Leah Findlater is an Associate Professor in the Department of Human-Centered Design & Engineering at the University of Washington (College of Engineering). She also holds adjunct and affiliate appointments in Computer Science & Engineering and Disability Studies, respectively, and serves as Associate Director of UW CREATE (Center for Research and Education on Accessible Technology and Experiences). Since 2021, she has led a research team in AI and accessibility at Apple. Her research focuses on creating adaptive technologies that accommodate individual needs, particularly for people with disabilities. Key areas include touchscreen personalization, machine learning interfaces, and sound recognition systems for deaf and hard-of-hearing users. Funding sources include NSF, DoD, and major tech companies. Recent work explores AI-driven tactile graphics, visual privacy management for blind users, and sound recognition systems. Her research combines lab studies, field deployments, and qualitative methods. NSF CAREER Award (2014) She advises through the Inclusive Design Lab, emphasizing mixed-methods research and collaborations with disability communities.
Pouya Bashivan is an Assistant Professor in the Department of Physiology at McGill University's Faculty of Medicine. His research focuses on developing computational models to explain and regulate neural responses during visual tasks requiring memory, combining machine learning, neuroscience, and cognitive science. Education : Ph.D. in Computer Engineering (2016), Postdocs in Machine Learning (2020) and Computational Neuroscience (2016-2020) His lab investigates: Topographical neural networks for visual cortex simulation Massively-multitask models for prefrontal cortex Saccade-driven visual exploration models Predictive hippocampus models for episodic memory Recent publications explore adversarial robustness, memory-augmented networks, and brain-state decoding. Current projects emphasize causal models, brain-AI alignment, and translating computational neuroscience into therapeutic applications. The lab is located in the McIntyre Medical Sciences Building, Room 1117, Montreal, Quebec.
Kede Ma is an Associate Professor in the Department of Computer Science at City University of Hong Kong (CityUHK). He received his B.E. from the University of Science and Technology of China (USTC) in 2012, and MASc and Ph.D. degrees from the University of Waterloo in 2014 and 2017, respectively. From 2018 to 2019, he was a Research Associate with the Howard Hughes Medical Institute and New York University. Prof. Ma has been named to the Highly Cited Researchers list by Clarivate Analytics in 2024 and currently serves on the editorial boards of IEEE Transactions on Image Processing, IEEE Transactions on Information Forensics and Security, and IEEE Signal Processing Letters. Prof. Ma leads the Multimedia Analytics (MA) Laboratory, an interdisciplinary research group focused on computational vision, computational modeling of human visual perception, perceptual multimedia signal processing, quality assessment, and multimedia forensics. His research spans computational photography, high dynamic range imaging and rendering, omnidirectional video analysis, camera processing pipeline design, and artificial intelligence safety in multimedia systems. His work integrates machine learning techniques including reinforcement learning, generative modeling, self-supervised learning, and continual learning for multimedia signal processing applications. His recent publications demonstrate a strong focus on image quality assessment, deep learning for multimedia processing, and multimedia forensics. His work bridges theoretical computer vision principles with practical applications in multimedia systems. The research trends show increasing integration of foundation models with specialized multimedia processing tasks, particularly in quality assessment and security applications. Highly Cited Researchers list by Clarivate Analytics (2024) Best Paper Award at IEEE International Conference on Virtual Reality and Visualization (2021) Best Paper Runner-Up at International Joint Conference on Artificial Intelligence Workshop (2021) Top 10% Award at IEEE International Conference on Image Processing (2015) Finalist for the Governor General's Gold Medal, University of Waterloo (2017) Spotlight presentation at NeurIPS (2022) Highlight paper at ICCV (2025) Oral presentation at ICLR (2025) Prof. Ma advises numerous PhD students and postdoctoral fellows in the MA Laboratory. His research is supported by various grants enabling work in multimedia analytics, image processing, and computer vision. The laboratory maintains active collaborations with researchers at institutions including SUSTech, ZJU, and HIT. Current projects focus on advancing image quality assessment methodologies, developing more robust deep learning techniques for multimedia forensics, and exploring new approaches to HDR imaging and omnidirectional video processing. The Multimedia Analytics Laboratory maintains a strong focus on both theoretical foundations and practical applications of multimedia processing. Current research directions include integrating large language models with image quality assessment, developing more robust deepfake detection methods, and advancing techniques for continual learning in multimedia applications. The lab emphasizes rigorous evaluation methodologies and maintains multiple datasets for multimedia quality assessment research.
