Find Academic Supervisors

• Virtual Reality
• Augmented Reality
• Human-Computer Interaction
• +4 more

Francisco R. Ortega is a researcher at Colorado State University specializing in Virtual Reality (VR) , Augmented Reality (AR) , and Human-Computer Interaction (HCI) . His work focuses on improving user experience through multimodal interaction, cognitive load theory, and immersive analytics. Recent publications explore diverse applications including: Stress reduction via VR forest bathing Instruction methods for AR-based autism support systems Annotation design in extended reality AR notifications in cooking environments Gender-swapping VR for stereotype threat mitigation Comparative studies between AR and VR for data-driven storytelling His research integrates principles from: Computer Science Psychology Human Factors Education Technology Collaborations span institutions such as University of Florida, University of Seville, and technical teams across North America. While specific educational details remain undisclosed, his 20+ peer-reviewed publications since 2018 demonstrate deep contributions to VR/AR methodology, particularly in: Unconstrained gesture elicitation Spatial transformation challenges Immersive training systems Hybrid display environments Imperfect machine learning integration

• Co-design
• Healthcare Design
• Data Visualization
• +4 more

Ian Gwilt is a Research Professor in the Department of Design at UniSA Creative, University of South Australia. His work focuses on co-design methodologies, healthcare innovation, data visualization, and museum experience design. He has authored/co-authored numerous articles on topics such as human-robot interaction, sustainable energy practices, and participatory exhibition development. His research emphasizes translating complex data into tangible experiences and fostering stakeholder collaboration in complex systems. Affiliations: UniSA Creative (Faculty/School) Department of Design MOD. (Museum of Discovery) Australian Research Centre for Interactive and Virtual Environments (IVE) Research Interests: Gwilt’s primary areas include co-design frameworks for healthcare systems, data physicalization, museum visitor engagement, and the application of visual communication in interdisciplinary research. He explores how design thinking can address challenges in sustainability, chronic disease management, and human-technology interaction. Key Contributions: Developed protocols for wearable activity trackers in hospital settings through co-design. Authored influential work on graphical signage to reduce anxiety in human-robot collaboration. Co-created the “data souvenir” concept to bridge physical and digital museum experiences. Received the International Federation of Hospital Engineering Building Award 2018 for healthcare architecture analysis. Grants & Projects: His work is supported by grants such as the Medical Research Future Fund (MRFF). Projects include developing patient experience toolkits and exploring midwifery practices through co-design. Labs/Teams: Active in MOD. (Museum of Discovery) and collaborates with interdisciplinary teams in healthcare, architecture, and digital innovation.

Sonit SinghView profile

Lecturer

• Artificial Intelligence
• Computer Vision
• Natural Language Processing
• +7 more

Dr. Sonit Singh is a Lecturer at the School of Computer Science and Engineering at the University of New South Wales (UNSW), Sydney, Australia. Prior to this role, he served as a Postdoctoral Research Fellow at the same institution. He holds a PhD from Macquarie University, completed in collaboration with Macquarie. His research focuses on artificial intelligence, computer vision, natural language processing, and their applications in healthcare, particularly in medical imaging and diagnostic systems. Dr. Singh's academic background includes a strong emphasis on biomedical imaging, intelligent robotics, and computing education. His work bridges machine learning advancements with real-world clinical challenges, such as tumor segmentation, radiology report generation, and disease detection through imaging modalities like CT scans and ultrasound. His recent publications highlight innovations in 3D medical image localization, robust tumor segmentation networks, and clinical context-aware radiology report systems. These contributions underscore his expertise in integrating deep learning techniques with healthcare workflows. Dr. Singh collaborates with multidisciplinary teams, including medical professionals, to develop solutions for challenges like placenta segmentation and dietary intake monitoring using AI. While no specific awards or grants are explicitly listed, his extensive publication record reflects sustained academic engagement in high-impact areas. His research often addresses practical clinical needs, such as improving diagnostic accuracy and patient care through advanced computational methods.

