classroom systems (EduSense, ClassID) IoT security labeling frameworks Audio privacy protection mechanisms Computational decarbonization of infrastructure His research combines hardware-software co-design with societal impact considerations. Scientific Recognition : NSF Expeditions in Computing Award for CoDec (2024) Ubicomp/IMWUT Distinguished Paper Award (2024) CHI Best Paper Honorable Mention (2022) Advising & Collaboration : Mentors students in IoT systems , privacy research , and green computing . Collaborates with institutions including University of Massachusetts Amherst and industry partners like Johnson Controls. Grants include NSF funding for multi-university research initiatives.
Dr A. I. Shihab is a Senior Lecturer at Kingston University's Faculty of Engineering, Computing and the Environment, Department of Networks and Digital Media. He teaches programming languages (C++/Java), data structures, web development, and AI/machine learning. His research focuses on affective computing and machine learning applications including: Acoustic event detection in sports environments Audio signal analysis for tennis match modeling Multi-camera visual surveillance systems Medical imaging analysis using fuzzy clustering techniques Publications demonstrate expertise in combining audio/video modalities for sports analytics (tennis rallies, court-shots) and developing Markov models for sound event sequence analysis. Contact: a.shihab@kingston.ac.uk
Matthew B. Blaschko is a Professor in the Department of Electrical Engineering at KU Leuven, Belgium. He serves as director of the KU Leuven ELLIS unit and is a fellow in the ELLIS Health program. He is a Core PI in the Flanders AI Research Program, working as a workpackage lead for Decision Support Systems and Medical Imaging. Blaschko is also a member of the KU Leuven Institute for Artificial Intelligence and one of the leaders of the working group on Machine Learning and Data Science. Professor Blaschko received his B.S. from Columbia University, M.S. from the University of Massachusetts Amherst, and Dr. rer. nat. from Technische Universität Berlin (awarded for work at Max Planck Institutes Tübingen). He was a Newton International Fellow at the University of Oxford and received his Habilitation (HDR) from École Normale Supérieure de Cachan. Prior to joining KU Leuven, he was a Permanent Research Scientist in the INRIA Saclay Research Center and a Faculty Member at Ecole Centrale Paris. His research focuses on machine learning techniques applied to visual data, with particular emphasis on calibration in deep learning, medical image analysis, and federated learning. Blaschko's work bridges theoretical foundations with practical applications, as evidenced by technology developed in his research being incorporated into MONA, software for ophthalmic image analysis. His research group has made significant contributions to the fields of model calibration, uncertainty estimation, and medical imaging analysis, with recent publications showing strong trends toward improving reliability of AI systems in medical contexts and advancing theoretical understanding of calibration metrics. Professor Blaschko has been recognized with several awards including the Université Paris-Saclay STIC Doctoral School Best Scientific Contribution Award, Best Paper Award at CVPR 2008, Main Award at DAGM 2008, and Best Student Paper Award at ECCV 2008. Professor Blaschko has supervised numerous PhD and Master's students, with current and former students including Deniz Soysal, Claire Marchal, Dongli Xu, Sebastian Gruber, Jiameng Li, Marco Mezzina, and many others working on diverse topics from Alzheimer's disease analysis to surgical phase recognition. His research has been supported by various funding sources including the Flanders AI Research Program. He has co-organized several influential workshops including the "Another Brick in the AI Wall: Building Practical Solutions from Theoretical Foundations" at CVPR 2025, Commands 4 Autonomous Vehicles workshop at ECCV 2020, and the Learning from Limited Labeled Data workshop series at NIPS 2017 and ICLR 2019. His laboratory focuses on machine learning for medical image analysis, with applications in ophthalmology, neurology, and surgical robotics. The group maintains active collaborations with medical institutions and participates in international challenges such as the KNee OsteoArthritis Prediction (KNOAP2020) challenge.
Danqi Chen is an Associate Professor in the Department of Computer Science at Princeton University's School of Engineering and Applied Science. Their research focuses on advancing large language models (LLMs), with emphasis on model alignment, safety, and long-context reasoning capabilities. Key research areas: LLMs, AI safety, retrieval systems, and model optimization Recent work explores theorem proving, context encoding, and ethical content generation Their 2025 publications highlight innovations in formal verification scaffolding, attention mechanism efficiency, and copyright-aware generation. 2024 studies investigate continual memorization, rule-based chatbot representations, and scientific literature retrieval benchmarks. Current projects demonstrate commitment to improving model robustness, interpretability, and security compliance in multimodal systems.