Anthony TUNG Kum Hoe is a Professor in the Department of Computer Science at the National University of Singapore (NUS), where he has established himself as a leading researcher in database systems and data mining. He is also affiliated with the NUS Graduate School for Integrative Sciences and Engineering and serves as a SINGA supervisor. His educational background includes a Ph.D. in Computer Science from Simon Fraser University (2001), an M.Sc. in Information Systems & Computer Science from NUS (1998), and a B.Sc. with 2nd Class Upper Honours in Information Systems & Computer Science from NUS (1997). Professor Tung's research spans several interconnected areas within database systems and data mining. His primary focus is on developing efficient methods for indexing and searching complex data structures including time series, trajectories, trees, graphs, and high-dimensional objects. He has pioneered work in visual query processing, keyword search, and ranking systems. His GENIE (Generic Inverted Index) and LAMP (semi-Lazy Mining Paradigm) projects represent significant contributions to big data analytics, particularly in handling the 'variety' aspect of big data by providing unified frameworks for processing diverse data structures while preserving semantic meaning. His research bridges theoretical database concepts with practical applications in visual data mining, collaborative analytics, and just-in-time model construction. His recent publications reveal a clear evolution from traditional database research toward more complex analytics on diverse data types. While maintaining his core expertise in database indexing and query processing, his work has expanded to incorporate machine learning techniques, particularly in areas like nearest neighbor search, anomaly detection, and predictive analytics. There's a noticeable trend toward interdisciplinary applications, with publications spanning computer vision, natural language processing, transportation systems, and social computing. His research group consistently publishes in top-tier venues including SIGMOD, VLDB, ICDE, and KDD, demonstrating both theoretical rigor and practical relevance. 2005 Best Paper Award for 'Indexing DNA Sequences Using q-grams' 2007 Invited panel speaker on 'Advice for a successful database researcher career in Asia' at SIGMOD 2010 Guest Lecturer for VLDB Database School 2012 VLDB 2012 Research PC Co-chairs 2015 10 Years Best Paper Award, DASFAA 2015 Invited to SIGMOD 2008 and SIGKDD 2008 Program Committees Professor Tung has supervised numerous PhD students and research associates throughout his career, including notable researchers like Zhang Zhenjie (recipient of the 2007 President Graduate Fellowship) and Wang Nan (published in SIGMOD'08). His research group has been consistently productive, with students publishing in top conferences including SIGMOD, ICDE, and VLDB. His professional service is extensive, having served as PC Chair for COMAD'06, Research PC Co-chair for VLDB 2012, and on program committees for virtually all major database and data mining conferences over the past two decades. His research has been supported by various grants that have enabled significant contributions to database technology. His GENIE and LAMP projects represent a cohesive research direction focused on developing systematic approaches to big data analytics. GENIE provides a unified platform for storage and retrieval of big data with various structures, while LAMP introduces a novel paradigm for predictive analytics that combines the strengths of lazy and eager learning approaches. These projects have evolved to incorporate GPU acceleration and parallel processing capabilities, reflecting his commitment to addressing real-world scalability challenges in data-intensive applications.