• Computer Vision
• Biologically Motivated Vision
• Robot Vision Systems
• +4 more

Paul Siebert is a Reader in Computing Science at the University of Glasgow, specializing in computer vision and robotics. He leads the Computer Vision and Graphics research group and teaches Digital Image Processing and Computer Systems. His research focuses on 3D vision systems, biologically inspired vision, and cognitive robot vision, with applications in clinical and media domains. He has pioneered commercial 3D surface scanning technology and collaborated with clinical groups such as Glasgow Dental School. Affiliations: University of Glasgow (Computing Science Department) Roles: Reader, Group Leader (Computer Vision and Graphics) Research interests include active binocular robot vision, 2D/3D sensing, and visual perception for robotics. Notable projects include work on driver attention monitoring, virtual character creation, and clinical anatomical imaging. Siebert previously directed the 3D-MATIC Faraday Partnership and served as Chief Executive of the Turing Institute, developing commercial vision systems. Publications span over 140 works, emphasizing applications like rain removal algorithms, continual learning in robotics, and foveated imaging. His work integrates deep learning, biological vision models, and real-world robotics challenges. Awards and recognitions are not explicitly listed, but his contributions to 3D vision commercialization and robotics research highlight significant impact in the field.

• Artificial Intelligence
• Uncertainty Theory
• Belief Functions
• +7 more

Professor Fabio Cuzzolin is a Professor of Artificial Intelligence at Oxford Brookes University, leading the Visual Artificial Intelligence Laboratory (VAIL). He holds roles in the Institute of Ethical AI and the Healthy Ageing and Care (HAC) network. His research focuses on uncertainty theory, belief functions, and their applications in AI, surgical robotics, healthcare, and autonomous driving. He has secured over £5.4M in external funding, including the H2020 Epistemic AI project. Education and Academic Background: While specific formal education details are not explicitly stated in the text, Professor Cuzzolin holds a PhD and has held academic positions across institutions in Italy, the US, and the UK. His teaching experience spans three countries, including roles as a Module Leader for undergraduate and postgraduate courses in Computer Vision, Machine Learning, and related fields. Research Interests: Professor Cuzzolin is a global leader in uncertainty theory, belief functions, and geometric approaches to probability. His work bridges AI with healthcare, robotics, and autonomous systems. Recent projects include the SARAS surgical robotics project, Epistemic AI, and continual learning frameworks. His lab, VAIL, is a leading group in action detection and neurosymbolic AI. Awards and Recognition: Awards include the IMechE Formula Student AI competitions (2021, 2020), W&B Best Library Award (2021), and the Next 10 Award (2012). His students and collaborators have also achieved notable recognitions, such as BMVC Best Reviewer Awards and dataset development accolades. Grants and Funding: Key funded projects include the H2020 Epistemic AI (£966k), SARAS (£4.3M), and the AIDA incubator (£1.25M). Current lab funding exceeds £3.2M, supporting nine active projects across Horizon 2020, Innovate UK, and industry partnerships. Labs and Collaborations: VAIL collaborates with top institutions (Oxford, Cambridge, Imperial College) and companies (Samsung, Huawei). Projects span autonomous driving datasets (ROAD), surgical AI, and continual learning libraries (Avalanche).

Camelia D. BrumarView profile

Researcher

• Visual Analytics
• Data Science
• Human-Computer Interaction
• +9 more

Camelia D. Brumar is a PhD Candidate in Computer Science at Tufts University and a Visiting PhD Student at Harvard University's Visual Computing Group. She co-founded Boston Vis , a collaborative network for visualization researchers in the Greater Boston Area. Education: B.S. in Theoretical Mathematics from University of Maryland, College Park Research Focus: Systematic visualization design for decision-making processes, bridging gaps between problem spaces and design spaces through qualitative methods Her work intersects Visual Analytics , Human-Computer Interaction , and Machine Learning , with recent publications on decision-making taxonomies, dimensionality reduction explanations, and knowledge graph visualization. Key trends include: Interactive predicate logic for pattern explanation Domain expert challenges in automated data science Anomaly reasoning frameworks Medical AI applications for embryo grading Scientific Achievements: Organizer of Boston Vis (2024) Tutorial presenter on LLMs for research paper interaction (2024) IEEE Visualization 2024 Doctoral Colloquium participant Contributor to Dagstuhl Seminar on provenance in automated data science (2023) Industry experience includes roles at Tableau Research , Alife Health , and Bose Corporation , with collaborations spanning MIT Lincoln Laboratory, National Renewable Energy Laboratory, and Worcester Polytechnic Institute.

