Dr. João Henriques is a Research Fellow of the Royal Academy of Engineering (RAEng) at the Visual Geometry Group (VGG), University of Oxford. His research focuses on advancing computer vision, deep learning, and robotics, particularly in areas like 3D scene understanding, reinforcement learning, and multi-agent systems. He is renowned for developing the KCF and SiameseFC visual trackers, which won the VOT Challenge and are deployed in consumer hardware. His work spans 3D geometry, self-supervised learning, causal inference, and neuro-symbolic systems. Key contributions include methods for egocentric video analysis, unsupervised reconstruction, and robot navigation. He leads the VGG's research on neural feature fields, hierarchical scene understanding, and real-time 3D perception. Recent publications emphasize 3D-aware segmentation, universal place recognition, and neuro-symbolic world modeling for robotics. His research often bridges theoretical guarantees with practical applications, such as medical imaging and autonomous systems. Dr. Henriques collaborates with industry and academia on AI ethics, friendly AI, and interpretable learning. His lab hosts DPhil students advancing creative AI applications, such as generative models for gameplay design and LLM evaluations in real-world editorial workflows.
Dr. Tim Oates is a Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County . His research spans machine learning, artificial intelligence, and brain-machine interfaces, with a focus on weakly supervised methods, human-in-the-loop reinforcement learning, and grounded policy development for robotics. Ph.D., Computer Science, University of Massachusetts, Amherst, 2000 M.S., Computer Science, University of Massachusetts, Amherst, 1997 B.S., Computer Science and Electrical Engineering, 1989 Current research threads include: Developing non-invasive brain injury severity assessment via medical time series Modeling human brain development through computational frameworks Designing algorithms for autonomous robotic learning Recent publications highlight AI security mechanisms (backdoor detection via tensor decomposition, matrix factorization) Medical applications (3D artery reconstruction, skin lesion diagnosis, EEG denoising) Neuro-symbolic integration (holographic representations, language-guided reinforcement learning) Mathematical reasoning (schema-based problem solving, subitizing algorithms) Contact: oates@cs.umbc.edu | Office: 336 Information Technology and Engineering (ITE) Building
Brian Kulis is an Associate Professor at Boston University with appointments in the Department of Electrical and Computer Engineering, Computer Science, Systems Engineering, and the Faculty of Computing and Data Sciences. He holds the Peter J. Levine Career Development Professorship and has previously been an Amazon Scholar at Alexa AI (2019–2023) and an assistant professor at Ohio State University (2012–2015). His research focuses on machine learning, including large-scale optimization, metric learning, deep learning, Bayesian methods, and applications in audio and visual data analysis. He earned his PhD in Computer Science from the University of Texas at Austin (2008) and a BS in Computer Science and Mathematics from Cornell University. Key awards include the NSF CAREER Award (2015), CVPR Best Student Paper (2008), and ICML Best Student Paper (2007, 2005). His work spans publications in top venues like CVPR, NeurIPS, ICML, and ECCV, emphasizing scalable algorithms and domain adaptation. Current research explores metric learning, adversarial audio augmentation, and HPC anomaly detection. He advises multiple PhD students and collaborates on grants such as the NSF Traineeship for Sustainable Energy Solutions (2024). He teaches advanced courses in machine learning, deep learning, and data structures. His lab focuses on foundational and applied ML challenges, with affiliations in the Intelligent, Autonomous & Secure Systems group. Recent service includes senior area chair roles at AAAI, NeurIPS, and ICML.
