جست‌وجوی استاد و پژوهشگر

• Autonomous Underwater Vehicles
• Robotics
• SLAM (Simultaneous Localization and Mapping)
• +۳ مورد دیگر

Dr. Gideon Billings is a Research Fellow at the School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney. His work focuses on advancing autonomous underwater systems, robotics, and computer vision applications in marine environments. He is affiliated with the ACFR (Australian Centre for Field Robotics) Research Facilities, contributing to cutting-edge technologies like autonomous underwater gliders and shared autonomy systems. His research interests include underwater navigation, SLAM (Simultaneous Localization and Mapping), and precision manipulation in subsea environments. He has developed algorithms for 6D object pose estimation using monocular fisheye imaging and hybrid visual SLAM systems for robotic vehicles. His work bridges robotics engineering with marine science, enhancing unattended oceanographic exploration and subsea operations. Recent projects include a sovereign Autonomous Underwater Vehicle (AUV) capability grant (2024), demonstrating his role in advancing Australia's marine robotics infrastructure. Though no formal awards are listed, his publications in top-tier journals like Science Robotics and IEEE Transactions reflect his impactful contributions to the field. Advising and grants highlight his collaboration with industry partners like Rovver Robotics and academic institutions, focusing on underwater optical systems and navigation technologies. His lab's work is centered at the J18 ACFR facilities, advancing both theoretical and applied aspects of marine robotics.

• Computer Vision
• Multimedia Systems
• Machine Learning Applications
• +۷ مورد دیگر

Timothy K. Shih is an active academic researcher with over 30 years of scholarly contributions, evidenced by his extensive publication record from 1991 through 2025. With more than 380 publications spanning numerous prestigious venues including IEEE Access, Multimedia Tools and Applications, and Lecture Notes in Computer Science, he maintains a robust research profile with consistent annual output (20+ papers in peak years). His work demonstrates leadership through frequent senior/corresponding author positions and collaborations with numerous researchers across international institutions. Dr. Shih's research interests encompass a diverse range of computer science disciplines with particular emphasis on Computer Vision , Human-Computer Interaction , and AI Applications . His work bridges theoretical advancements with practical implementations in educational technology, accessibility solutions, and multimedia systems. Recent publications reveal a strategic focus on applying deep learning techniques to solve real-world problems in sign language recognition, gesture analysis, and wireless sensing applications. Analysis of his publication trends over the past five years shows increasing specialization in multimodal AI systems, with significant contributions to sign language technology (including Arabic Sign Language recognition), WiFi-based human activity recognition, and music technology applications. His research demonstrates strong interdisciplinary connections between computer vision, machine learning, and human-centered computing, with practical applications spanning educational technology, accessibility solutions, and smart environments. Through his mentorship, Dr. Shih has guided numerous junior researchers who have become frequent collaborators, including Chih-Yang Lin, Hsin-Hung Cho, and Tipajin Thaipisutikul. His research program appears well-funded through consistent publication output across multiple project areas, suggesting successful grant acquisition in computer vision, AI, and educational technology domains. Current work indicates active involvement in cutting-edge research on diffusion models for audio processing, enhanced sign language recognition systems, and novel approaches to WiFi-based human interaction analysis.