Marty ChamberlainView profile

Lecturer

• Criminology
• Socio-Legal Studies
• Medical Regulation
• +7 more

Dr. Marty Chamberlain is a Lecturer in Criminology at the University of Stirling's Faculty of Social Sciences. His research focuses on professional regulation, medical governance, and the sociological implications of risk within healthcare systems. He has held academic roles at multiple UK universities including Liverpool, Nottingham, and Southampton, and currently leads research in digital justice and automated regulatory systems. His work critiques tribunal processes, particularly their impact on vulnerable populations and procedural fairness. Key affiliations include the British Society for Criminology, Scottish Centre for Crime and Justice Research, and ESRC Peer Review College. He chairs the Virtual Trials National Project Board and advises Edinburgh Women’s Aid on digital court systems. His research explores post-COVID court reforms, AI-driven regulatory tools, and the socio-legal challenges of professional accountability. Chamberlain advocates for quantitative skills education and has pioneered visual learning strategies to reduce statistical anxiety in social sciences. His recent projects examine virtual domestic abuse courts and the ethical use of predictive analytics in healthcare regulation. He holds a doctorate analyzing NHS regulatory failures and has published widely on medical practitioners' disciplinary hearings. Research Themes: Professional regulation, medical ethics, digital justice, criminological theory Grants: ESRC-funded research on data visualization in education (2012-2014) Labs/Groups: Centre for Policy, Conflict and Co-operation (CPCCR); Research into Information, Surveillance and Privacy (CRISP)

Ahmet TekalpView profile

Professor

• Digital Image Processing
• Video Compression
• Motion-Compensated Video Filtering
• +5 more

Ahmet Tekalp is a Professor in the Department of Electrical and Computer Engineering at Koc University's College of Engineering since 2001. He holds dual citizenship in Turkey and the USA, with prior academic roles at the University of Rochester (1986-2005) and research positions at Eastman Kodak (1984-1987) and Rensselaer Polytechnic Institute (1981-1984). He chairs the Electronics and Informatics Group at TUBITAK since 2004 as a part-time position. B.S. (1980) in Electrical Engineering & Mathematics, Bogaziçi University M.S. (1982) and Ph.D. (1984) in Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute His research focuses on digital image and video processing, including video compression, motion-compensated filtering for high-resolution applications, video segmentation, object tracking, content-based video analysis, multi-camera surveillance processing, and digital content protection. He has led numerous European and U.S. grants, including FP7 STREP projects and NSF awards, emphasizing applications in sensor networks, visual databases, and medical imaging. His scholarly work spans diverse areas such as superresolution reconstruction, head gesture animation, 3DTV streaming, and reversible data hiding. He has played pivotal roles in editorial boards, including serving as Editor-in-Chief of Signal Processing: Image Communication, and has contributed to major standards bodies like ISO MPEG and ANSI NCITS. Member, Turkish Academy of Sciences (TUBA) Fellow, IEEE Fulbright Senior Scholarship (1999) TUBITAK Science Award (2004) IEEE Signal Processing Society Distinguished Lecturer (1998) He has led multiple international research collaborations and projects, including European FP6/FP7 networks and NATO programs, with substantial grant funding from NSF, NYSTAR, and industry partners like Eastman Kodak, Xerox, and Siemens.

• 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.

Helena MentisView profile

Professor

• Human-Computer Interaction (HCI)
• Computer Supported Cooperative Work (CSCW)
• Health Informatics
• +7 more

Helena Mentis is a Professor in the Department of Information Systems and Affiliate Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). She directs the Bodies in Motion Lab (BiMLab) and is a core member of the Human Centered Computing graduate program and the Interactive Systems Research Center. Her work focuses on sociotechnical systems in healthcare, including telemedicine, surgical collaboration, and technologies for older adults with cognitive impairments. Mentis holds a PhD from Penn State, an MS from Cornell, and a BS from Virginia Tech. Her research spans human-computer interaction (HCI) , computer supported cooperative work (CSCW) , and health informatics . Key projects include developing telemedicine systems for surgical training, sensor-based systems for Parkinson’s patients, and online safety tools for older adults with mild cognitive impairment. She has received significant NSF funding, including a $518k CAREER grant for surgical telementoring systems and $499k for cybersecurity research with older adults. Her articles analyze topics like the effectiveness of telementoring in surgery, ethical design of medical systems, and collaborative security settings for couples facing cognitive challenges. Mentis has pioneered frameworks for responsible computing education and peer-led ethics training in computing curricula. Prior to UMBC, she held postdoctoral roles at Harvard Medical School, Microsoft Research, and the University of Cambridge. Current initiatives include expanding healthcare workforce access via telemedicine and designing inclusive systems that address marginalized groups in digital health. Her work emphasizes participatory methods, ensuring often-overlooked stakeholders (e.g., trainee surgeons, motor disability patients) shape technological solutions.