Lee M. Miller is a Professor and Vice Chair of Academic Affairs in the Department of Neurobiology, Physiology and Behavior at the University of California, Davis, affiliated with the Center for Mind and Brain. His research focuses on neuroengineering, computational neuroscience, and neural mechanisms underlying attention, speech processing, and multisensory integration. Research interests include the development of neural prosthetics, decoding of neuromuscular signals for prosthetic control, and understanding how auditory and visual systems interact during speech perception and attentional processes. His work bridges clinical applications (e.g., cochlear implants) with fundamental neuroscience, leveraging tools like electrophysiological recordings, EEG/MEG, and advanced signal processing techniques. Recent publications highlight innovations in electromyographic speech neuroprosthetics, the topology of neuromuscular signals, and the neural basis of speech-in-noise processing. Miller’s studies emphasize translational potential, such as improving speech synthesis from brain signals and designing haptic feedback systems for motor coordination. His contributions have advanced understanding of neural mechanisms in sensory integration, auditory attention, and the impact of cognitive factors on perception. Miller maintains a lab dedicated to these interdisciplinary efforts, with a focus on both basic science and clinical applications.
Nick Koudas is a Professor in the Department of Computer Science at the University of Toronto. His research focuses on large-scale data management, integrating machine learning into data systems, and developing efficient query processing techniques for unstructured and streaming data. He holds a PhD from the University of Toronto, an MSc from the University of Maryland at College Park, and a Bachelor's from the University of Patras in Greece. Research interests include data systems, big data analysis, video query processing, and natural language interfaces for databases. He leads projects like ReDD (Relational Deep Dive), SVQ (Streaming Video Queries), and Reliable Text-to-SQL, aiming to bridge human-readable queries with database execution. His work emphasizes scalability, intelligence, and real-world applicability. Recipient of the University of Toronto's Inventor of the Year Award (2011), he translates research into startups like Sysomos, Aislelabs, and Workorb. His contributions span over 200 publications in top venues such as SIGMOD, VLDB, and ICDE. Courses taught include advanced data systems, database design, and system internals. Current projects explore schema extraction from unstructured data, video query optimization, and cost-effective machine learning pipelines. Collaborations with industry and academic partners drive innovations in both theory and practical applications.
Professor Garg Vikas holds the position of Assistant Professor in the Department of Computer Science at the School of Science. His research spans quantum computing, artificial intelligence, and machine learning with applications in computational biology, healthcare, and drug design. He leads the HEALED/Garg project focused on human-steered machine learning for drug discovery and collaborates with institutions like MIT and industry partners. He co-founded YaiYai Oy, providing AI/ML solutions to global sectors. Education: PhD from MIT CSAIL under Tommi Jaakkola, with postdoctoral and industry experience at Amazon, Microsoft, and IBM. His work aligns with UN SDGs, particularly in healthcare and sustainable energy. Research interests include graph neural networks, generative models, and quantum AI. Recent projects involve climate modeling via physics-informed neural ODEs and optimizing quantum circuits using graph autoencoders. Key collaborations include MIT’s MLPDS Consortium and the Finnish Center for Artificial Intelligence. He supervises doctoral researchers like Yogesh Verma and postdocs such as Kogkalidis.
Sanmi Koyejo is an Assistant Professor of Computer Science at Stanford University and holds an adjunct position as Associate Professor at the University of Illinois at Urbana-Champaign. He leads the Stanford Trustworthy AI Research (STAIR) group, focusing on fairness, robustness, and healthcare applications in machine learning. His work bridges theoretical foundations with practical systems, emphasizing ethical AI and clinical informatics. Affiliations: SAIL, HAI, CRFM, AIMI, AI Safety, and the Machine Learning Group. Research Interests: His expertise spans trustworthy AI, federated learning, and neuroimaging. He actively addresses challenges in algorithmic fairness, particularly in healthcare, where he collaborates with institutions like OSF Healthcare on projects like federated learning for clinical data. Key Contributions: Co-developed frameworks for unlearning in large language models, evaluated AI systems' societal impacts, and advanced benchmarks for medical applications. His work has been featured in venues like NeurIPS, ICML, and AAAI. Awards: NSF CAREER Award, Alfred P. Sloan Fellowship, and Terman Faculty Fellowship. Grants & Teams: Leads NSF-funded projects on domain adaptation and fairness in breast cancer risk scoring. Collaborates with interdisciplinary teams on NIH's MIDRC and NSF's AIFARMS initiative for agricultural sustainability. Labs/Teams: STAIR lab drives interdisciplinary research in ethical AI, with emphasis on real-world deployment and policy implications.
