Find Academic Supervisors

Timothy CootesView profile

Research Professor

• Medical Image Analysis
• Computer Vision
• Statistical Models of Appearance
• +3 more

Timothy Cootes is a Research Professor at the University of Manchester's Division of Informatics, Imaging & Data Sciences. He holds an MSc teaching role in Mathematical Methods and has led projects like BoneFinder and ASPIRE™. His research focuses on statistical models for medical image analysis, facial interpretation, and musculoskeletal disease diagnosis. Education: Bachelor's in Maths and Physics from Exeter University PhD in Civil Engineering (storm sewer overflow) from Sheffield City Polytechnic Research Interests: Statistical shape/appearance models for medical imaging Machine learning applications in osteoporosis/osteoarthritis analysis Facial feature tracking and recognition systems Groupwise image registration techniques Awards: 2015 ISBI Grand Challenges Prize 2010 IAPR Fellowship 2023 Highly Commended Oral Presentation Collaborations: Dr. Paul Bromiley (medical imaging) Dr. Claudia Linder (musculoskeletal projects) Dr. Adrian Davison (facial recognition) Key Impacts: Developed Active Appearance Models (AAMs) for facial recognition Co-created BoneFinder automated bone analysis software Contributed to global osteoarthritis risk assessment frameworks

John LoehrView profile

Researcher

• Wildlife ecology
• Evolutionary biology
• Conservation biology
• +7 more

John Loehr is a principal investigator (PI) and research coordinator at the University of Helsinki , serving as the head of the Wildlife Ecology Research (WildER) group at Lammi Biological Station . He also coordinates the university’s Wild Animal Phenotyping Infrastructure and is a supervisor and research coordinator for multiple doctoral programs including Wildlife Biology and Interdisciplinary Environmental Sciences. Education: PhD (specific discipline not stated) Research Interests: John’s research integrates wildlife ecology , evolutionary biology , and conservation science . His work with the Eurasian lynx explores acoustic communication and landscape-scale conservation. He also investigates microevolutionary change in amphipods, facial morphology and behavior in humans, and the life-history consequences of forced migration using the MiKARELIA database. His team pioneers non-invasive wildlife monitoring and human-wildlife coexistence technologies . Scientific Contributions: John has authored or co-authored 42 peer-reviewed publications , including recent articles in Nature Ecology & Evolution , Oecologia , and Plant Ecology . His research spans acoustic ecology, forest integrity, conservation policy, and digital humanities. Awards and Outreach: Featured in national and international media for research and science communication Leads public science trails and citizen science initiatives Developed the international internship program at Lammi Biological Station Supervision and Teaching: John supervises multiple PhD and MSc students and teaches courses such as Technology in Ecological Research and Environmental Monitoring and Mammal Ecology . He is actively involved in doctoral training across interdisciplinary environmental sciences. Labs and Teams: He leads the WildER group at Lammi Biological Station and coordinates research infrastructure across the University of Helsinki. His lab uses advanced tools such as 4G trail cameras, bioacoustic recorders, and UAVs for ecological monitoring.

Ming LiView profile

Professor

• Audio and Speech Processing
• Multimodal Behavior Signal Analysis
• Speech Synthesis
• +3 more

Ming Li is a Professor of Electrical and Computer Engineering at Duke Kunshan University's Division of Natural and Applied Science, and a Principal Research Scientist at the Digital Innovation Research Center. He holds an adjunct position as a Professor at Wuhan University's School of Computer Science. His research focuses on audio/speech processing, multimodal behavior signal analysis, and applications in autism spectrum disorder diagnosis. Li has over 200 publications and serves on editorial boards of journals like IEEE Transactions on Audio, Speech and Language Processing. Education: Ph.D. in Electrical Engineering from the University of Southern California (2013). Awards include the IBM Faculty Award (2016), ISCA 5-Year Best Paper Award (2018), and Youth Achievement Award (2020). He leads initiatives in anti-spoofing countermeasures, voice conversion, and speech synthesis. Recent Courses: Random Signals and Noise Speech Recognition Data Science Key Research Contributions: Development of datasets like KunquDB, TMCSpeech, and systems for speaker verification, deepfake detection, and autism diagnosis tools. His work bridges signal processing with clinical applications, leveraging AI for social interaction improvement in neurodiverse populations.

