Find Academic Supervisors

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +3 more

Alexander Ilin is a Visiting Professor and part-time teacher in the Department of Computer Science at Aalto University, specializing in Artificial Intelligence and Machine Learning. He holds roles in both the Computer Science - Artificial Intelligence and Machine Learning (AIML) research area and the Professors of Practice group. His research focuses on Machine Learning, Reinforcement Learning, and Deep Learning applications, with contributions to areas like neural networks, generative models, and healthcare informatics. Dr. Ilin earned a Doctoral degree in Engineering and Technology from Helsinki University of Technology in 2006. His work aligns with UN Sustainable Development Goals, particularly in advancing education and innovation. Key projects include the Finnish Center for Artificial Intelligence (FCAI) and the APPIA project on privacy-aware AI applications. His research explores cutting-edge topics such as diffusion models for dynamical systems, reinforcement learning for robotics, and self-supervised forecasting in healthcare. He has led projects like APPIA (2020–2021) and FCAI (2020–2026), securing grants from the Academy of Finland and Business Finland. His work bridges theory and practice, with applications in autonomous systems, nanotechnology, and medical diagnostics. Notable activities include visiting research at the UK Met Office Hadley Centre and presentations at top conferences like NeurIPS and AAMAS. His lab focuses on scalable AI solutions for real-world challenges, emphasizing ethical AI and privacy-preserving techniques.

• Visual and Cognitive Computing
• Machine Learning
• Social Robotics
• +6 more

Professor Hui Yu is a Professor of Visual and Cognitive Computing at the University of Glasgow's School of Psychology & Neuroscience. He leads the Visual Computing and Social Robotics Group (VCSR) within the cSCAN research center. His research focuses on machine learning, social robotics, and 3D/4D facial avatar technologies. Professor Yu holds editorial roles for journals including Neurocomputing and IEEE Transactions on Human-Machine Systems. He has secured grants from EPSRC, EU Horizon, and the Royal Society. Notable awards include the Royal Academy of Engineering Industrial Fellowship and inclusion in Stanford's global top 2% cited scholars list. His work bridges theoretical frameworks with practical applications in healthcare, autonomous vehicles, and human-machine interaction. Ongoing projects include the EPSRC-funded ACMod initiative exploring affective computing and real-time wave imaging for maritime safety. Yu actively supervises PhD students in visual computing and social robotics, with current openings in 3D vision and social signal analysis. Grants: EPSRC ACMod (2024-2028), EPSRC Seakeeping (2025-2027), Royal Society Newton Fellowship Lab Affiliation: VCSR Group at cSCAN Professional Roles: IEEE SMC Associate Vice President, Chair of IEEE Social Computing Technical Committee

Souyoung JinView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Cognitive Science
• +3 more

Souyoung Jin is an Assistant Professor in the Department of Computer Science at Dartmouth College. Her research focuses on Computer Vision, Machine Learning, and Cognitive Science with expertise in video understanding. She earned her B.S. from Dongguk University, M.S. from KAIST, Ph.D. from UMass Amherst, and completed a postdoc at MIT's CSAIL under Aude Oliva. She teaches courses like Machine Learning and Deep Learning, and leads research on AI systems mimicking human perception. Her lab explores multimodal tasks and synthetic data applications. She is an Area Chair for WACV 2025 and actively recruits motivated students for her lab. Education: B.S., Dongguk University M.S., Korea Advanced Institute of Science and Technology Ph.D., University of Massachusetts Amherst Postdoc, MIT Research Interests: Dr. Jin develops AI systems to understand human experiences through video analysis. Her work spans video understanding, synthetic data applications, and cognitive-inspired models. She emphasizes temporal context in low-resource regimes and cross-modal learning. Recent projects include BOLD Moments (fMRI-video datasets) and LangNav (language-based navigation). Grants & Labs: Her research is supported by grants focusing on synthetic video representations and multimodal fusion. She leads Dartmouth's Video Understanding Lab, fostering interdisciplinary collaborations between computer vision and cognitive science.

Adithya K PediredlaView profile

Assistant Professor

• computational photography
• computer graphics
• acousto-optics
• +8 more

Adithya K Pediredla is an Assistant Professor in the Department of Computer Science at Dartmouth College. His research focuses on computational imaging systems that co-design hardware and software to enhance imaging capabilities. He holds a B.Tech from the National Institute of Technology, Warangal (India), an M.E. from the Indian Institute of Science (Bangalore), a Ph.D. from Rice University, and completed a postdoctoral fellowship at Carnegie Mellon University. Educational Background: B.Tech, National Institute of Technology, Warangal, India M.E., Indian Institute of Science, Bangalore, India Ph.D., Rice University Postdoctoral Researcher, Carnegie Mellon University His research interests include computational photography, computer graphics, acousto-optics, and computer vision. He explores adaptive sensing, depth imaging, computational microscopy, and imaging in non-line-of-sight scenarios. His work also involves physically-based rendering and inverse rendering techniques for advanced computational imaging applications. No scientific awards or grants are explicitly listed in the provided information. He advises no students (PhD/Master’s) as per the data, and no lab affiliations are specified beyond his personal website.