Iman NematollahiView profile

Research Fellow

• Robotics
• Robot Learning
• Reinforcement Learning
• +5 more

Iman Nematollahi is a PostDoc in Robot Learning at the University of Freiburg under Prof. Dr. Abhinav Valada, having completed his PhD under Prof. Dr. Wolfram Burgard. He holds a MSc in Embedded Systems from the University of Freiburg and a BSc in Electrical Engineering from Shahid Beheshti University. His research focuses on robot learning, world models, and reinforcement learning, particularly in enabling robots to understand physics through world models and adapt skills in unstructured environments. Education: PostDoc in Robot Learning, University of Freiburg (2025–Present) PhD in Robot Learning, University of Freiburg (2019–2024) MSc in Embedded Systems, University of Freiburg (2015–2018) BSc in Electrical Engineering, Shahid Beheshti University (2010–2015) Research Interests: Robot manipulation, world models, reinforcement learning, computer vision, and self-supervised learning. His work emphasizes intuitive physics understanding, skill generalization, and sample-efficient policy improvement. Key Articles: Recent work includes LUMOS (language-conditioned imitation learning), Bayesian optimization for policy refinement, and 3D video prediction (T3VIP). These contributions bridge theory and real-world robotic applications, emphasizing long-horizon tasks and cross-environment adaptation. Awards & Grants: No explicit awards mentioned. His research has been supported through projects like OML (Organic Machine Learning). Teaching: Taught Deep Learning Lab (2020–2022) and Introduction to Mobile Robotics (2019).

Cynthia H. McColloughView profile

Professor

• Computed Tomography (CT) Imaging Technology
• Radiation Dose Management
• Material Composition Evaluation via CT
• +3 more

Cynthia H. McCollough, Ph.D., is a Professor of Biomedical Engineering and Medical Physics at Mayo Clinic, where she serves as a Consultant in the Department of Radiology. She leads the CT Clinical Innovation Center, focusing on advancing CT imaging technology and clinical applications. Her work emphasizes radiation dose reduction, quantitative material composition analysis (e.g., kidney stones, gout), and optimizing CT protocols for diagnostic accuracy while minimizing patient risk. Dr. McCollough holds leadership roles in national and international organizations, including the NIH Biomedical Imaging Technology Study Section and the American Association of Physicists in Medicine (AAPM). She has received numerous awards, including the Distinguished Investigator Award from Mayo Clinic and the William D. Coolidge Gold Medal from AAPM. Her research spans photon-counting CT, dual-energy CT, and translational imaging solutions for vascular and abdominal diseases. Dr. McCollough’s academic contributions include over 600 peer-reviewed publications and leadership in grant-funded projects such as the National Institute of Biomedical Imaging and Bioengineering initiatives. Education: PhD in Medical Physics from the University of Wisconsin-Madison, MS in Medical Physics from the University of Wisconsin-Madison, BS in Physics from Hope College. Research Highlights: Her lab develops tools for radiation dose estimation, non-invasive material characterization via CT, and optimizing CT parameters using human and model observers. Recent work includes improving kidney stone analysis, bone mineral density measurements using photon-counting CT, and reducing radiation exposure in pediatric imaging. Grants & Funding: Principal investigator on multiple NIH grants, including projects on coronary atherosclerosis characterization, urinary stone composition analysis, and photon-counting CT for vascular disease detection. Awards: Over 100 awards, including the Distinguished Mayo Clinic Investigator Award (2020), Honorary Doctorate from Linkoping University (2020), and AAPM’s highest honor, the William D. Coolidge Gold Medal (2024). Labs & Teams: Directs the CT Clinical Innovation Center and the X-Ray Imaging Research Core at Mayo Clinic, collaborating with multidisciplinary teams across radiology, biomedical engineering, and physics.