Roozbeh Razavi-Far is an Assistant Professor at the Faculty of Computer Science and the Canadian Institute for Cybersecurity at the University of New Brunswick. His research focuses on machine learning, big data analytics, and cybersecurity of cyber-physical systems and IoT devices. He has authored/co-authored over 150 publications and is listed by Stanford as among the top 2% of most cited researchers (2022). His work spans federated learning, transfer learning, quantum machine learning, and dependable AI systems. He serves as an Associate Editor for Neurocomputing, Machine Learning with Applications, and IEEE Transactions on Industrial Cyber-Physical Systems, among others. As an IEEE Senior Member, he chairs IEEE Computational Intelligence and Systems, Man, and Cybernetics Societies. Previously, he directed the Learning System and Cybernetics Group at the University of Windsor (2016–2022). His research interests emphasize security in non-stationary environments, adversarial machine learning defenses, and real-time analytics for smart grids. Awards include NSERC-DG, NSERC-ECR, and USRG grants. He has mentored students who received NSERC Alexander G. Bell, MITACS, and Ontario Graduate Scholarships. His recent publications highlight advancements in privacy-preserving split learning, blockchain-based federated learning security, and graph-based malware detection. He also explores quantum computing applications in AI and cybersecurity frameworks for cyber-physical systems.
Radu Timofte is an academic researcher specializing in computer vision and image processing. He completed his PhD in 2013 at Katholieke Universiteit Leuven, Belgium, with a thesis on sparse and collaborative representations for computer vision. His work focuses on advancing techniques such as image super-resolution, denoising, object detection, and deep learning-based image restoration. He has collaborated extensively with institutions like ETH Zurich and co-authored seminal papers in top-tier journals and conferences. His research bridges theoretical advancements with practical applications in areas like medical imaging, aerial scene analysis, and real-time visual tracking. Timofte’s contributions include developing efficient deep learning architectures for tasks like lightweight object detection (e.g., CH-YOLO-Lite), diffusion models for image-to-image translation (DiffI2I), and calibration-free raw image denoising. He has also contributed to the development of video restoration transformers (VRT) and frameworks for unsupervised real-time video enhancement. His work often emphasizes practicality and efficiency, addressing challenges such as small object detection in aerial imagery and underwater image super-resolution. Timofte’s collaborations span academia and industry, with notable co-authors including Luc Van Gool (ETH Zurich) and Kai Zhang (Nanjing University of Science and Technology). His research has been published in venues like IEEE Transactions on Pattern Analysis and Machine Intelligence, CVPR, ECCV, and the International Journal of Computer Vision.
Andrea Santilli is a Research Scientist at Nous Research and holds a PhD in Computer Science from GLADIA at Sapienza University of Rome. His research focuses on large language models (LLMs), robustness, reliability, and multimodal learning. He previously worked at Apple MLR, Hugging Face’s BigScience, and Pi School. He earned his MSc and BSc in Computer Science from Tor Vergata University and Sapienza. Education: PhD in Computer Science, Sapienza University of Rome (2024) MSc in Computer Science, University of Roma Tor Vergata (2020) BSc in Computer Science, University of Roma Tor Vergata (2018) Research Interests: Santilli’s work spans LLM robustness , mechanistic interpretability , multimodal neural databases , and instruction-tuning . He introduced Parallel Jacobi Decoding and contributed to projects like BLOOM, Camoscio, and Fauno. His research bridges syntax-aware NLP, privacy-preserving LLMs, and cross-modal alignment. Publications: His work includes advancements in 3D-text latent space alignment (CVPR 2025), evolutionary merging (ICML 2025), and efficient decoding (ACL 2023). Over 15+ peer-reviewed papers span venues like ACL, CVPR, and ICLR. Awards: Received the Emanuele Pianta Award for his MSc thesis on continual language learning with syntax-based episodic memory. Grants & Projects: Winner of ‘Machine Learning Algorithms for Translation’ grant (2022), developing Parallel Decoding Co-PI for ‘Multimodal AI for 3D Analysis’ (2021) with Ecole Polytechnique Labs & Teams: Active in GLADIA (Sapienza), Apple MLR, and Hugging Face’s BigScience initiative. Core contributor to open-source projects like PromptSource and BLOOM.
