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Jason GaigerView profile

Professor

• Aesthetics
• Art Theory
• Theories of depiction and visual meaning
• +2 more

Jason Gaiger is Professor of Aesthetics and Art Theory at the University of Oxford and a Fellow of St Edmund Hall. He serves as Head of Art History and Theory at the Ruskin School of Art and acts as the Undergraduate Admissions Co-Ordinator. Previously, from 2010 to 2014, he was Head of the Ruskin School of Art and currently serves as Director of Graduate Studies. Professor Gaiger received his education in art history and philosophy at the universities of St Andrews, Essex and Heidelberg. Before joining Oxford in 2010, he was Senior Lecturer and Director of Research in the Art History Department at the Open University. Jason Gaiger's research spans aesthetics and art theory from the seventeenth century through to the present day, with additional focus on theories of depiction and visual meaning, and contemporary art practice and theory. His scholarly work is informed by both the historical resources of the German aesthetic tradition and debates in contemporary analytic philosophy. He has published extensively on topics including painting theory, sculpture, rhythm in visual art, visual perception, and the historical development of art theory. His most significant contribution is Philosophy of Painting: Ancient, Modern, Contemporary (2022), which provides a historically informed analysis of painting's contemporary relevance. Professor Gaiger's publication record demonstrates a consistent focus on the philosophical dimensions of visual art, particularly examining the temporal aspects of viewing, the relationship between materiality and meaning, and the historical continuity of aesthetic concepts. His work bridges historical analysis with contemporary philosophical inquiry, often drawing connections between eighteenth and nineteenth-century aesthetic theories and current debates in visual culture. The recurring themes across his publications include the nature of pictorial representation, the cognitive processes involved in art viewing, and the philosophical foundations of art historical interpretation. Professor Gaiger has supervised numerous graduate students, with recent theses under his direction exploring topics such as The Epistemic Value of Contemporary Art , Modes of Presence in the Contemporary Sculptural Encounter , and Affective Vision: Urban Landscape Photographs and their Paratexts . His mentorship reflects his broad expertise across historical and contemporary art theory.

• Human Factors Engineering
• Neuroergonomics
• Robotic Surgery
• +3 more

Denny Yu is an Associate Professor at the Edwardson School of Industrial Engineering, Purdue University. His work bridges human factors, neuroergonomics, and healthcare safety through advanced sensor systems and AI. Primary Affiliation : Edwardson School of Industrial Engineering, Purdue University Research Themes : Surgical ergonomics, autonomous vehicle human factors, cognitive workload assessment, multimodal physiological sensing Dr. Yu's research focuses on neuroergonomics and human-robot interaction , particularly in surgical and transportation contexts. His team develops sensor-based systems for workload monitoring, including: EEG-eye tracking fusion for situation awareness Wearable exoskeletons for surgical posture support Computer vision tools for lifting task risk analysis Smart infusion pump usability frameworks AI-driven surgical coaching systems Recent publications emphasize deep learning applications in soft tissue deformation estimation and real-time adaptive systems for robotic surgery augmentation. His work spans both occupational health (veterinary surgeons, airport workers) and medical device innovation domains.

Jan SalmenView profile

Researcher

• Machine Learning
• Computer Vision
• Neural Networks
• +3 more

Dr. Jan Salmen is a researcher at Ruhr University Bochum's Faculty of Computer Science, affiliated with the Institute of Neuroinformatics (INI). His work focuses on real-time systems, computer vision, and machine learning. Doctoral thesis: Efficient video-based driver assistance systems Salmen's research spans autonomous driving, traffic sign recognition, stereo vision, and sports analytics. He has contributed to benchmarks in traffic sign detection and soccer analysis. Publications highlight his expertise in image processing, pattern recognition, and sensor fusion for autonomous systems. Key trends include optimization of machine learning algorithms for real-time applications. He collaborates with interdisciplinary teams at INI, which integrates experimental psychology, neurophysiology, and robotics into artificial cognitive systems research.