Anjan DuttaView profile

Senior Lecturer

• Computer Vision
• Machine Learning
• Deep Learning
• +4 more

Dr. Anjan Dutta is a Senior Lecturer in Artificial Intelligence at the University of Surrey, UK. He holds a PhD in Computer Science from the Autonomous University of Barcelona (UAB), awarded with Excellent Cum Laude and the Extraordinary PhD Thesis Award (2013-14). His research focuses on computer vision and machine learning, particularly deep multi-modal embedding, zero-shot learning, and graph neural networks. Education: PhD in Computer Science, UAB (2014) MSc in Computer Vision & AI, UAB (2010) MCA, Maulana Abul Kalam Azad University of Technology (2009) BSc Mathematics (Honours), University of Calcutta (2006) Research Interests: Deep Learning for Vision Tasks Zero-Shot and Few-Shot Learning Graph Neural Networks Multi-modal Embedding Techniques Structured Representation Learning Recent Research Trends: His work emphasizes scalable and interpretable AI systems, with notable contributions in object counting, bias reduction in neural networks, and sketch-based retrieval. Publications span top-tier venues, reflecting interdisciplinary innovation in vision and learning. Awards & Honors: Extraordinary PhD Thesis Award (UAB, 2013-14) Excellent Cum Laude PhD Award (UAB, 2014) Labs & Affiliations: Active in the Surrey Institute for People-Centred AI (PAI) and the Centre for Vision, Speech and Signal Processing (CVSSP), contributing to cross-disciplinary AI research.

• Digital Watermarking
• Neural Representation
• Copyright Protection
• +5 more

Renjie Wan is an Assistant Professor in the Department of Computer Science at Hong Kong Baptist University. He holds a BEng from the University of Electronic Science and Technology of China (2012) and a Ph.D. from Nanyang Technological University (2019). His research focuses on digital watermarking, neural representation, copyright protection, and AI security, with notable contributions in computational photography and image processing. He has received awards including the Microsoft CRSF Award and VCIP 2020 Best Paper Award. His academic journey includes a Wallenberg-NTU Presidential Postdoctoral Fellowship (2020–2022). He currently advises Ph.D. students in areas like neural radiance field watermarking and backdoor attack mitigation. Teaching responsibilities include courses on AI application development and Python for data analysis. Research Projects: Developing multispectral imaging algorithms for coastal surveillance Dynamic environment reflection separation techniques Face image reflection removal using neural radiance fields Awards: Meritorious Winner at Mathematical Contest in Modeling 2023 Silver Medal of 50th International Exhibition of Inventions Geneva His lab focuses on advancing AI security and 3D content protection through innovative techniques like neural radiance field watermarking and adversarial defense mechanisms.

Jochen LangView profile

Professor

• Computer Graphics
• Computer Vision
• Computational Photography
• +3 more

Professor Jochen Lang is a faculty member at the University of Ottawa's School of Electrical Engineering and Computer Science. He holds a Ph.D. from the University of British Columbia (2001) and M.Sc. from York University (Toronto), along with engineering degrees from Germany. Previously, he was a researcher at the Max-Planck-Institut für Informatik (2002–2004). His research focuses on measurement-based modeling in visual computing, integrating computer graphics, computer vision, and robotics. Notable projects include the IRIS NCE-funded 'Reality-based Modeling and Simulation' and EU-funded 'Virtual Heritage' initiatives. Key research areas include Computer Graphics , Computer Vision , and Computational Photography . His work addresses challenges like optical flow estimation, 3D modeling, medical imaging (e.g., SPECT analysis), and haptic rendering. He has contributed to advancements in video object segmentation, texture synthesis, and deep learning applications in medical diagnostics. His recent publications (2023–2025) explore topics such as optical flow under low visibility, medical image processing, and AI-driven defect detection in scintigraphy. His work often emphasizes practical applications in healthcare, robotics, and immersive technologies like virtual reality. Funding sources include EU grants and industry partnerships, reflecting his interdisciplinary impact.