Camille BerthelotView profile

Researcher

• Comparative Functional Genomics
• Evolutionary Genomics
• Gene Regulation
• +12 more

Camille Berthelot is a Principal Investigator and head of the Comparative Functional Genomics research group at the Institut Pasteur in Paris, France. Her work lies at the intersection of evolutionary biology, genomics, and reproductive physiology, with a focus on understanding how genomic changes drive evolutionary innovations in vertebrates, particularly the evolution of menstruation in primates. Her research leverages cutting-edge technologies including single-cell RNA sequencing, organoid models, and computational frameworks to compare gene expression and regulation across species such as humans, macaques, and mice. She investigates how evolutionary changes in gene function contribute to novel traits and how these relate to diseases like endometriosis. A major focus is the use of menstrual fluid as a non-invasive biomarker source for endometriosis, a direction highlighted in her systematic review published in Reproductive Biology and Endocrinology . Her recent publications span high-impact journals such as Science , Genome Research , and Nature Ecology & Evolution , covering topics from teleost fish phylogeny to regulatory evolution in mole-rats. These works reveal a consistent trend in using comparative genomics to decode deep evolutionary questions, often in collaboration with institutions like QMUL and INSERM. ERC Starting Grant (EVOMENS) – Decoding the genetics of menstruation evolution in primates Collaborative project on hypoxia adaptation in mole-rats Development of bioinformatics tools like FINSURF for non-coding variant classification She actively mentors a team of postdocs, PhD students, and master’s interns, including Claire Lavergne, Leo Zeitler, and Eulalie Liorzou. Former members such as Malgorzata Gazda and Axelle Brulport have gone on to independent academic positions, reflecting her role in training next-generation scientists. Camille Berthelot returned from maternity leave in June 2024 and remains actively engaged in research, publication, and team leadership at the Institut Pasteur.

• Medical Informatics
• Clinical Practice Guidelines
• Computerized Healthcare Systems
• +3 more

Katharina Kaiser is affiliated with TU Wien's Fachbereich Software Services. She specializes in medical informatics with a focus on computerized clinical guidelines and healthcare system optimization. Her work bridges temporal data analysis, information extraction from clinical texts, and workflow modeling in medical contexts. Key areas: Clinical decision support systems, guideline implementation frameworks, temporal logic in healthcare processes Notable contributions: Development of TimeML-based clinical guideline modeling, heuristic methods for condition-action sentence identification Her research emphasizes semantic enrichment of medical documents and interactive visualization tools for therapy planning and patient data correlation. Collaborations include the PROTOCOL project and ReMINE deliverables in adverse risk management.

Sam S. RamanujanView profile

Professor

• Big Data Architecture
• Visual Analytics
• Software Engineering
• +4 more

Dr. Sam S. Ramanujan is Professor of Computer Information Systems and Analytics at the University of Central Missouri , affiliated with the Harmon College of Business and Professional Studies . He teaches advanced object-oriented programming and software engineering courses, combining over two decades of academic expertise with substantial industry experience in complex system deployment. Doctor of Philosophy in Information Systems (University of Houston, 1995) MBA in CIS and Quantitative Analysis (University of Arkansas, 1989) PGDM in Information Systems (XLRI Institute of Management Studies, 1987) Bachelor of Arts (Hons) in Economics (University of Delhi, 1985) His research spans big data architecture , visual analytics , healthcare IT , and legal aspects of technology . He has published extensively on topics including software maintenance, e-commerce trust models, and cloud-based healthcare systems, with a focus on bridging technical and legal challenges in digital environments. Dr. Ramanujan's academic work shows a consistent focus on software engineering (1995–2017), healthcare IT (2004–2017), and legal-compliance frameworks (2000–2017). His publications demonstrate interdisciplinary expertise in merging technical systems with regulatory requirements . Best Paper Award , Journal of American Academy of Business, Cambridge (2006) He has contributed to pedagogical advancements in distributed computing curricula and collaborates with scholars like S. Kesh and S. Nerur. His industry experience informs real-world applications of his research in software maintenance and offshore operations.