Farinaz Koushanfar is a Professor in the Department of Electrical and Computer Engineering at the Jacobs School of Engineering, University of California San Diego (UCSD) . She holds the Siavouche Nemat-Nasser Endowed Chair and serves as Founding Co-Director of the Center for Machine-Intelligence, Computing and Security . Her affiliations include NSF Trust-Hub (Co-PI) and NSF TILOS AI Institute . She also serves on the Editorial Board of The Proceedings of the IEEE . Research Focus: Prof. Koushanfar leads research in secure and efficient computing , including robust/safe AI , hardware/system security , AI-based optimization , and cryptographically secure privacy-preserving computing . Her work pioneered logic obfuscation/locking for chip security, automated co-design of AI systems , watermarking/tracing of deep learning models , and physical proofs of provenance . She explores co-design with cryptographic constructs for privacy preservation and manages nonlinearities in ciphertext domains. Article Trends: Recent publications show expertise in neural watermarking (deepfakes, media authentication), zero-knowledge proof frameworks , Trojan attack defenses in ML models, secure federated learning , and hardware acceleration of cryptographic protocols . Her work combines machine learning , cryptography , and physical design security across 2022-2025 publications. Scientific Awards: Fellow of ACM Fellow of IEEE Fellow of National Academy of Inventors (NAI) Fellow of Kavli Foundation of NAS Inducted to NAI 2024 Fellows Advising & Leadership: She has advised multiple PhD students who became faculty at top universities (e.g., Stanford, Purdue). She chairs conferences like ACM WiSec 2024 and co-led the NSF SaTC decadal review. Her lab ( ACES Lab ) produces award-winning graduates like Bita Rouhani (DAC Under-40 Innovators) and Shehzeen Hussain (UCSD Best Dissertation Award).
Ernest Davis is a Professor at the Department of Computer Science , Courant Institute of Mathematical Sciences , New York University . His research focuses on representing commonsense knowledge in AI systems , with an emphasis on spatial and physical reasoning , and he collaborates with Gary Marcus on integrating AI and psychological models. He has authored over 50 scientific papers and three books, including Linear Algebra and Probability for Computer Science Applications (2012). His teaching includes courses on Artificial Intelligence and Fundamental Algorithms. Research Trends: His recent work examines benchmarks for commonsense reasoning , limitations of large language models (e.g., GPT-4, DALL-E 2), mathematical reasoning in AI, and the Winograd Schema Challenge . Professional Activities: He has served as an ACM reviewer, program committee member for 50+ conferences, and area editor for ACM Transactions on Computational Logic . He contributes book reviews to Computing Reviews , SIAM News , Artificial Intelligence journal, and others. Non-Technical Writing: Davis writes for general audiences on topics spanning computer science, mathematics, cognitive psychology, and literary themes, published in outlets like The New Yorker , Wired , and The Times Literary Supplement .
Dr. Ghazal Bargshady is a Lecturer at the University of Canberra , with expertise in Affective Computing , Artificial Intelligence , and Healthcare Technology . Her roles include teaching units such as Computer Vision, Data Analytics, and Soft Computing, as well as supervising PhD and Master by Research students in AI-driven projects for healthcare and road safety. Education: She earned her PhD in Artificial Intelligence and Computer Vision from the University of Southern Queensland in 2020. Research Interests: Dr. Bargshady specializes in Computer Vision Deep Learning Biosignal Processing Facial Expression Analysis Human Factors in AI Wearable Sensors Multimodal Data Fusion Brain–Computer Interfaces Her work addresses real-world challenges in pain assessment, depression recognition, and driver safety using cutting-edge AI models. Article Trends: Her recent publications focus on Transformer architectures , fNIRS signal analysis , multimodal pain detection , and depression severity estimation via facial video data. These studies highlight her contributions to AI in healthcare , transportation safety , and biomedical signal processing . Teaching Activities: Dr. Bargshady has lectured units including Programming for Data Science , Computer Vision , and Soft Computing , emphasizing practical AI applications.