Clara ColombattoView profile

Assistant Professor

• Cognitive Psychology
• Social Psychology
• Vision Science
• +5 more

Dr. Clara Colombatto is an Assistant Professor in the Department of Psychology at the University of Waterloo, where she directs the Vision and Cognition Lab. She holds an honorary lecturer position at University College London and a PhD from Yale University. Her research explores how visual perception extracts social and cognitive states from others, focusing on attentiveness, confidence, and metacognition. She investigates non-biological agents like AI and the perceptual roots of social cognition, including group dynamics and moral judgment. Education: BS in Neuroscience and Philosophy, Duke University PhD in Psychology, Yale University Postdoctoral Research Fellow, University College London Research Interests: Perception of attentiveness and metacognition, social group perception, AI ethics, moral judgment, and visual cognition. Her work bridges cognitive psychology, vision science, and social psychology to understand how humans perceive other minds and interact with non-human agents. Recent Trends in Articles: Recent work emphasizes human-AI collaboration, trust in technology, and perceptual foundations of social interaction. Key themes include gaze dynamics, confidence attribution in AI, and optimal group size perception. Grants & Collaborations: Collaborates with institutions like Princeton University, University of Oxford, and Microsoft Research. Her team includes students working on topics like robot teleoperation and moral narratives. Labs & Teams: Leads the Vision and Cognition Lab at Waterloo, focusing on experimental psychology and computational modeling. Current projects explore metacognition in advice-taking and perceptual grouping in social interactions.

• Artificial Intelligence and Data Science
• Decision Making & Optimization
• Machine Learning & Intelligence
• +8 more

Min Lee is a Full-time Assistant Professor of Computer Science at Singapore Management University's School of Computing and Information Systems (SCIS). Holding a PhD from Carnegie Mellon University (2021), Lee specializes in Artificial Intelligence with a focus on Human-AI Collaborative Systems and their applications in healthcare. Their research bridges technical innovation with human-centric design, emphasizing trustworthiness, explainability, and accessibility in AI systems. Research interests include decision-making optimization, human-machine collaboration, and AI-driven solutions for healthcare challenges such as stroke rehabilitation, elderly care monitoring, and clinical decision support. Lee has pioneered low-cost AI/robotic solutions for post-stroke rehabilitation exercises, integrating socially assistive robotics with real-time feedback mechanisms. Recent work highlights AI's role in enabling trustworthy clinical decision-making through explainable AI (XAI) techniques like counterfactual explanations and gradient-based methods. Their systems address ethical concerns in predictive healthcare and advocate for stakeholder-inclusive design processes. Awards and grants: No specific awards mentioned in the profile, though research has been supported through collaborative projects with clinical partners and iterative user evaluations involving therapists and patients. Advising focuses on multidisciplinary projects spanning AI, healthcare, and human-computer interaction. Current advisees include BHOSALE Rimmon Saloman, TRAN Truong Thuy, and YANG Xinlin. Lee collaborates closely with clinical stakeholders to translate technical advancements into practical healthcare solutions, emphasizing iterative design and real-world applicability. Labs/teams: Active in SCIS's AI and Data Science initiatives, leading projects on intelligent decision support systems, socially assistive robotics for rehabilitation, and human-centered AI ethics frameworks.