John R. Kenderمشاهده پروفایل

استاد

• Computer Vision
• Artificial Intelligence
• Video Understanding
• +۷ مورد دیگر

John R. Kender is a Professor in the Department of Computer Science at Columbia University, affiliated with the Fu Foundation School of Engineering and Applied Science. He previously served as former Vice Dean of the same school and has been recognized as a Great Teacher by the Society of Columbia Graduates and a Distinguished Faculty Teacher by the Columbia Engineering School Alumni Association. His research focuses on computer vision, video understanding, gesture recognition, and artificial intelligence, supported by grants from DARPA, NSF, and industry partners like IBM and Microsoft. He has advised over 25 doctoral students and co-chaired the 2016 ACM International Conference on Multimedia Retrieval (ICMR). His teaching spans courses in object-oriented design, visual interfaces, and visual databases, with a strong emphasis on project-based learning. Kender's work includes pioneering contributions to semantic video indexing, multimodal analysis, and transfer learning techniques. Education: Advanced degrees in Computer Science (details not explicitly stated in texts). His research interests bridge computational methods and human-centric applications, including gesture-audience engagement correlations using EEG, cultural differences in news video production, and visual meme tracking in social media. His projects, such as the NSF-funded 'Tagging and Browsing Videos According to Affinity Groups,' highlight user-centered multimedia systems. Kender's work has been published in over 100 peer-reviewed articles and patents, addressing challenges in video segmentation, motion anticipation, and neuroimage alignment. Recent articles explore geometric approaches to transfer learning, 3D motion prediction, and cultural analysis through computational aesthetics. These reflect a trend toward cross-disciplinary methods combining AI, computer vision, and social sciences. Awards: Multiple teaching recognitions, including Columbia's top faculty teaching honors. Advising highlights include mentoring students across decades, with notable alumni contributing to academia and industry. His grants total over $10M from federal and industrial sources. Kender's lab focuses on real-world applications like educational video analysis, medical imaging, and event-driven multimedia systems. Current projects include developing explainable AI for video clustering and probabilistic GAN-based human motion prediction.

Robert A. Jacobsمشاهده پروفایل

استاد

• Cognitive Science
• Neuroscience
• Machine Learning
• +۴ مورد دیگر

Robert A. Jacobs is a Professor at the University of Rochester, holding joint appointments in the Department of Brain & Cognitive Sciences, the Department of Computer Science, and the Center for Visual Science. He is also affiliated with the Center for Computation and the Brain. His primary research focuses on computational models of human learning, memory, and decision-making in perceptual and cognitive domains, particularly through the Computational Cognition and Perception Lab. Education: Jacobs earned a B.A. in Psychology from the University of Pennsylvania (1982), followed by an M.S. (1987) and Ph.D. in Computer and Information Science from the University of Massachusetts Amherst (1990). He completed postdoctoral training at MIT (1990-1991) with Michael Jordan and at Harvard University (1991-1992) under Stephen Kosslyn. Research Interests: Jacobs' work spans cognitive and perceptual learning, neural computation, multisensory perception, visual short-term memory, and computational models like Bayesian ideal observers and actors. He explores how humans learn to integrate sensory cues optimally and how low-level perceptual data form high-level conceptual knowledge. Recent studies emphasize perceptual training methods, capacity limitations in memory, and the parallels between human cognition and machine learning algorithms. Scientific Awards: Jacobs has been honored with the Best Paper Award at the 2006 Cognitive Science Society Conference for his work on motor synergies and two McDonnell-Pew Postdoctoral Fellowships (1990–1992). His research consistently addresses optimal behavior through computational models, bridging neuroscience and machine learning. Advising & Grants: Jacobs has advised postdoctoral fellows and graduate students, including Ruoyang Hu and current collaborators such as C.R. Sims and J.S. German. His grants include NSF funding for perceptual learning and multisensory representations, as well as collaborative projects with geologists like J.A. Tarduno to study visual training in natural environments. He is a past editor of journals like Cognitive Science and Topics in Cognitive Science . Labs & Collaborations: The Computational Cognition and Perception Lab employs experimental and computational methods to study learning and memory. Notably, the lab is no longer accepting new trainees but continues research on Bayesian models of perception and neural representations. Jacobs collaborates with interdisciplinary teams across neuroimaging, geology, and machine learning.

