Find Academic Supervisors

Gim Hee LeeView profile

Associate Professor

• Computer Vision
• Robotic Perception
• Machine Learning
• +3 more

Gim Hee Lee is an Associate Professor at the Department of Computer Science, National University of Singapore (NUS) School of Computing. He heads the Computer Vision and Robotic Perception (CVRP) Laboratory and holds affiliations with the NUS Graduate School for Integrative Sciences and Engineering (NGS) and the NUS Institute of Data Science (IDS). Previously, he was a researcher at Mitsubishi Electric Research Laboratories (MERL) in the USA and worked at DSO National Laboratories in Singapore. Education Dr.sc. in Computer Science from ETH Zurich M.Eng. in Mechanical Engineering from National University of Singapore B.Eng. in Mechanical Engineering (1st Class Honors) from National University of Singapore Research Interests Professor Lee's research focuses on Computer Vision , Robotic Perception , and Machine Learning , with specialization in dynamic 3D scene reconstruction, event camera applications, and cross-view geo-localization. His work bridges theoretical advances with real-world applications in autonomous systems and augmented reality, particularly through neural scene representations and geometric computer vision techniques. Scientific Awards Faculty Teaching Excellence Award (AY 2018/19 and AY 2017/18) CVPR 2014 Doctoral Consortium Travel Award Finalist for IROS 2012 Best Paper Award Finalist for IROS 2012 Best Video Award Advising and Service Professor Lee serves as Associate Editor for IJCV and has held Area Chair positions for major conferences including CVPR, ICCV, ECCV, and NeurIPS. He chaired Program Committees for 3DV 2022 and served as Demo Chair for CVPR 2023. His research on the Cross-View Matching Network (CVM-Net) demonstrated real-time ground-to-aerial geo-localization in moving vehicles, establishing a benchmark for subsequent research in this field. Laboratories He leads the Computer Vision and Robotic Perception (CVRP) Laboratory at NUS, which develops cutting-edge solutions for 3D scene understanding, event-based vision, and robotic navigation. The lab's work includes acquiring high-quality datasets for dynamic scene reconstruction and advancing neural representations for real-world applications.

• Computer Vision
• 3D Reconstruction
• SLAM
• +5 more

Victor Adrian Prisacariu is an Associate Professor of Engineering Science at the University of Oxford. He holds a BSc from Gheorghe Asachi Technical University, Iasi, Romania, and a DPhil from Oxford. Previously, he served as an EPSRC Postdoctoral Researcher and Dyson Senior Research Fellow. His current research focuses on real-time computer vision techniques for AR/VR on mobile platforms, with emphasis on semantic visual tracking, 3D reconstruction, and SLAM. He co-founded 6D.ai (acquired by Niantic), developing crowdsourced 3D mapping technologies. He leads the Active Vision Lab, which explores computational vision applications in robotics, wearable computing, and navigation. His work bridges academia and industry, emphasizing scalable solutions for real-world AR/VR challenges. Education : BSc (First Class Honors) in Computer Engineering, Gheorghe Asachi Technical University (2008) DPhil in Engineering Science, University of Oxford (2012) Research Interests : AR/VR system optimization for mobile and wearable devices Real-time 3D reconstruction and semantic mapping SLAM algorithms for navigation and localization Computer vision techniques for scene understanding and object tracking Industry-academia collaboration in AR/VR technologies Key Projects : Active Vision Lab Code Repository (AVLCode) 6D.ai SDK (Niantic's real-time 3D semantic mapping) Development of Gaussian splatting techniques for efficient 3D rendering Awards & Recognition : Contributions acknowledged in CVPR 2025 Best Paper Award (group recognition) Lab & Teams : The Active Vision Lab collaborates on projects like 3D reconstruction, visual localization, and AR system development. His work integrates academic rigor with industry-driven applications through partnerships like Niantic.