Richard MarksView profile

Professor

• AR/VR
• Machine Learning
• Context-Aware Systems
• +3 more

Richard Marks is a Professor of the Practice at the University of North Carolina at Chapel Hill, holding dual appointments in the School of Data Science and Society and the Department of Computer Science. His research focuses on AR/VR, machine learning, and context-aware systems. He previously led the PlayStation Magic Lab at Sony PlayStation, contributing to innovations like the EyeToy, PlayStation Eye, Move, and VR systems. Marks has over 100 patents and earned a PhD in Computer Science from Stanford University (1996) and a BS in Avionics from MIT (1990). Education: PhD in Computer Science, Stanford University, 1996 BS in Avionics, MIT, 1990 Research Interests: Marks explores immersive technologies including AR/VR interfaces, machine learning applications for interactive systems, and context-aware computing. His work emphasizes user-centered design principles in virtual and augmented environments. Recent Publications/Trends: Recent work focuses on spatial computing, gesture-based VR navigation, and multi-user VR environments. Key themes include improving tracking accuracy, optimizing HMD interactions, and integrating voice/gesture systems for natural user interfaces. Lab/Affiliations: Formerly director of PlayStation's Magic Lab and Google ATAP, he now leads UNC's research on next-generation interactive systems. His patents reflect innovations in controller design, tracking systems, and ambient computing interfaces.

Yongxin YangView profile

Lecturer

• Machine Learning
• Computational Finance
• Medical Genetics

Dr. Yongxin Yang is a Lecturer in Financial Technology at Queen Mary University of London (QMUL), affiliated with the School of Electronic Engineering and Computer Science and the Centre for Multimodal AI. He holds a PhD from QMUL (2017), supervised by Prof. Timothy Hospedales. His research focuses on machine learning (transfer learning, domain adaptation) and its applications in finance (derivatives pricing, portfolio optimization) and medical genetics. Prior to his current role, he was a Lecturer in Machine Learning at the University of Surrey. He is an ACCA-certified accountant and professional web designer, with industry experience through co-founded ventures and consulting roles. Research interests include: Machine Learning, Computational Finance, and Medical Genetics. Notable work spans medical imaging fairness (e.g., MEDFAIR), option pricing via neural networks, and domain generalization techniques like MixStyle. His contributions bridge theoretical machine learning with practical applications in finance and healthcare. He has authored numerous publications in top venues (NeurIPS, ICML, CVPR, ICLR) and collaborates with industry partners. His work emphasizes interdisciplinary approaches, combining finance, genetics, and AI to solve real-world challenges.

Shanxin YuanView profile

Lecturer

• 3D Computer Vision
• Reconstruction and Rendering
• Multimodal Learning
• +5 more

Dr. Shanxin Yuan is a Lecturer in Digital Environment at the School of Electronic Engineering and Computer Science, Queen Mary University of London. Previously, he worked as a Senior Research Scientist at Huawei Noah’s Ark Lab, London. His research focuses on computer vision and machine learning, particularly 3D digital humans, computational photography, and multimodal learning. Key research interests include hand pose estimation, neural rendering, and image restoration techniques such as super-resolution and HDR imaging. Research Interests: 3D Computer Vision, Reconstruction, and Rendering Neural Rendering for Deformable Objects Low-level Vision and Image Restoration Multimodal Learning and Vision-Language Integration Applications in Immersive Gaming, Music Understanding, and Fashion AI Recent Research Trends: Recent publications emphasize advancements in 3D hand modeling, neural radiance fields, and adaptive image restoration techniques. Work on motion retargeting and spatial-temporal modeling reflects growing focus on dynamic human digitization and realistic rendering. Labs & Affiliations: Active in the Digital Environment Research Institute (DERI) and the Centre for Multimodal AI , collaborating on projects that bridge computer vision with real-world applications.

Greg SlabaughView profile

Professor

• Computer Vision
• Deep Learning
• Computational Photography
• +4 more

Professor Greg Slabaugh is the Director of the Digital Environment Research Institute (DERI) at Queen Mary University of London and holds a Professorship in Computer Vision and AI. He earned a PhD in Electrical Engineering from Georgia Institute of Technology, focusing on 3D shape reconstruction from 2D images. Prior academic roles include six years at City, University of London, where he taught computer vision, graphics, and programming while leading research grants funded by the EU Commission, EPSRC, and Innovate UK. Industrial experience includes roles at Huawei (Chief Scientist in Computer Vision), Medicsight (medical imaging for cancer detection), and Siemens (medical image computing). His research interests span computer vision, deep learning, computational photography, and medical image computing. Key achievements include 36 granted patents and over 150 publications. Awards include a 2017 Research Student Supervision Award and a 2016 Teaching Award. He also serves as the Alan Turing Institute's Turing (Academic) Liaison for Queen Mary. Current research projects include collaborations on digital twins for healthcare, AI-driven cardiac modelling, and multimodal AI applications. His work bridges academia and industry, emphasizing practical applications in health tech and imaging.