Jiajun Wu is an Assistant Professor of Computer Science and, by courtesy, of Psychology at Stanford University. He holds multiple affiliations including membership in Bio-X, Faculty Affiliate status at the Institute for Human-Centered Artificial Intelligence (HAI), and membership in both the Wu Tsai Human Performance Alliance and Wu Tsai Neurosciences Institute. Dr. Wu earned his Ph.D. and S.M. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology before joining Stanford. Dr. Wu's research program focuses on creating AI systems that understand and interact with the physical world through the integration of computer vision, machine learning, robotics, and cognitive science. His work emphasizes physics-based modeling combined with deep learning to develop systems capable of perceiving, reasoning about, and predicting physical interactions. Key research areas include 3D scene understanding, neurosymbolic AI approaches, multimodal perception (combining vision, sound, and language), and embodied intelligence for robotics applications. His lab develops novel frameworks that bridge the gap between neural networks and symbolic reasoning to create more interpretable and robust AI systems. Analysis of Dr. Wu's recent publications reveals a strong trajectory toward integrated multimodal understanding for embodied AI. His work increasingly combines vision, sound, and language processing with physical reasoning to create systems that can interact meaningfully with the physical world. There's a clear progression from foundational computer vision research toward practical robotics applications, with significant emphasis on foundation models for robotics, sim2real transfer techniques, and creating comprehensive datasets for embodied AI research. Dr. Wu's exceptional contributions have been recognized with numerous prestigious awards including the NSF CAREER award (2024), Young Investigator Programs from ONR (2024) and AFOSR (2023), the Okawa research grant (2024), and being named to IEEE Intelligent Systems' 'AI's 10 to Watch' (2024). He has received multiple best paper awards at leading conferences including ICRA (2024), SIGGRAPH Asia (2023), and CoRL (2023). Dr. Wu actively mentors a large cohort of students across multiple levels, serving as primary advisor for doctoral candidates, master's students, and numerous independent researchers. His research is supported by substantial funding from major technology companies including Google, Meta, Amazon, Samsung, and J.P. Morgan, as well as government agencies like NSF, ONR, and AFOSR, reflecting the significance and impact of his work in physical AI and multimodal perception systems. Dr. Wu leads a dynamic research group at Stanford that collaborates extensively with the Wu Tsai Neurosciences Institute and Institute for Human-Centered AI. Current projects include developing neurosymbolic models for computer graphics, creating multisensory datasets like OBJECTFOLDER 2.0 for sim2real transfer in robotics, and building foundation models for embodied intelligence that can understand and manipulate objects with human-like physical intuition.
Ivan Flechais is an Associate Professor in Software Engineering at the Department of Computer Science, University of Oxford. His work focuses on the intersection of security engineering and human factors, developing approaches that balance technical security requirements with usability considerations in real-world contexts. Dr. Flechais earned his BSc and PhD in Computer Science from University College London. He holds dual French-British nationality and was educated in France until university level. His academic journey reflects an international perspective that informs his research on security systems across different cultural contexts. Flechais's research centers on developing methods for creating secure systems that account for real-world usability constraints. His work addresses the complex challenge where security competes with other system requirements like functionality, usability, and efficiency. He is particularly known for developing the AEGIS design methodology, which provides a cost-effective approach to security design that incorporates usability considerations. His current research explores socio-organizational factors in secure systems design, with recent work focusing on smart home security and privacy, remote work security challenges, and the intersection of security culture with technical implementation. His publication record shows a strong trajectory in usable security research, with recent work (2020-2024) increasingly focused on smart home environments, privacy in domestic settings, and the security challenges of remote work. His research demonstrates consistent attention to the human element in security systems, examining how users interact with security mechanisms in real-world contexts across different cultural settings and technological domains. Smart home security and privacy challenges User experience of security mechanisms Socio-organizational aspects of security implementation Cross-cultural security and privacy considerations Security for distributed and remote work environments Dr. Flechais has supervised numerous PhD and Master's students, including Sarah Alromaih, Varad Vishwarupe, George Chalhoub, and Martin J. Kraemer, among others. His supervision work often focuses on the practical application of security principles in emerging technologies, with students frequently examining security challenges in smart homes, IoT devices, and remote work contexts. His research has been supported through various projects including webinos and Sponstaneous Security, which address security challenges in distributed mobile applications and ad-hoc network environments.