Prof. Anna Franklin is a Professor of Visual Perception and Cognition at the University of Sussex's School of Psychology. She leads the Sussex Colour Group and Sussex Baby Lab , focusing on human color perception, development, and neural representation. Her research combines cognitive psychology, developmental science, and neuroscience to explore how color perception develops, influences aesthetics, and relates to conditions like autism. Key projects include ERC-funded initiatives studying environmental impact on color perception and developing the ColourSpot diagnostic app for childhood color vision deficiency. She also serves as Deputy Director of Research and Knowledge Exchange for the School of Psychology. Education includes a BA from the University of Nottingham and a PhD from the University of Surrey, followed by a postdoctoral fellowship. Her work spans 107+ publications, emphasizing interdisciplinary methods like hyperspectral imaging and fMRI. Collaborations include industry partnerships with ETTA LOVES and COSATTO to apply infant visual preference research in product design. Research grants include European Research Council awards (Starting Grant 2012-2017, Consolidator Grant 2018-2025) and a Proof of Concept grant for ColourSpot . Her labs investigate cross-cultural color categorization, infant aesthetics, and neural correlates of color processing. Future work focuses on calibrating visual systems to environmental statistics and improving early childhood color vision screening.
Haohan Wang serves as Assistant Professor at the School of Information Sciences, University of Illinois Urbana-Champaign, with additional appointments as Affiliate at the Carl R. Woese Institute for Genomic Biology and Assistant Professor at the National Center for Supercomputing Applications (NCSA). His interdisciplinary work bridges machine learning, genomics, and AI security, focusing on trustworthy systems for biomedical applications and foundational AI research. Wang's research centers on robust and secure artificial intelligence, with emphasis on large language model vulnerabilities (jailbreaking, safety evaluation), federated learning personalization, and genomic data analysis. He develops techniques for privacy-preserving dataset distillation, confounding factor correction in genome-wide studies, and multi-agent frameworks for scientific discovery. His fingerprint highlights expertise in Machine Learning (94%), Linear Mixed Models (87%), and Confounding Factor Correction (41%), reflecting his focus on methodological rigor in complex data environments. Analysis of his 2025 publications reveals dominant trends in AI security (jailbreak evaluation frameworks like GuardVal, adversarial attacks such as InfoFlood), biomedical AI (transcriptomic analysis, wearable data privacy), and foundational methods (federated learning optimization, synthetic data generation). These works consistently address real-world challenges in model trustworthiness while advancing computational techniques for genomics and healthcare. Through NCSA's high-performance computing resources and the Institute for Genomic Biology's collaborative ecosystem, Wang integrates supercomputing capabilities with biological research to tackle data-intensive problems in disease modeling and AI safety testing, as evidenced by media coverage of his team's AI security testing methods.