John Breslin is a Personal Professor in Electronic Engineering at the College of Science and Engineering, University of Galway, serving as Director of the TechInnovate and AgInnovate programmes. Associated with two Taighde Éireann – Research Ireland Centres, he is a Principal Investigator at Insight Centre for Data Analytics (specializing in data analytics) and a Funded Investigator at VistaMilk (Agri-Technology), while also leading the EDIH Data2Sustain project. With an h-index of 50, over 12,000 citations, and 300+ peer-reviewed publications including seminal books on the Social Semantic Web, he ranks among Ireland's most influential researchers in digital technologies. Breslin's research fundamentally bridges Semantic Web technologies, AI-driven data analytics, and practical innovation. His co-creation of the SIOC framework—implemented across 65,000+ websites by entities like Yahoo and Boeing—demonstrates real-world impact in social data interoperability. Current work leverages blockchain and federated learning for sustainable Agri-Technology through VistaMilk, while his TechInnovate programmes translate academic research into commercial ventures across healthcare, smart manufacturing, and energy systems. Analysis of his 15 most recent publications reveals dominant themes in AI-enhanced security (35% of works), blockchain applications for sustainability (27%), and multimodal AI for healthcare (20%). His team pioneers privacy-preserving techniques for IoT and medical devices, neurosymbolic visual reasoning frameworks, and federated learning architectures addressing data heterogeneity—directly supporting his roles in national research infrastructures like Insight and VistaMilk. John has received several prestigious awards: IIA Net Visionary Award (twice) ITAG Outstanding Contribution to the ICT Sector Award Galway Chamber President’s Award Best Irish-Published Book Award (2020 for Old Ireland in Colour) Multiple Best Paper Awards He leads major research initiatives funded by Taighde Éireann – Research Ireland: Insight Centre for Data Analytics (as Principal Investigator) VistaMilk SFI Research Centre (as Funded Investigator) EDIH Data2Sustain (as Principal Investigator) His entrepreneurial programs TechInnovate and AgInnovate have mentored 200+ startups, securing €50M+ in follow-on funding. Breslin co-founded PorterShed (Galway City Innovation District) and serves on Scale Ireland's Steering Group, creating Ireland's most active regional innovation ecosystem outside Dublin. He maintains active industry partnerships with Vodafone, Boeing, and agricultural cooperatives through VistaMilk's testbed facilities.
Laura Quick is an Associate Professor of Hebrew Bible at the University of Oxford and a Tutorial Fellow of Theology and Religion at Worcester College. Her academic work bridges biblical studies with comparative ancient Near Eastern literary culture, focusing on the intersection of language, gender, and ritual in the Hebrew Bible. Research Focus: Beauty and aesthetics in the Hebrew Bible and ancient Near East; body symbolism in biblical and Dead Sea Scroll texts; philological gender analysis; ritual and liturgical studies Academic Roles: Editorial Board, Hebrew Studies; Steering Committee, Philology in Hebrew Studies (SBL); External Member, Sheffield Institute for Interdisciplinary Biblical Studies Recent publications examine topics ranging from cosmetic practices in Esther and Ruth to the mythological background of Isaiah 14. Her scholarship frequently engages with Deuteronomy, Dead Sea Scrolls, and Septuagint studies, employing comparative methods to analyze textual strategies and cultural exchange. She teaches Hebrew poetry, ritual, and liturgy, contributing to both undergraduate and postgraduate theological education at Oxford.
Benjamin Ricaud is an Associate Professor and Group Leader in Machine Learning at UiT The Arctic University of Norway's Department of Physics and Technology. His core affiliations include membership in the Machine Learning Group, Visual Intelligence center, and co-directorship of the Digital Technology Innovation Lab focused on Arctic-region tech startups. He also co-chairs the annual Northern Light Deep Learning conference. Ricaud's research spans: Fundamental ML : Graph signal processing, explainable AI, and generative models Applications : Microfossil classification, medical diagnostics (retinal aging), drug analysis, and climate data interpretation Emerging domains : Self-supervised learning and biological data analysis using Raman spectroscopy His recent publications (2020-2025) cluster in three domains: Graph ML methodologies (35%) Biomedical/biological applications (40%) Geoscience/climate informatics (25%) with consistent focus on interpretability and real-world data challenges. Teaching includes Image Processing (FYS-2010), Pattern Recognition (FYS-3012), and Machine Learning (FYS-2021). He leads outreach initiatives developing AI exhibits for Tromsø Science Centre.