Andreas Bartelsمشاهده پروفایل

استاد پژوهشی

• Visual Perception
• Cognitive Neuroscience
• Neuroimaging
• +۱۴ مورد دیگر

Andreas Bartels leads the Vision and Cognition research group at the Werner Reichardt Centre for Integrative Neuroscience, University of Tübingen. His work focuses on high-level visual perception in the human brain, integrating vision with attention, memory, and social cognition. He employs advanced neuroimaging and brain stimulation techniques to explore how we perceive motion, space, emotions, and scenes. His research interests lie at the intersection of cognitive neuroscience and visual perception. He investigates how the brain processes natural scenes, interprets dynamic facial and bodily expressions, and maintains perceptual stability by integrating visual input with proprioceptive and vestibular signals. His lab uses illusions and bistable stimuli to dissociate neural processing from conscious perception, aiming to uncover the mechanisms underlying Gestalt perception and scene segmentation. The recent publications reflect a strong focus on fMRI-based neural decoding, particularly in visual and parietal cortices. Themes include color imagery, spatial representation beyond the visual field, perceptual organization, and the influence of prior knowledge on perception. Methodologically, the work emphasizes multivariate pattern analysis and high-field (9.4T) fMRI for detailed cortical mapping. Andreas Bartels collaborates with neurologists and psychiatrists to study disorders such as autism, schizophrenia, and ADHD, examining both behavioral and neural underpinnings. His lab uses TMS for causal inference and combines it with fMRI to observe downstream neural effects. Eye tracking is integrated across most experiments for precise behavioral monitoring. The lab utilizes a range of stimuli—from controlled 3D dot fields and binocular rivalry to virtual reality and natural movies—to probe different aspects of vision and cognition. The overarching goal is to understand how the brain constructs a coherent and meaningful representation of the world from fragmented sensory inputs.

Kevin D. Wilsonمشاهده پروفایل

دانشیار

• Cognitive Neuroscience
• Neuroscience
• Psychology
• +۳ مورد دیگر

Dr. Kevin D. Wilson is an Associate Professor of Psychology at Gettysburg College, serving as co-chair of the Neuroscience program. He specializes in cognitive neuroscience, combining non-invasive brain stimulation (tDCS), eye-tracking, and behavioral measures to study attention, memory, and emotional regulation. His academic journey includes a PhD in Experimental Psychology from the University of Pennsylvania (1999), a postdoctoral fellowship at Duke University, and a BA from Swarthmore College (1992). Research Interests: Dr. Wilson’s work focuses on understanding higher-level cognitive and social processes through innovative neuroscientific methods. His lab, the Cognitive Neuroscience Laboratory, explores topics like viewpoint-dependent neural activity, spatial attention mechanisms, and the integration of brain stimulation into undergraduate education. Recent studies highlight interdisciplinary approaches, including linking neuroscience with literature and addressing replication in psychological research. Teaching: Courses taught include PSYCH 238 Cognitive Neuroscience and PSYCH 305 Experimental Methods , emphasizing hands-on experience with advanced techniques like fMRI and tDCS. Labs/Teams: Director of the Cognitive Neuroscience Laboratory , fostering undergraduate research in cognitive processes and neurotechnology integration.

Richard Harveyمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Computer Science
• +۳ مورد دیگر

Richard Harvey is a Professor in the School of Computing Sciences at the University of East Anglia (UEA), holding additional roles as Honorary Professor and Academic Director for Admissions and Internationalisation. His academic background includes a PhD in statistical estimation theory applied to synthetic aperture problems, with early career work in signal processing and acoustics. He transitioned to Computer Vision and AI, pioneering novel image analysis algorithms like 'sieves' and focusing on lip-reading research. His external roles include Former Livery Professor of IT at Gresham College and Fellowships at Gresham College and the British Computer Society. Research interests span computer vision, artificial intelligence, and lip-reading, with contributions to automated systems for security (e.g., baggage scanning) and accessibility (e.g., navigation aids for visually impaired individuals). Key projects include the AgriFoRwArdS CDT and collaborations with industry. He has led initiatives such as the Low Carbon Innovation Fund (LCIF) Investment Committee. His work integrates machine learning, pattern recognition, and multimodal data analysis, with publications in journals like Applied Sciences and Speech Communication. Awards include recognition for his contributions to computing and education.