• Image Registration
• Image Segmentation
• Image Classification
• +6 more

Sune Darkner is a Professor at the Department of Computer Science (DIKU) at the University of Copenhagen, specializing in the Image Analysis, Computational Modelling, and Geometry research section. His work focuses on medical image processing with particular emphasis on neuro-imaging data including MRI and PET scans. His primary research interests include Image Registration, Segmentation and Classification of Medical Image Data , with a specific focus on estimation of image similarity as his main research interest. Darkner strongly believes that the implementation of image processing algorithms should be thoroughly tested and reflect the theoretical properties as accurately as possible. His work primarily centers on neuro-imaging data such as MRI and PET. His recent publications (2024-2025) reveal a strong focus on medical image analysis, with particular emphasis on tumor volume delineation, deformable image registration with physics constraints, and applications of deep learning in medical imaging. His work spans both theoretical foundations of image processing and practical clinical applications. Darkner previously held a Post Doc position at the Technical University of Denmark from February 2009 to January 2010, demonstrating his longstanding engagement with image analysis research in the Danish academic community.

• Software Correctness
• Cybersecurity
• Usability Engineering
• +5 more

James Davis is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University. His research focuses on engineering robust computing systems through socio-technical approaches, emphasizing software correctness, security, and usability. He applies empirical methodologies to evaluate the practical impact of technical solutions. Research interests include software supply chain security, deep learning reproducibility, regular expression optimization, IoT cybersecurity, and the socio-technical challenges in system design. His work bridges theoretical foundations with real-world applications, addressing issues like regex denial-of-service (ReDoS), model reuse in AI, and developer practices for safety-critical systems. Recent publications span topics such as actor reputation metrics in software supply chains, AI safety for downstream developers, and edge-computing optimizations for vision transformers. His interdisciplinary approach integrates empirical studies, formal verification, and human-centered design principles. No scientific awards are explicitly mentioned in the provided materials. His advising record is currently unspecified, though his research group likely engages in collaborative projects with industry and academia. He contributes to initiatives like the Sigstore ecosystem and open-source security tooling, reflecting his commitment to practical impact.

Eduardo M. García-MolinaView profile

Assistant Professor

• Ancient History
• Seleukid Empire
• Imperial Bureaucracy
• +9 more

Eduardo M. García-Molina serves as Assistant Professor of Classics in the Department of Classics at the University of Illinois Urbana-Champaign's School of Literatures, Cultures & Linguistics. His research uniquely spans Seleukid imperial history and video game reception studies, examining how ancient states and cultures manifest in digital media like Total War and World of Warcraft . His academic journey includes: B.A. from University of South Florida M.A. from Florida State University Ph.D. from University of Chicago García-Molina's scholarship investigates imperial bureaucracy, archival practices, and political thought in the Seleukid Empire while pioneering analyses of antiquity's representation in video games. His dual focus reveals how documentary media shaped ancient governance and how modern games reinterpret historical systems through mechanics and epigraphy. This interdisciplinary approach bridges classical philology with digital humanities, emphasizing material culture's role in both ancient administration and contemporary game design. His publication pattern demonstrates growing influence across traditional classical journals and emerging game studies venues. The consistent engagement with Seleukid governance structures alongside video game analysis reflects a cohesive research vision exploring power dynamics across temporal contexts. Notable recognitions include: Hurst Summer Institute Fellowship in Legal History (2025) Lawrence Friedman/Stanford Law School Fellowship (2025) Humanities Research Institute Postdoctoral Fellowship (2024-2025) Franke Institute Doctoral Fellowship (2023-2024) Mellon Foundation Dissertation Award (2023-2024) As Faculty Affiliate in Game Studies and Design, García-Molina develops innovative courses like The Ancient World in Video Games while mentoring students through his monograph project on Seleukid information technologies. His current work examines how sealing practices and archival storage shaped subjects' experience of empire, with future research likely expanding into digital preservation of ancient knowledge systems.

Lee MillerView profile

Professor

• Motor Control
• Neural Networks
• Brain-Machine Interfaces
• +3 more

Lee Miller is a Professor of Physiology, Physical Medicine & Rehabilitation, and Biomedical Engineering at the University of Chicago. His research focuses on understanding how the brain encodes movement commands through neural signals, with applications in developing brain-machine interfaces (BMIs) to restore motor function in paralyzed patients. His work integrates neuroscience, engineering, and computational methods to study neural networks in motor systems. Key research areas include decoding cortical signals to predict muscle activity, developing closed-loop BMIs, and investigating functional connectivity in neural circuits. Miller collaborates extensively with the Biomedical Engineering Department and the Interdepartmental Neuroscience Program (NUIN). His lab combines experimental approaches (e.g., chronic neural recordings) with computational tools to study neural dynamics and develop therapeutic technologies. Recent work emphasizes restoring hand function via cortically controlled functional electrical stimulation (FES), translating neural signals into muscle activation. His publications span neural decoding algorithms, sensory feedback systems, and the neurobiology of motor control. Miller’s contributions bridge fundamental neuroscience and clinical neuroengineering, with potential impacts on spinal cord injury rehabilitation and prosthetic control systems.