Colin PalmerView profile

Research Fellow

• social vision
• sensory processing
• face perception
• +7 more

Dr. Colin Palmer is a Visiting Fellow in the School of Psychology at the University of New South Wales (UNSW), where he conducts research on visual perception with a focus on social features of our sensory environment. His work examines how the brain processes elements like eyes, faces, and behaviors of people around us using visual psychophysics, computational modeling, and 3D graphical rendering. Dr. Palmer completed his Ph.D. in 2016 and Bachelor of Behavioural Neuroscience (Honours) in 2009, both at Monash University. His doctoral research explored how neurocognitive models of sensory processing relate to differences in sensory integration and social cognition in autism. His primary research interests center on understanding the perceptual and neural mechanisms underlying our sensitivity to dynamic social cues, particularly eye and head movements. Dr. Palmer investigates how the visual system extracts basic environmental elements (color, shape, motion) and develops a mechanistic understanding of how our experience of the social world arises from nervous system activity. His work has clinical applications for understanding sensory and social difficulties in conditions like autism and schizophrenia. Dr. Palmer's recent publications reveal a consistent focus on social vision, particularly gaze perception, face processing, and animacy detection. His research increasingly incorporates computational modeling approaches to understand visual perception mechanisms. There's a strong emphasis on how lighting and shading affect face and gaze perception, with growing attention to clinical applications for neurodevelopmental conditions. Dr. Palmer has received recognition for his work through several awards: Emerging Investigator Award, Australasian Cognitive Neuroscience Society, 2017 Postdoctoral presentation award, Australasian Cognitive Neuroscience Society, 2016 Dr. Palmer is actively involved in research supervision and teaching. He teaches PSYC 3221 Vision and Brain and is available to supervise research students. His research is supported by significant funding: ARC Discovery Project (2020-2022): "Extracting meaning from motion" ($492,000) ARC Discovery Early Career Researcher Award (2019-2021): "Human sensitivity to the dynamics of other people's eye movements" ($356,000) Experimental Psychology Society Study Visit Grant (2017): "Testing computational theories of autism spectrum disorder in the social domain" (£2,580) Dr. Palmer collaborates extensively with Professor Colin Clifford at UNSW and maintains international collaborations with researchers in the UK and Australia, particularly on projects related to autism spectrum disorders and social cognition.

Thomas HeldtView profile

Associate Professor

• Computational Physiology
• Neuromonitoring
• Critical Care Informatics
• +2 more

Thomas Heldt is Associate Professor of Electrical and Biomedical Engineering in the Department of Electrical Engineering and Computer Science at MIT, and a Principal Investigator at MIT's Research Laboratory of Electronics. He leads the Integrative Neuromonitoring and Critical Care Informatics Group and serves as Associate Director of the Institute for Medical Engineering and Science. Research focuses on: Noninvasive intracranial pressure monitoring Computational models of cerebrovascular dynamics Sepsis detection algorithms Wearable physiological monitoring Clinical decision support systems Recent publications (2022-2024) demonstrate advances in hemodynamic modeling, diagnostic algorithms for critical care, and AI applications for physiological monitoring. Key innovations include open cranium models for intracranial hypertension studies, deep learning frameworks for fatigue assessment, and mobile-based neurocognitive tracking. Professor Heldt collaborates with Boston Children's Hospital, Beth Israel Deaconess Medical Center, and Boston Medical Center to translate research into clinical practice. His work has been recognized through the W.M. Keck Career Development Professorship and IEEE EMBS Distinguished Lectureship.