Johannes Brandstetter is an Associate Professor at the Institute for Machine Learning at Johannes Kepler University Linz (JKU) where he leads the "AI for data-driven simulations" research group. He is also Co-founder and Chief Scientist at Emmi AI, bridging academic research with industrial applications in AI-driven physics simulation. Brandstetter earned his PhD after working at CERN's CMS experiment on Higgs boson physics. In 2018, he transitioned to machine learning, joining Sepp Hochreiter's research group in Linz. From 2021-2023, he worked at the Amsterdam Machine Learning Lab under Max Welling and Microsoft Research, developing expertise in Geometric Deep Learning and neural surrogates for partial differential equations. He returned to JKU in October 2023 to establish his own research group. His research spans Machine Learning, Deep Learning, and Physics-Informed Machine Learning with focus areas including Neural PDE solvers, Computational Fluid Dynamics, and Climate Modeling. Brandstetter believes AI is poised to revolutionize industrial-scale simulations, potentially saving thousands of compute hours across engineering domains. His work integrates computer vision, numerical simulation, and engineering components to advance data-driven approaches. Recent publications reveal a strong trend toward foundation models for scientific applications, particularly in atmospheric modeling (Aurora), geometric deep learning, and neural surrogates for complex physical systems. His interdisciplinary work spans computer vision, climate science, computational physics, and engineering, demonstrating the versatility of his research approach. Principal Investigator for "AlKa-DL: Alpine karst spring discharge prediction" (FWF-funded, 2024-2027) Principal Investigator for Cluster of Excellence "Bilateral Artificial Intelligence" (FWF-funded, 2024-2029) Co-PI for "Fast, efficient and flexible CFD simulation through generative AI" (FFG-funded, 2025-2026) As an educator and researcher, Brandstetter actively engages with the scientific community through invited talks at major conferences including presentations on "Closing the Gap Between Scientific Foundation Models and Real-World Applications" (March 2025) and "Scientific Machine Learning for Science and Engineering" (February 2025).
Peng Li is a Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara. His research focuses on integrated circuits, brain-inspired computing, electronic design automation, and hardware machine learning systems. He holds Fellow status in the Institute of Electrical and Electronics Engineers (IEEE). His work emphasizes neuromorphic engineering, spiking neural networks, and the intersection of machine learning with analog circuit design. Education includes a PhD in Electrical and Computer Engineering from Carnegie Mellon University, an MS in Systems Engineering from Xi'an Jiaotang University, and a BS in Information Science and Engineering from the same institution. His research has been recognized with prestigious awards including the ICCAD Ten-Year Retrospective Most Influential Paper Award and multiple Design Automation Conference Best Paper Awards. Key research trends in his articles include advancements in spiking neural networks (SNNs), hardware accelerators for neuromorphic computing, Bayesian optimization for analog circuit design, and robustness in machine learning systems. He explores topics like adversarial robustness, energy-efficient architectures, and data-efficient prediction techniques. His work bridges theoretical machine learning models with practical hardware implementations, particularly in 3D integration and systolic array acceleration. Notable contributions include pioneering hybrid approaches combining formal verification with machine learning for analog circuits (HFMV framework), and innovations in neuromorphic processors such as the 3D Liquid State Machine architecture. His research also addresses challenges in semiconductor manufacturing, including wafer map pattern recognition and failure detection through semi-supervised learning and contrastive methods. Awards highlight his impactful contributions to both design automation and neural computing. His grants and collaborations likely span industry partnerships in semiconductor technology and neuromorphic computing. He leads a lab focused on next-generation hardware-software co-design for intelligent systems, emphasizing energy efficiency and scalability.
Angelica Lim is an Assistant Professor of Professional Practice and Rajan Family Scholar in the School of Computing Science at Simon Fraser University. Her research focuses on Human Robot Interaction, Affective Computing, and Multimodal Perception with applications in healthcare and developmental robotics. She holds a PhD in Informatics from Kyoto University (2014), an M.Sc. from Kyoto University (2012), and a B.Sc. in Computing Science from SFU (2008). Her work bridges robotics and human-centered AI through projects like the ROSIE Lab, exploring emotion-aware systems, socially assistive robots, and VR programs for aging populations. Key contributions include benchmarking emotional speech recognition (BERSting), developing embodied emotion models for robots, and co-designing healthcare technologies with patient partners. Recent publications emphasize ethical AI, multimodal perception systems, and human-robot collaboration in dynamic environments. Teaching includes courses on software engineering, artificial intelligence, and introductory computer science. Her research has been applied in dementia care through VR programs, robotic companionship for older adults, and emotion-aware human-robot communication systems. Current initiatives focus on inclusive HRI design and sim2real methodologies for underrepresented data in affective computing.
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