Kevin F. KellyView profile

Associate Professor

• Optics and Photonics
• Nanotechnology
• Imaging and Spectroscopy
• +7 more

Kevin F. Kelly is an Associate Professor in the Department of Electrical and Computer Engineering at Rice University. He was formerly the Chair of the Applied Physics Program and is affiliated with the Smalley-Curl Institute. Additionally, he has been a member of the Penn State Center for Nanoscale Science and the Mid-Infrared Technologies for Health and the Environment (MIRTHE) Center at Princeton University. Dr. Kelly co-founded Inview Technology Corporation as its Chief Scientist, focusing on commercializing compressive imaging technologies. He has also consulted for the Baker Institute for Public Policy regarding photovoltaics and taught courses in anthropology and history at Rice. Dr. Kelly holds a B.S. in Engineering Physics from the Colorado School of Mines (1993), followed by an M.S. (1996) and Ph.D. (1999) in Applied Physics from Rice University. His postdoctoral work included fellowships at the Institute for Materials Research in Sendai, Japan, and the Chemistry Department at Penn State University. His research interests span Optics and Photonics, Imaging and Spectroscopy at the nanoscale, and the role of mathematics in image acquisition. He develops Scanning Probe Microscopy techniques and studies Electronic Materials such as graphene and topological insulators. A major focus is Compressive Hyperspectral Imaging systems, including single-pixel camera innovations and advanced microscopy methods. He also pioneers molecular machines like the Nanocar and investigates charge transport in polymer photovoltaics. Over recent years, his work emphasizes interdisciplinary applications, such as integrating compressive sensing with neural networks for machine vision and exploring technological disaster analysis through history courses. His contributions have been recognized with awards like the IEEE Fellow (2022) and Technology Review’s Top 10 Emerging Technologies (2007). In addition to academic roles, Dr. Kelly has co-founded Inview Technology and contributed to grants and collaborations through his involvement in the MIRTHE Center and other institutes. While no formal advisees are listed, his teaching includes courses on nanotechnology since 2009 and he actively engages in policy consultations for photovoltaic commercialization. His lab at Rice and collaborations with the Smalley-Curl Institute drive advancements in nanotechnology and imaging, with a particular emphasis on practical applications of compressive sensing and molecular-scale devices.

Renata DividinoView profile

Assistant Professor

• Graph Knowledge Representation
• Machine Learning
• Web Science
• +5 more

Renata Dividino is an Assistant Professor in the Department of Computer Science at Brock University, Canada. She holds a BSc from the University of Campinas (Brazil), an MSc from Universität des Saarlandes (Germany), and a PhD from Universität Koblenz – Landau (Germany). Her research focuses on graph knowledge representation, machine learning, and their applications in web science, semantic web foundations, and provenance systems. She has worked at institutions like DFKI, Fraunhofer IGD, and the Big Data Analytics Lab at Dalhousie University, bridging academic and industrial sectors. Her industry experience includes roles as an AI Scientist and Director of Data Science in the maritime sector, where she developed patented technologies for AI-driven maritime operations and risk assessment systems. Key research contributions include improving AI system reliability via provenance analysis and advancing knowledge graph applications. Education: BSc in Computer Science, University of Campinas MSc in Computer Science, Universität des Saarlandes PhD in Computer Science, Universität Koblenz – Landau Research Interests: Provenance systems, semantic web foundations, knowledge graphs, graph-based AI, maritime AI applications, and federated learning. Her work emphasizes practical applications in complex networks, web-scale data, and social networks. Awards: No specific awards mentioned, but her contributions include patented technologies and peer-reviewed publications on provenance-driven AI transparency. Advising & Grants: Secured industry grants for R&D projects in maritime AI and data science. Her work on vessel risk assessment and infectious disease prediction demonstrates applied research impact.