Simon HadfieldView profile

Associate Professor

• Computer Vision
• Robotics
• Machine Learning
• +3 more

Dr Simon Hadfield is an Associate Professor (Reader) in Robot Vision and Autonomous Systems at the University of Surrey, affiliated with the Centre for Vision, Speech and Signal Processing (CVSSP) within the School of Computer Science and Electronic Engineering. His research focuses on advancing computer vision and machine learning techniques for real-world applications, particularly in robotics. He holds a PhD and certifications such as FHEA (Fellow of the Higher Education Academy) and AUS (Advanced University Skills). Research Interests: - Effective exploitation of novel visual sensors - Practical application of computer vision to robotics - Efficient dynamic reconstruction techniques - Real-time robotics applications - Sensor fusion and calibration systems Recent projects include the NIMROD initiative exploring analogue visual sensors and contributions to multimillion-pound research efforts in sustainable packaging and agricultural robotics. His work emphasizes bridging the gap between lab-based techniques and real-world deployment, with publications in top-tier conferences and journals. Notable achievements include developing paradigms for low-complexity 3D reconstruction and proposing methods for depth estimation and localization. His research has been funded by grants such as a £1m award for robotic applications in farming. Collaborations and lab affiliations include CVSSP, where he leads projects at the intersection of vision, robotics, and autonomous systems. Future work continues to focus on scalable vision techniques and ethical AI practices in representation learning.

John KeyserView profile

Professor

• Computer Science
• Graphics and visualization
• Physically-based simulation
• +5 more

John Keyser is a Professor in the Department of Computer Science & Engineering at Texas A&M University, serving as Graduate Advisor. He holds a Ph.D. from the University of North Carolina at Chapel Hill and multiple B.S. degrees from Abilene Christian University. His research focuses on geometric computing, physically-based simulation, graphics, and modeling, with notable contributions to 3D visualization and computational geometry. Keyser's educational background includes advanced degrees in Computer Science, Applied Mathematics, and Engineering Physics. He has received prestigious awards such as the William Keeler Faculty Fellowship and teaching accolades from the Association of Former Students and Tenneco. His research interests span robust geometric modeling, physically-based simulation, brain networks, and graphics. Keyser's work bridges theoretical foundations with applied systems, including innovations in particle-based fluids, mesh rendering, and filament tracking in microscopy. Awards: William Keeler Faculty Fellow (2009-2010), Teaching Excellence Awards (2007), Montague Scholar (2003-2004). Advising: Graduate Advisor role with expertise in guiding students through complex computational projects. Keyser collaborates on advanced imaging techniques like the Knife-Edge Scanning Microscope (KESM) for brain tissue analysis and contributes to GPU-accelerated algorithms for real-time simulations. His research trends emphasize interdisciplinary applications of geometric computing in both academic and industrial contexts.

Pierre PoulinView profile

Professor

• Computer Graphics
• Realism
• Image Synthesis
• +6 more

Pierre Poulin is a Full Professor in the Department of Computer Science and Operations Research at the Université de Montréal. He leads the LIGUM laboratory and has held roles such as Department Chair (2017–2021) and PREDOC responsible (2023). His research focuses on computer graphics, realism, and image synthesis, with expertise in global illumination, procedural modeling, and real-time rendering. Poulin holds a Ph.D. from the University of British Columbia (1993), M.Sc. from the University of Toronto (1989), and B.Sc. from Université Laval (1986). His research spans natural phenomena simulation, fluid dynamics, and interactive visualization. Notable awards include the CHCCS Achievement Award (2021) and Fellow of Eurographics France (2022). He has supervised over 50 students, contributing to advancements in rendering algorithms and software visualization. Key grants include funding from CRSNG and FRQNT for projects on procedural scene creation and fluid simulation. His work bridges academia and industry, with collaborations in animation, visualization, and software engineering.

• Computer Vision
• Medical Imaging
• Generative Models
• +4 more

Guha Balakrishnan is an Assistant Professor in the Electrical and Computer Engineering (ECE) department at Rice University, affiliated with the Digital Health Initiative. His research focuses on computer vision, medical imaging, and algorithmic fairness. He holds a BS from the University of Michigan, and MS and PhD from MIT's CSAIL, followed by a postdoc at MIT and industry experience at Amazon Web Services. Education: BS in Computer Science Engineering & Computer Engineering, University of Michigan (2011) MS in EECS, MIT (2013) PhD in EECS, MIT (2018) Postdoc: MIT (2018-2020) Applied Scientist: Amazon Web Services (2020-2021) Research Interests: His work spans generative models for visual data, medical applications (e.g., CT reconstruction, vital sign measurement), geospatial imaging, and ethical AI systems. He emphasizes fairness, accountability, and responsible deployment of vision systems. Recent projects include ALPINE (PyTorch library for implicit neural representations) and the EMACS workshop at CVPR 2025. Industry Impact: Prior experience at AWS focused on fairness in AI systems. Active in developing tools like Voxelmorph for medical image registration and frameworks like GELDA for dataset bias analysis. Labs & Teams: Leads efforts in Rice's Digital Health Initiative, focusing on low-cost medical AI solutions and cross-disciplinary collaborations.