Ki-Woong Park is a tenure-track full Professor in the Department of Computer and Information Security at Sejong University. He leads the System Security and Computer Engineering Research (SysCore) Lab, which focuses on system security research with numerous ongoing projects funded by major Korean research institutions including IITP, NRF, and KRIT. Sejong University, Department of Computer and Information Security System Security and Computer Engineering Research (SysCore) Lab Leader Member of IEEE, IEEE Computer Society, and ACM Education: Ph.D. in Electrical Engineering & Computer Science, KAIST (Advisor: Prof. Kyu-Ho Park) M.S. in Electrical Engineering & Computer Science, KAIST (Advisor: Prof. Kyu-Ho Park) B.S. in Computer Science, Yonsei University (Summa Cum Laude) Exchange Student at University of California, Los Angeles (UCLA) Professor Park's research focuses on designing, building, and analyzing secure systems, particularly for cloud computing, networked systems, and embedded systems. His work often involves reevaluating existing security mechanisms and actual system implementations with subsequent evaluation in real computing environments. He has made significant contributions to areas including cloud security, IoT security, ransomware detection, moving target defense, and metaverse security. His research approach emphasizes both theoretical foundations and practical implementation, with numerous publications in top-tier security and systems venues. His recent publications (2023-2024) demonstrate a strong focus on emerging security challenges in modern computing environments, particularly in metaverse platforms, UAV systems, and edge computing. These works span both theoretical security frameworks and practical implementations, with an emphasis on visualization techniques, hardware-based security mechanisms, and AI-enhanced security analysis. His research shows a clear progression from traditional cloud and network security toward next-generation security challenges in immersive virtual environments and cyber-physical systems. Scientific Awards: Microsoft Research Fellowship (2009-2010) Best Poster Gold Award at WISA 2020 Best Paper Award at MobiSec'18 Professor Park actively mentors numerous graduate and undergraduate students through the SysCore Lab, with current members including Ph.D. students, MS students, and undergraduate researchers. His research is supported by multiple significant grants, including the NRF Outstanding Researcher-Mid-career Researcher project, IITP Information Security Core Source Technology Development, and Defense Technology Advancement Research Institute projects. These grants total tens of billions of Korean won and address critical national security challenges in cyber defense, cloud security, and metaverse technologies. The SysCore Lab, under Professor Park's leadership, maintains a strong industry and government collaboration network, with part-time researchers from organizations including Hyundai Duty Free, Astron Security, Korea University, and various military cyber commands. This unique structure enables the lab to address both theoretical security challenges and practical implementation issues in real-world systems.
Noah A. Smith is an Adjunct Professor of Computer Science and Engineering at the University of Washington. His work focuses on computational linguistics, machine learning, and natural language processing. He holds a Ph.D. in Computer Science from Johns Hopkins University (2006). His research explores ethical AI applications, multimodal systems, and foundational aspects of language models. Key research areas include: Ethical considerations in NLP, such as detecting rights abuses through text analysis Efficient decoding and alignment strategies for large language models Large-scale evaluation frameworks for multitask and multimodal generation Understanding pretraining dynamics and data composition effects Recent work emphasizes transparency in language models (e.g., tracing outputs to training data) and improving alignment through human feedback. He has contributed to open-source projects like OLMo and Dolma, advancing reproducibility in NLP research. No awards explicitly listed in provided texts. No specific advising or grant details available, though extensive publication output indicates active research involvement.
Christopher Kruegel is a Professor in the Department of Computer Science at the University of California, Santa Barbara (UCSB), where he conducts research in systems security. He is affiliated with the International Secure Systems Lab (iSecLab) and was a co-founder of Lastline, Inc., which was acquired by VMware in 2020. His work focuses on creating practical security solutions that address real-world problems through system building and experimental validation. Professor Kruegel's research spans multiple areas of computer security including malware analysis, web security, network security, and vulnerability analysis. His work often involves developing systems that analyze programs for malicious behavior, scanning web applications for vulnerabilities, and improving privacy on social networks. He has made significant contributions to firmware security, smart contract analysis, and mobile security through his extensive publication record. His 15 most recent publications (2022-2023) demonstrate a strong focus on practical security solutions across diverse domains including firmware security (Shimware, Fuzzware), blockchain and smart contract security (Confusum Contractum, NFT security), mobile security (TEEzz, Columbus), and vulnerability detection techniques (Actor, Toss a Fault to Your Witcher). His research shows consistent innovation in security tools and methodologies with several papers receiving distinguished awards. Fellow of the Institute of Electrical and Electronics Engineers Outstanding Graduate Mentor Award, UCSB Academic Senate Distinguished Artifact Award for Fuzzware paper Distinguished Paper Award for Ramblr paper Best Student Paper Award for Detecting Spammers On Social Networks Professor Kruegel has advised numerous graduate students and collaborated extensively with researchers at UCSB and beyond. His work has been funded by various research grants supporting his security research initiatives. He is actively involved in the International Secure Systems Lab, which focuses on practical security solutions for real-world problems.