• Computer Vision
• Applied Machine Learning
• Computer Graphics
• +3 more

Fahim Hasan Khan is an Assistant Professor in the Computer Science and Software Engineering Department at California Polytechnic State University, San Luis Obispo (Cal Poly). His research focuses on computer vision, applied machine learning, and citizen science applications, with a special emphasis on environmental monitoring and education. He holds a PhD in Computer Science and Engineering from UC Santa Cruz, where he was advised by Professors Alex Pang and James Davis, and a Master's in Computer Science from the University of Calgary. Key research contributions include real-time rip current detection systems (RipFinder, RipScout), mobile citizen science platforms (SmartCS), and educational tools to engage high school students in STEM research. His work has received media attention for innovations in drowning prevention and environmental safety. Notable awards include the Best Poster Presentation Award at ICIAR 2019 and the Best of the Baskin School of Engineering Award at UC Santa Cruz in 2022. Dr. Khan collaborates extensively with industry and academic partners to develop practical solutions for challenges in marine safety, autonomous systems, and healthcare diagnostics. He actively mentors students and seeks to democratize access to machine learning tools through no-code platforms.

Derek BrakeView profile

Assistant Professor

• Animal Nutrition
• Ruminant Digestion
• Cattle Metabolism
• +2 more

Dr. Derek Brake is an Assistant Professor in the Division of Animal Sciences within the College of Agriculture, Food and Natural Resources at the University of Missouri. He holds a Ph.D. and M.S. in Ruminant Nutrition from Kansas State University. Dr. Brake teaches courses in Ruminant Nutrition (AnSci 4332/7332) and Beef Production (AnSci 4975/4975W/7975). His research program focuses on fundamental and applied aspects of ruminant nutrition with particular emphasis on: Nutrient utilization efficiency in beef and dairy cattle Small intestinal starch digestion mechanisms Application of computer vision and AI for precision livestock management Metabolic adaptations to different feed sources Sustainable forage and crop utilization systems Analysis of his recent publications reveals strong research focus on: Innovative feed evaluation methods Metabolic interventions for improving cattle productivity Sustainable grazing and forage management systems Advanced technologies for livestock monitoring Mycotoxin mitigation strategies Dr. Brake maintains active collaborations with the beef and dairy industries with research aimed at simultaneously improving livestock productivity, economic returns for producers, and environmental sustainability of cattle production systems.

• Computer Graphics
• Human-Computer Interaction
• Robotics
• +3 more

Dr. Daniel Berio is a researcher at Goldsmiths, University of London, specializing in computational models for human-like movement in digital art and robotics. His work bridges computer graphics, cognitive psychology, and robotic manipulation, focusing on stylized stroke generation, graffiti analysis, and kinematic modeling. He collaborates with Frederic Fol Leymarie and Rejean Plamondon, utilizing the Sigma Lognormal model to simulate human handwriting dynamics. Education : Doctoral thesis on AutoGraff (2021), exploring computational understanding of graffiti and calligraphy. Research Themes : Human-like motion in digital art, kinematic reconstruction from static traces, robotic graffiti generation, and perceptual fluency in aesthetic evaluation. Publications : 15+ works since 2015, spanning ACM Transactions on Graphics, British Journal of Psychology, and conferences like MOCO and IROS. Applications : Font stylization tools, synthetic graffiti generation, compliant robot control, and semantic typography systems.

HanQin CaiView profile

Assistant Professor

• Mathematical Optimization
• Data Science
• Machine Learning
• +5 more

Dr. HanQin Cai is the Paul N. Somerville Endowed Assistant Professor in the Department of Statistics and Data Science at the University of Central Florida (UCF), also serving as Director of the Data Science Lab. He holds a joint appointment with the Department of Computer Science. His research focuses on theoretical and algorithmic foundations of mathematical optimization, data science, and machine learning, with emphasis on non-convex algorithms, adversarial attacks, signal/image processing, and deep learning integration. He has secured NSF grants totaling over $2.6M, including a $121K single-PI grant and a $2.49M co-PI grant. His work has been recognized with the UCF OSCaR Award (2025) and IEEE Senior Member status (2024). Education: PhD in Applied Mathematical and Computational Sciences from University of Iowa (2018), with M.S. in Mathematics (2014) and M.C.S. in Computer Science (2017). Previously served as a Postdoc at UCLA Mathematics Department under Dr. Wotao Yin. Research highlights include: query-efficient zeroth-order optimization, robust signal processing with corrupted data, adversarial attacks on neural networks, and tensor-based methods for high-dimensional data analysis. His recent publications explore advanced techniques in matrix recovery, tensor decompositions, and explainable AI. Grants & Awards: NSF DMS-2304489 (2022–2025), NSF DUE-2321986 (2024–2029), UCF OSCaR Award, IEEE Senior Membership. Labs & Teams: Directs UCF's Data Science Lab, collaborates across disciplines in statistics, computer science, and engineering.