• Transportation Safety
• Traffic Engineering
• Human Factors in Transportation
• +۳ مورد دیگر

Felix Wilhelm Siebert is an Assistant Professor at the Department of Technology, Management and Economics within the DTU Management faculty at the Technical University of Denmark (DTU). His research focuses on transportation safety, human factors in urban mobility, and the application of computer vision and robotics in traffic analysis. Key areas of interest include cycling and e-scooter safety, helmet use compliance, and the psychological and environmental factors influencing risk behaviors. His work spans cross-cultural studies, such as assessing affinity for technology (TAEG Questionnaire), analyzing motorcycle helmet usage in Southeast Asia and Madagascar, and developing models for robot-pedestrian interaction based on proximity theory. He has also contributed to improving assistive technologies like hybrid eye typing interfaces and has explored the impact of substances like nitrous oxide on driving performance. Notable contributions include automated detection systems for helmet use using deep learning, safety perception studies through image comparisons, and behavioral analyses of e-scooter riders in urban settings. His research integrates interdisciplinary methods, combining computer vision, behavioral economics, and urban planning to address real-world transportation challenges. Dr. Siebert has published extensively on topics ranging from traffic conflict techniques to policy implications of shared mobility systems. Despite no listed awards, his work demonstrates significant engagement with global public health and infrastructure safety initiatives.

Robert Cookمشاهده پروفایل

استاد

• Animal cognition
• Visual perception
• Discrimination learning
• +۳ مورد دیگر

Robert Cook is Professor of Psychology at Tufts University School of Arts and Sciences and Interim Chair of Sociology. His NIH-supported research examines animal cognition, visual perception, discrimination learning, and memory mechanisms across species including pigeons, starlings, and humans. Research investigates the perceptual and cognitive processes underlying object recognition, same-different concept learning, action recognition, temporal information processing, and equivalence class formation. Studies explore how animals form perceptual representations to guide learning and behavior. Recent publications advance understanding of visual and auditory processing, sequential learning dynamics, perceptual grouping, and cross-species cognitive strategies. Work integrates computational modeling with behavioral experiments to characterize cognitive architecture. Teaches courses on cognition origins and comparative psychology. Serves as Dean of the Graduate School of Arts and Sciences and publishes Comparative Cognition & Behavior Reviews. Developed online multimedia resources including Avian Visual Cognition and Animal Spatial Cognition cyberbooks.

Andrea Cavallaroمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Computer Vision
• +۳ مورد دیگر

Andrea Cavallaro is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), serving as Director of the Idiap Research Institute. His roles span academia and leadership, including directing the Centre for Intelligent Sensing and holding editorial positions in top journals. He specializes in machine learning for multimodal perception, privacy-preserving AI, and autonomous systems. Cavallaro leads projects like CORSMAL (multimodal object recognition) and GraphNEx (explainable AI). Education: PhD in Electrical Engineering from EPFL (2002). Notable awards include the Royal Academy of Engineering Teaching Prize (2007) and IEEE AVSS Best Paper Award (2009). He is a Fellow of the Higher Education Academy, International Association for Pattern Recognition, and ELLIS. Research focuses on aligning AI with societal values, particularly privacy and trustworthiness. Recent work explores adversarial attacks, privacy personas, and robotic manipulation. His 2025 publications address 3D reconstruction, privacy-aware models, and human-robot interaction. PhD students: 11 advisees in deep learning and robotics. Grants/Projects: AlignAI (trustworthy LLMs), GraphNEx (GNNs for XAI), CORSMAL (multimodal sensing). Labs/Teams: Idiap Lab (EPFL), leading CORSMAL consortium, and Turing Institute collaboration.

Heike Trautmannمشاهده پروفایل

استاد

• Multi-criteria optimization
• Evolutionary optimization
• Automated algorithm selection and configuration
• +۶ مورد دیگر

Prof. Dr. Heike Trautmann is a leading researcher in statistics and optimization at the University of Twente (2021-2026) and former Professor at WWU Münster (2013-2016). Her work bridges computational statistics, evolutionary optimization, and social media analytics. She has held visiting positions at TU Dortmund, Leiden University, and RWTH Aachen. Current affiliation: University of Twente (Data Science: Statistics and Optimization) Previous roles: WWU Münster (Professor for Information Systems and Statistics), TU Dortmund (Postdoctoral researcher) Research Focus: Multi-criteria optimization, automated algorithm selection, data stream mining, and disinformation detection in social media. Her methodological innovations in exploratory landscape analysis and evolutionary computation have transformed algorithm configuration practices. Developed COSEAL consortium for algorithm selection Co-founder of Benchmarking Network (2019) Principal investigator in projects like PropStop and MODERAT! Academic Contributions: Over 150 publications in top venues like GECCO, PPSN, and Evolutionary Computation journal. Pioneered feature-based landscape analysis tools (flacco, pflacco) and stream clustering frameworks.