Yintong Huo is a tenure-track Assistant Professor in the Department of Computer Science at Singapore Management University (SMU), School of Computing and Information Systems. He joined SMU in early 2024 after completing his PhD at The Chinese University of Hong Kong (CUHK) under Prof. Michael R. Lyu. His academic journey includes a Bachelor's degree from the University of Electronic Science and Technology of China. Education: PhD in Computer Science and Engineering, The Chinese University of Hong Kong (2024) Bachelor's degree, University of Electronic Science and Technology of China Huo's research focuses on intelligent software engineering , particularly empowering AI models (especially LLMs) for software development, testing, and operations. His work spans AI4SE, LLM4SE, AIOps, code intelligence, and multimodal software engineering . Two flagship projects define his current research: LogPAI - an open-source AI platform for automated log analysis adopted by leading tech companies, and WebPAI - a multimodal intelligence project for automatic webpage development. His research addresses critical challenges in software reliability, log analysis, and UI code generation through innovative applications of AI. His recent publications reveal a strong trend toward multimodal approaches in software engineering , combining vision and language models for UI code generation, and increasingly sophisticated applications of LLMs for log analysis and software reliability. Huo's work demonstrates exceptional impact, with multiple papers accepted at top-tier venues including ASE, ICSE, and FSE with high acceptance rates (e.g., 9.5% for ASE'25). Scientific Awards: ICSE Distinguished Reviewer Award (2025) ISSRE Distinguished Reviewer Award (2024) IEEE Open Software Services Award (2022, for LogPAI with 3k+ GitHub stars and 70k+ downloads) ACM SIGSOFT CAPS Travel Grants (ASE'23, ICSE'24, FSE'24) Nomination for Best Teaching Assistant Award (2022) National Scholarship (2019) Huo actively mentors students at multiple levels, currently supervising PhD students Shi Ying Chang and Dan Huang (co-supervised with Prof. David Lo), research engineer Minxing Wang, and visiting students including Shiwen Shan. His undergraduate mentee Truong Hai Dang will intern at Apple Inc. He maintains strong industry connections, with his LogPAI project adopted by world-leading tech companies. Huo serves on program committees for major conferences including ASE'25, ICSE'26, and FSE'26, and is recruiting fully-funded PhD students and research assistants for projects in AI4SE and multimodal software engineering. Huo leads the LogPAI and WebPAI research initiatives, which have evolved into substantial open-source projects with significant industry adoption. His team focuses on practical applications of AI in software engineering, with particular emphasis on reliability and usability in real-world systems. The research environment benefits from SMU's strong position in software engineering research, where the university ranks No. 2 globally in Software Engineering according to CSRankings (2020-2025).
Professor John D. Kubiatowicz is a faculty member at the University of California at Berkeley in the Department of Electrical Engineering and Computer Sciences since 1998. He holds a PhD in Electrical Engineering and Computer Science (minor in Physics) from MIT (1998), an M.S. in EECS (1993), and a double B.S. in Electrical Engineering and Physics (1987) from MIT. His research interests span Quantum Computing Architectures Distributed Systems and Storage Network Security and Peer-to-Peer Protocols Introspective and Manycore Operating Systems Edge and Fog Computing Hardware-Assisted Security He has pioneered systems like OceanStore , a global-scale distributed file system, and Tessellation , a manycore OS with continuous adaptation. The scientific awards he has received include Presidential Early Career Award (PECASE, 2000) Scientific American 50 (2002) Diane S. McEntyre Teaching Award (2003) IEEE ICRA Best Paper (2025) George M. Sprowls Award for MIT PhD thesis (1998) Okawa Research Grant (1998) Best Paper at International Conference on Supercomputing (1993) His recent publications focus on Quantum Circuit Design and Optimization Edge/Fog Computing Architectures Secure Runtime Systems Distributed Garbage Collection Manycore OS Innovations Hardware-Assisted Security Mechanisms He leads the Quantum Architecture Research Center and co-founded the SWARM Lab at Berkeley, advancing a vision of self-adapting, secure systems from the chip level to internet scale.
Dr. Ana Lucic is an Assistant Professor at the University of Amsterdam , holding a joint appointment between the Institute for Logic, Language, and Computation (ILLC) and the Informatics Institute . Her research focuses on interpretable machine learning for applications in science and society, with emphasis on Earth system modeling and AI for environmental forecasting . PhD in Explainable Machine Learning (University of Amsterdam, 2022) MSc/BSc in Mathematics (McMaster University, Canada) Former researcher at Microsoft Research AI for Science and Partnership on AI Her recent work includes Aurora , a foundation model for Earth system forecasting published in Nature , and Clifford-Steerable CNNs at ICML 2024. Ana actively mentors PhD students and leads projects in mechanistic interpretability and geospatial machine learning . Scientific awards include top placements in ML competitions. She contributes to open science through reproducibility initiatives and collaborates with AI for climate consortia.