Fan YaoView profile

Associate Professor

• Computer Architecture
• Hardware Security
• AI Security
• +5 more

Fan Yao is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Central Florida's College of Engineering and Computer Science. She received her Ph.D. in Computer Engineering from The George Washington University in 2018 and currently leads the Computer Architecture and Systems Research (CASR) lab. Her research focuses on the intersection of computer architecture, security, and machine learning, with particular emphasis on hardware-based security vulnerabilities and defenses. Dr. Yao's research interests span computer architecture, hardware and system security, AI security, energy-efficient computing, and cloud computing. Her work addresses critical security challenges in modern computing systems, particularly focusing on microarchitecture attacks, hardware-based model tampering in deep learning systems, and information leakage threats in emerging non-volatile memory systems. She has developed innovative defense mechanisms against cache timing channels, branch predictor vulnerabilities, and GPU-based side channels. Her recent publications demonstrate a strong focus on AI security (particularly Deep Neural Network vulnerabilities), hardware security (including cache and branch predictor attacks), and secure memory architectures. The research shows an evolution from traditional computer architecture topics toward the security implications of AI hardware and emerging memory technologies, with increasing emphasis on practical attacks and defenses in real-world systems. NSF GW I-Corps Site Grant Award, 2018 Best Dissertation Award, GWU, 2018 The Norris & Betty Hekimian Engineering Endowment Fellowship, GWU, 2017 Top Picks in Hardware and Embedded Security, 2019 NSF CAREER project award, 2024 Dr. Yao currently leads multiple NSF-funded research projects including 'Understanding and Taming Deterministic Model Bit Flip Attacks in Deep Neural Networks' (NSF SaTC, 2020-2023), 'Towards Secure-By-Design Integration of Emerging Non-Volatile Memory in Future System' (NSF CNS, 2020-2023), and 'Architecting Secure-by-Design Memristor-Based Memories' (NSF CNS, 2019-2022). She has successfully mentored numerous PhD students, many of whom appear as first authors on top-tier conference publications, demonstrating her commitment to graduate education and research mentorship. As the leader of the CASR lab, Dr. Yao oversees a vibrant research group focused on building secure-by-design, efficient, and advanced future systems through novel techniques spanning hardware, computer architecture, and systems. The lab actively publishes at top computer architecture and security conferences including ISCA, MICRO, HPCA, IEEE S&P, and USENIX Security, with multiple papers accepted to these venues annually. The group has developed several influential tools and frameworks for security analysis, including proof-of-concept code for BranchSpec exploits that has been widely cited in the hardware security community.

Francesco GrecoView profile

Research Fellow

• Human-Computer Interaction
• Usable Security
• End-User Development
• +4 more

Francesco Greco is a Research Fellow and Ph.D. student at the University of Bari Aldo Moro's Computer Science Department, actively contributing to the Interaction, Visualization, Usability & UX (IVU) Laboratory under Prof. Maria Francesca Costabile. He completed a visiting research position at King's College London's Cybersecurity (CYS) group from October 2023 to March 2024 under Prof. Luca Viganò's supervision. His academic qualifications include: Master's degree in Computer Science (2022, University of Bari, full marks with honors) Bachelor's degree in Computer Science and Digital Communication (2020, University of Bari - Taranto, full marks with honors) Greco's research centers on Human-Computer Interaction and Usable Security , with specialized expertise in End-User Development , Internet of Things security , Computer Vision , and eXplainable AI for cybersecurity . His work develops human-centered security tools that translate technical findings into actionable user protections, particularly through phishing detection systems that generate intuitive explanations. Analysis of his 15 recent publications (2023-2025) reveals a cohesive research trajectory focused on XAI-driven security interventions . Key themes include timing optimization for phishing warnings, human factors in cybersecurity incidents, and LLM-based educational tools. His work consistently bridges theoretical HCI principles with practical security applications, as demonstrated by tools like APOLLO for phishing email analysis. Greco actively collaborates within the IVU Lab ecosystem and maintains international partnerships, including his recent work at King's College London. His research output demonstrates significant contributions to usable security frameworks and cybersecurity education methodologies.