• VLSI CAD
• Low-power IC Design
• Mixed-signal IC Design
• +5 more

Riadul Islam serves as an Assistant Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC), maintaining his primary office in room 316 of the Information Technology and Engineering (ITE) Building. His academic appointment focuses on hardware design and verification within the institution's engineering framework. His educational qualifications include: Ph.D. in Computer Engineering from UCSC (2017) M.A.Sc. in Electrical and Computer Engineering from Concordia University, Montreal (2011) B.Sc. in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology (2007) Professor Islam's research centers on VLSI CAD tools and low-power digital/mixed-signal IC design , with significant contributions to current-mode clock networks, vehicular security systems, and error-robust circuit architectures. His work increasingly integrates machine learning for design automation while exploring neuromorphic computing applications and secure hardware implementations. This multidisciplinary approach bridges traditional IC design with modern AI-driven optimization techniques. Analysis of his 2023-2025 publications reveals three dominant research thrusts: (1) Machine learning applications in early-stage Design Rule Checking (DRC) prediction and clock network optimization, (2) Graph-based intrusion detection systems for automotive networks (particularly CAN bus security), and (3) Event-based vision systems and neuromorphic computing architectures. These areas demonstrate consistent innovation in merging hardware design with AI/ML methodologies for enhanced system reliability and efficiency. He directs the UMBC VLSI and SoC Research Group , which develops energy-efficient clocking networks, secure vehicular communication protocols, and compute-in-memory architectures. The lab maintains active collaboration with industry partners on hardware security and neuromorphic computing initiatives while supporting graduate student research in cutting-edge IC design methodologies.

• Artificial Intelligence
• Robotics
• Manufacturing Systems
• +5 more

Professor Will Browne is the Chair in Manufacturing Robotics at QUT, collaborating with the ARM Hub and CSIRO to advance robotics in manufacturing, healthcare, and industry. He holds a Doctorate in Engineering from the University of Wales and has over 30 years of expertise in AI, robotics, and Learning Classifier Systems (LCS). His research focuses on transparent AI systems like LCS for Explainable AI (XAI), human-robot collaboration, and advanced manufacturing solutions. He co-leads the $5M SfTI Robotics Spearhead project and is internationally recognized for LCS contributions, including co-authoring the first LCS textbook with Ryan Urbanowicz. Publications emphasize emotion recognition, embodied AI, and scalable machine learning. His work bridges academia and industry through the ARM Hub, fostering innovation in robotics and design-led manufacturing. Key roles include co-track chair at GECCO (2011–2025) and editorial board memberships. Research spans evolutionary computation, robotics control, and energy systems optimization.

Elisabeth Huis in 't VeldView profile

Assistant Professor

• Cognitive Neuroscience
• Artificial Intelligence
• Medical Psychology
• +4 more

Elisabeth Huis in 't Veld is an Assistant Professor at Tilburg University's Department of Cognitive Science and Artificial Intelligence, part of the Tilburg School of Humanities and Digital Sciences. She also serves as Group Leader for Donor Cognition at Sanquin in Amsterdam and co-founded AINAR B.V., a company focused on AI applications in healthcare (https://www.ainar.io). Her research spans cognitive neuroscience, medical psychology, and AI-driven solutions for health challenges. Key interests include vasovagal reactions during needle procedures, serious games for decision-making studies, and blood donor behavior analysis. Recent projects include developing predictive models for vasovagal responses using facial analysis and creating the 'Sustainable Port' serious game to study expert decision-making in port management scenarios. Publications highlight interdisciplinary work, blending AI techniques (e.g., CNNs, time-series classification) with clinical and behavioral insights. Her work addresses real-world challenges in healthcare, donor recruitment, and game-based training systems. Professional affiliations include leadership roles in academic and industry collaborations, emphasizing translational research between cognitive science and practical health applications.