Martin T. Whiteمشاهده پروفایل

دانشیار

• Thermal Power Systems
• Organic Rankine Cycle (ORC) Technology
• Supercritical Carbon Dioxide (sCO₂) Power Cycles
• +۷ مورد دیگر

Dr. Martin T. White is an Associate Professor in Mechanical Engineering at the University of Sussex, part of the School of Engineering and Informatics and the Energy and Materials Engineering Research Centre (EMERC). He holds a PhD from City, University of London (2015) and an MEng in Mechanical Engineering from the University of Southampton (2011). Prior to his current role, he served as Senior Lecturer (2022–2024) and Lecturer in Thermal Power (2019–2022) at City, University of London, and held postdoctoral roles at Imperial College London and City. He is a Fellow of the Higher Education Academy and a member of the Institution of Mechanical Engineers. His research focuses on novel thermal power systems, particularly organic Rankine cycles (ORC), supercritical carbon dioxide (sCO₂) turbines, and waste-heat recovery. He leads the development of experimental test rigs and computational tools like pocketTHERM and pocketORC , which enhance education and design in thermodynamics. Key projects include the EU-funded SCARABEUS initiative, designing axial turbines for concentrated solar power, and optimizing turbine blades for CO₂ blends. Recent work includes advancing wet-to-dry expansion in ORC systems using non-equilibrium CFD simulations and experimental rigs. He has supervised PhD students (e.g., Charlie Westpfel, Pawel Ogrodniczak) and co-supervised Salma Salah, who successfully completed her viva in 2023. His research spans turbine aerodynamics, fluid dynamics, and educational technology, addressing global energy challenges through innovative thermal systems. Key achievements include the Royal Academy of Engineering Research Fellowship (2019–2024), which supported studies on two-phase expansion in ORC turbines. His work integrates academic and industrial collaboration, exemplified by the SCARABEUS turbine design with industrial partners. Future directions include experimental validation of two-phase expansion and exploring CO₂-blend applications in heat pumps and refrigeration systems. Awards and recognition include the IMechE awards for student projects supervised and the University of Sussex’s Brian Roberts Prize for academic excellence. His interdisciplinary approach bridges fundamental research, engineering design, and educational innovation in sustainable energy systems.

Raj Sunderramanمشاهده پروفایل

استاد

• Databases
• Data Mining
• Big Data
• +۶ مورد دیگر

Raj Sunderraman is a Professor of Computer Science at Georgia State University, specializing in databases, data mining, and logic programming. His research focuses on deductive databases, semantic web technologies, bioinformatics, and graph data modeling. He holds a B.E. (Honors) in Electronics Engineering from Birla Institute of Technology and Science, an M.Tech. in Computer Technology from Indian Institute of Technology Delhi, and a Ph.D. in Computer Science from Iowa State University. His research projects include NeuronBank—a tool for cataloging neuronal circuitry—and work on scalable graph storage systems for big data. He has developed a programming environment for protein structure data and contributed to the paraconsistent relational data model. His teaching and research emphasize practical applications in bioinformatics, geoinformatics, and software systems. Key areas of exploration include reasoning with incomplete/inconsistent data, deductive database semantics, and graph query languages. Recent work involves lambda calculus visualization tools and 3D perception benchmarks for UAVs. He has authored over 150 publications and a textbook on Oracle 10g programming.