Yevgeni Berzak is an Assistant Professor at the Technion and a Research Affiliate at Mit BCS . He directs the Technion Language, Computation and Cognition (LaCC) Lab and leads the Cognitive Science Track in the Data Science and Engineering degree at the Technion. His academic background includes: PhD in Computer Science at Infolab, MIT CSAIL Postdoctoral research with Roger Levy at MIT CPL Lab Masters in Computational Linguistics at Universities of Saarland and Nancy Bachelors in Cognitive Science and Amirim Honors Program at Hebrew University of Jerusalem His research focuses on the intersection of Cognitive Science and Natural Language Processing (NLP) , studying human language acquisition and processing through computational modeling, linguistic theory, and neuroimaging. He develops datasets like OneStopQA and CELER , and explores how human gaze patterns can improve machine language understanding. Recent publications address topics such as: Eyetracking for language proficiency assessment Repetition effects in reading behavior Readability prediction via scrolling interactions Structured annotation schemes for reading comprehension The LaCC Lab under his leadership combines theoretical linguistics with machine learning to bridge human and artificial language processing.
David De Roure is Professor of e-Research at the University of Oxford and Academic Director of both the Digital Scholarship initiative and the Laboratory for AI Security Research. He is also an Honorary Research Professor at the Royal Northern College of Music (RNCM), where he serves as Technical Director of the Centre for Practice & Research in Science & Music (PRiSM). His work bridges computer science, digital humanities, cybersecurity, and music through his distinctive interdisciplinary approach. De Roure received his PhD in 1990 supervised by David W Barron and Peter Henderson, with research in Lisp and distributed systems. Prior to joining Oxford in 2010, he was Professor of Computer Science at the University of Southampton and Director of the Centre for Pervasive Computing in the Environment. His career spans multiple institutions and research domains, reflecting his commitment to interdisciplinary work. De Roure's research focuses on new methods of digital scholarship, innovation in knowledge infrastructure, cybersecurity, and computational approaches to music. His work uniquely combines humanities (digital musicology), social sciences (social machines and web science), engineering (Internet of Things), and computer science (distributed systems, AI). A key theme is empowering human creativity through technology rather than replacing humans with AI. He emphasizes co-creation between humans and machines, particularly in music composition where he explores how algorithms can generate fragments for human assembly. His recent publications reveal a strong focus on AI security in IoT systems, digital scholarship methods, and the intersection of music with computational approaches. There's a clear trajectory from foundational work in social machines and web science toward current applications in cybersecurity and music-AI co-creation. His publications consistently bridge technical domains with humanistic inquiry, demonstrating his commitment to interdisciplinary scholarship that addresses real-world challenges. Fellow of the British Computer Society (FBCS) Fellow of the Institute of Mathematics and its Applications (FIMA) Fellow of the Royal Society of Arts (FRSA) Chartered IT Professional (CITP) Turing Fellow at The Alan Turing Institute (2018-2024) De Roure has co-founded three major interdisciplinary initiatives: PETRAS National Centre of Excellence for IoT Systems Cybersecurity (the world's largest socio-technical research center focused on IoT security), the Software Sustainability Institute (dedicated to improving research software), and PRiSM at RNCM. He was Director of the Oxford e-Research Centre from 2012-17 and has led numerous research projects including SOCIAM (The Theory and Practice of Social Machines), FAST (Fusing Audio and Semantic Technologies), and Transforming Musicology. The Laboratory for AI Security Research, which he directs, took its first PhD students in 2024. At Oxford, De Roure chairs the Digital Research Cluster at Wolfson College and oversees the Laboratory for AI Security Research. The PRiSM team at RNCM has produced numerous musical works and performances, including six premieres in New York in 2024. He has been involved in designing gesture recognition software used in many performances and has collaborated on public engagement projects including the Science Together project which released a Hip Hop album. His current work includes exploring Chladni Plates for new musical instrument design and developing algorithmically enhanced instruments.
Gül Varol is a permanent researcher at École des Ponts ParisTech's IMAGINE group, an ELLIS Scholar, and Guest Scientist at Max Planck Institute. She holds a PhD from Inria Paris/ENS with awards from ELLIS and AFRIF. Her academic service includes Program Chair at ECCV'24 and Area Chair roles at major conferences. Current affiliations: IMAGINE group (École des Ponts ParisTech), Max Planck Institute Previous roles: Postdoctoral researcher at University of Oxford Her research focuses on vision-language applications, particularly in 3D human motion synthesis, sign language technology, and audio description generation. Key techniques include text-conditioned diffusion models, temporal context modeling, and synthetic data utilization. Scientific contributions recognized through: Google Research Scholar award (2023) ELLIS PhD Award (2020) AFRIF PhD thesis award (2020) Best application paper at ACCV'20 Recent publications demonstrate expertise in: Text-driven 3D motion editing (MotionFix, 2024) Cross-dataset generalization studies (TMR++, 2024) Temporal action composition frameworks (TEACH, 2022) Sign language dense annotation methods (BOBSL, 2022) Zero-shot audio description generation (AutoAD-Zero, 2024) She actively contributes to dataset development including BOBSL (British Sign Language corpus) and SURREACT synthetic action dataset, while pioneering new evaluation metrics for audio description quality and motion retrieval benchmarks.