• Artificial Intelligence
• Pattern Recognition
• Biometrics
• +6 more

Venu Govindaraju is a SUNY Distinguished Professor of Computer Science and Engineering at the University at Buffalo, where he also serves as Vice President for Research and Economic Development. He founded the Center for Unified Biometrics and Sensors (CUBS) in 2003 and leads the National AI Institute for Exceptional Education, funded by a $20M NSF grant. His research focuses on artificial intelligence, pattern recognition, biometrics, and handwriting analysis, with notable contributions to postal automation, cybersecurity, and early screening for dyslexia/dysgraphia in children. Govindaraju holds IEEE and ACM Fellowships, and was named Person of the Year by the Council of Heritage and Arts of India in 2024. He has advised 47 doctoral students and pioneered foundational AI technologies impacting global industries. Research interests include: AI-driven education tools, handwriting recognition systems, document analysis, and cross-domain biometric technologies. His work bridges AI with humanities through projects like the Marianne Moore Digital Archive. Recent initiatives focus on leveraging AI to address speech-language pathologies and enhance K-12 education accessibility. Leadership roles include co-chair of SUNY’s AI Task Group and advisor to the SUNY-IBM AI Research Alliance. He is a vocal advocate for responsible AI policy, advising lawmakers at White House events and contributing to legislative briefings. His innovations are highlighted in Empire AI, New York’s $400M computing consortium.

• Eye tracking
• Online teaching
• Human-computer interaction
• +5 more

Marian Sauter is a researcher at Ulm University's Institute for Psychology in Germany, specializing in the intersection of psychology, human-computer interaction, and educational technology. Her work primarily focuses on eye-tracking methodologies and gaze analysis to improve online teaching and learning environments. She frequently collaborates with colleagues from the Institute of Media Informatics at Ulm University, indicating strong interdisciplinary connections between psychology and computer science departments. Her research interests center on understanding visual attention patterns in educational contexts, particularly how gaze behavior correlates with learning outcomes in digital environments. Dr. Sauter investigates how eye movement data can be visualized and utilized to optimize instructional design, with specific attention to gaze-laser pointer relationships, gaze synchronicity among students, and attention monitoring in virtual classrooms. Her work bridges cognitive psychology theories with practical applications for e-learning platforms and remote education systems. Analysis of her recent publications reveals a strong trajectory in applying eye-tracking technology to solve real-world educational challenges, particularly those emerging during and after the pandemic-driven shift to online learning. Her research spans both theoretical investigations of visual attention mechanisms and practical implementations for educational technology. The consistent focus on webcam-based eye-tracking demonstrates her commitment to developing accessible methods that don't require specialized hardware, making her research particularly relevant for widespread educational applications. Best Paper Award at ETRA '22 Symposium on Eye Tracking Research and Applications Berlin Health Award (2023) Dr. Sauter's collaborative approach is evident in her frequent co-authorship with Tobias Wagner from Ulm University's Institute of Media Informatics and Anke Huckauf from General Psychology. Her research appears to be supported by institutional resources at Ulm University, though specific external grants are not mentioned in the available information. She appears to be actively mentoring students given her involvement in educational psychology research and student-focused studies. Her work suggests involvement with research groups focused on human-computer interaction, educational technology, and cognitive psychology at Ulm University, though specific lab names are not provided in the available information. The interdisciplinary nature of her publications indicates collaboration across psychology and media informatics departments.

• Intelligent Systems and Control
• Cognitive Robotics
• Nano-robotics
• +9 more

Laxmidhar Behera is a Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur, specializing in Intelligent Systems and Control. With over two decades of academic experience at IIT Kanpur and international research experience at institutions including Fraunhofer Institute of Autonomous Intelligent Systems in Germany, ETH Zurich, and University of Ulster, he has established himself as a leading researcher in cognitive robotics and intelligent control systems. Dr. Behera's research spans multiple cutting-edge domains including Cognitive Robotics, Nano-robotics, Vision based Control, Soft Computing, Information Retrieval in music and language, Semantic Information Processing, Physics of Complex Systems, Cyber Physical Systems, Formation Control of UAVs, Brain-Computer Interface (BCI), and Sanskrit Computational Linguistics. His interdisciplinary approach bridges traditional control theory with modern computational intelligence techniques, creating innovative solutions for complex real-world problems. His extensive publication record in top-tier journals like IEEE Transactions demonstrates his leadership in areas such as brain-computer interfaces, visual servoing, multi-robot systems, and music information retrieval. Notably, his work on quantum neural networks for EEG filtering and multisatellite formation control has received significant attention in the research community. UKIERI Standard Research Award 2008 Best Paper at International Conf. on Intelligent Sensors and Information Processing (ICISIP-2004) Best Paper at WoSco,02, Int. Conf. High-Performance Computing (HiPC, 2002) AICTE career award for young teacher (1997) Senior Member IEEE Multiple IEEE top accessed articles (2009-2010) As an Associate Editor for Autosoft Journal and Technical Committee Member for Intelligent Control at IEEE Control System Society, Dr. Behera actively contributes to the academic community. His laboratory in the Western Lab - 212A of the Department of Electrical Engineering serves as a hub for research in intelligent systems, where he mentors students and collaborates with researchers worldwide on cutting-edge projects in robotics, control systems, and computational intelligence.