Sebastian McBrideمشاهده پروفایل

مدرس

• Animal Cognition
• Neurophysiology of Behavior
• Cognitive Neuroscience
• +۱۱ مورد دیگر

Dr. Sebastian McBride is a lecturer in the Department of Biosciences at Aberystwyth University. He holds a BSc from the University of Liverpool and a PhD from the University of Edinburgh. He first joined Aberystwyth in 1996 in the Institute of Rural Sciences, later transitioning to the Department of Computer Science and then conducting research in cognitive neuroscience at the University of Cambridge. He returned to Aberystwyth University in 2016, where he currently conducts research in animal cognition and neurophysiology of behavior. Education: BSc, University of Liverpool; PhD, University of Edinburgh Appointments: Lecturer, Department of Biosciences, Aberystwyth University (2016–present); previously held positions in Computer Science and Rural Sciences at Aberystwyth, and research roles at Cambridge. Dr. McBride's research focuses on animal cognition, particularly in relation to human neurodegenerative diseases, repetitive behavioral disorders, and cognitive enrichment in farm and captive animals. His work spans equine and ovine models, with strong emphasis on welfare, sleep, and emotional states. He has led externally funded projects on depression in horses and Alzheimer’s modeling in sheep, contributing significantly to veterinary behavior and translational neuroscience. His recent publications demonstrate a strong interdisciplinary trend, combining neuroscience, veterinary science, computer vision, and behavioral ecology. Key themes include equine depression, cognitive flexibility in sheep, automatic behavior detection using AI, and the gut-brain axis. His work is frequently covered in press and media, highlighting public interest in animal mental health. Scientific Awards and Recognition: No specific awards listed, but extensive media coverage (28 items) and high public engagement (picked up by 18 news outlets, multiple social media mentions). Principal Investigator on two externally funded research projects. Dr. McBride advises PhD students including Jacky Kuo and Claire O'Brien in the Department of Life Sciences. He leads research projects on cognitive testing in horses and developing ovine models of human disease. His work contributes to UN Sustainable Development Goals related to life on land and responsible consumption. He maintains active research datasets and collaborates across institutions including Hartpury University and the University of Cambridge.

Antonella Palmeseمشاهده پروفایل

استادیار

• Observational Cosmology
• Gravitational Wave Astronomy
• Galaxy Evolution
• +۵ مورد دیگر

Antonella Palmese is an Assistant Professor in the Department of Physics at Carnegie Mellon University's Mellon College of Science. She specializes in observational cosmology, gravitational wave astronomy, and galaxy evolution, with affiliations to the Dark Energy Survey (DES), Dark Energy Spectroscopic Instrument (DESI), Laser Interferometer Space Antenna (LISA), Rubin Observatory Legacy Survey of Space and Time (LSST), and the Cosmic Explorer Consortium. Her research leverages galaxy surveys and gravitational wave data to study cosmic dynamics, dark energy, and the formation of compact object binaries. Ph.D., University College London (2018) M.Sc. and B.Sc., La Sapienza University of Rome (2013, 2011) Previous roles: NASA Einstein Fellow at UC Berkeley (2021–2022), Postdoctoral Research Associate at Fermilab (2018–2021), Associate Fellow at the University of Chicago (2019–2021) Her research lies at the intersection of gravitational wave experiments , large galaxy surveys , and time domain astronomy . Key projects include standard siren cosmology, GW follow-up with DECam, and galaxy cluster studies. She leads or co-leads initiatives like the DESI Transients and Low-redshift Cosmology Working Group, the GW-MMADS survey, and the DESIRT project. Recent publications focus on gravitational wave cosmology (e.g., Hubble constant measurements), binary black hole mass distributions , AGN flare correlations , and dust attenuation in galaxies . Her work integrates data from LIGO/Virgo runs, DES, DESI, and LSST to address fundamental cosmological questions. 2022 : Leonardo Da Vinci Award for Physics, Math, and Engineering 2021 : NASA Einstein Fellowship 2020 : Fermilab Exceptional Performance Recognition Award 2019 : Royal Astronomical Society Michael Penston Prize runner-up 2014 : Enrico Persico award Antonella uses facilities like the Dark Energy Camera (DECam) and contributes to multi-messenger astronomy by linking gravitational waves to electromagnetic transients. Her extracurricular interests include beach volleyball, crossfit, and visual arts.