Igor S. PandžićView profile

Professor

• Computer Vision
• Virtual Humans
• Facial Animation
• +7 more

Prof. Dr. Igor S. Pandžić is a Full Professor at the Department of Telecommunications , Faculty of Electrical Engineering and Computing (FER) , University of Zagreb . He is also affiliated with the Center of Excellence for Computer Vision , actively contributing to research in computer graphics, virtual humans, and interactive systems. Education: While specific degrees are not listed in the provided text, his title "prof. dr. sc." (Croatian for Full Professor with a Doctor of Science degree) indicates he has completed doctoral studies and achieved the highest academic rank. Research Interests: Virtual Humans & Avatars: Creation, animation, and behavioral modeling of lifelike virtual characters for interactive applications. Facial Animation & Biometrics: Real-time speech-driven facial animation, facial expression recognition, age/gender estimation, and biometric data filtering. 3D Graphics & Virtual Environments: Real-time rendering, networked collaborative virtual environments (e.g., VLNET), and 3D visualization on mobile/web platforms. Human-Computer Interaction: Multimodal interfaces combining speech, gesture, and facial cues for embodied conversational agents (ECAs). Computer Vision: Face alignment, landmark detection, pattern recognition with decision trees, and efficient algorithms for mobile deployment. Research Trends from Publications: His recent work (2022-2023) emphasizes efficiency in computer vision (e.g., fast face alignment, memory-efficient models), unsupervised learning for biometric data quality improvement, and real-time applications on mobile devices. Earlier foundational work spans MPEG-4 facial animation standards, networked virtual environments, and virtual human frameworks. Scientific Awards: No specific awards are mentioned in the provided text. Advising & Grants: While individual students are not named, his extensive publication record and leadership in research centers imply active supervision of PhD and Master's students. Grant details are not specified. Labs & Teams: He leads or heavily contributes to the Center of Excellence for Computer Vision at FER, fostering interdisciplinary collaboration in visual computing and AI.

• Visual Perception
• Face Recognition
• Object Recognition
• +2 more

Alejandro Estudillo is a Principal Academic in Psychology at Bournemouth University, where he researches visual perception and mathematical cognition. He holds a PhD in Human Cognitive Psychology and Neuropsychology from the University of Kent, complemented by MSc degrees from the University of Edinburgh and University of Granada, and a BSc from the University of Málaga. His research examines how visual systems process information for object/environment recognition and mathematical operations. Primary interests include face recognition mechanisms, holistic/featural processing, and cognitive neuroscience. He employs eye-tracking, EEG-ERP, and behavioral paradigms to study these phenomena across diverse populations. Recent publications (2022-2025) cluster around three themes: (1) face processing (mask/inversion effects, cross-race recognition), (2) neurocognitive techniques (tDCS/tRNS modulation of face areas), and (3) applied cognition (eyewitness identification, mathematical performance). Grants & Labs: British Academy grant for facial-composite optimization (2024) Malaysian/Spanish grants for face recognition/dyscalculia research (2012-2019) Established Cognitive Neuroscience and Learning Laboratory (2018) and Neurophysiology Lab (2019) He actively supervises MRes/PhD students in face processing and mathematical cognition.