Juho RousuView profile

Professor

• Machine Learning
• Metabolomics
• Biomedicine
• +6 more

Juho Rousu is a Professor in the Department of Computer Science at Aalto University. His research focuses on developing machine learning methods for computational and data science, with a strong emphasis on kernel methods, structured prediction, and applications in metabolomics, biomedicine, and synthetic biology. He leads efforts in multi-view learning, sparsity-driven models, and optimization techniques for complex data analysis. Key research areas include drug combination prediction, metabolite identification, and biomarker discovery through canonical correlation analysis. His work integrates chemical reaction analysis, graph learning, and deep learning approaches to tackle challenges in systems biology and precision medicine. Recent projects involve scalable methods for drug synergy modeling and computational tools like MassSpecGym for molecular discovery. Rousu’s methodologies often bridge algorithmic innovation with real-world applications, such as improving protein production in biotechnology and optimizing metabolic flux analysis. His contributions span from foundational machine learning theory to practical software tools like CamOptimus and SIRIUS 4. He collaborates across disciplines to address challenges in environmental microbiology, clinical diagnostics, and systems pharmacology. His research has been applied to critical areas like tuberculosis drug efficacy prediction and metabolic network reconstruction, demonstrating impact in both academic and industrial contexts. Despite no listed awards here, his extensive publication record reflects sustained leadership in computational science and its biomedical applications.

Xilu WangView profile

Research Fellow

• Data-driven Optimization
• Artificial Intelligence
• Machine Learning
• +5 more

Dr. Xilu Wang is a Surrey Future Fellow at the University of Surrey, affiliated with the School of Computer Science and Electronic Engineering. They are part of the Nature Inspired Computing and Engineering Research Group and the Surrey Institute for People-Centred Artificial Intelligence (PAI). Their research focuses on data-driven optimization, trustworthy AI, and machine learning, with specialties in multi-objective optimization, Bayesian optimization, evolutionary machine learning, and federated optimization. Dr. Wang holds a PhD and can be contacted via email . Research interests include developing algorithms for fair, privacy-preserving machine learning and optimization. Key topics span neural architecture search, federated optimization, and transfer learning. Dr. Wang has supervised multiple postgraduate students, including Abel Alexander (University of Surrey), Adrian Kruse (Bielefeld University), and Scheu Louis Lamar (Bielefeld University), focusing on Bayesian optimization and federated learning applications. Publications emphasize large-scale optimization, federated systems, and secure evolutionary algorithms. Notable works include surrogate-assisted optimization methods and privacy-preserving frameworks. Their contributions bridge theoretical advancements with practical applications in autonomous driving, SAR imagery, and resource-constrained computing. Dr. Wang’s work has been featured in IEEE Transactions on Evolutionary Computation and other top-tier journals. They actively explore interdisciplinary collaborations, particularly in trustworthy AI and federated learning security. Current projects aim to enhance algorithmic fairness, scalability, and real-world deployment in complex systems.

Jean-Yves GuillemautView profile

Senior Lecturer

• 3D Computer Vision
• 3D Reconstruction
• Computational Photography
• +4 more

Dr. Jean-Yves Guillemaut is a Senior Lecturer in 3D Computer Vision and Postgraduate Research Director at the Centre for Vision, Speech and Signal Processing (CVSSP) within the School of Computer Science and Electronic Engineering at the University of Surrey. His research focuses on 3D reconstruction of complex dynamic scenes, particularly addressing challenges with glossy materials and uncontrolled outdoor environments. He collaborates with industry to apply his work in AR/VR, immersive content production, and free-viewpoint video. Education: He holds an MEng (hons), PhD, and is a member of professional bodies including MIEEE, MBMVA, and FHEA. His technical expertise spans computational photography, lightfield imaging, and artificial intelligence. Current PhD students: Matthew Bailey, Yue Zhang, Gianmarco Addari Laboratory affiliations: CVSSP Research interests include scene modeling from multi-view video, dynamic scene reconstruction, and applications in creative industries. Recent projects involve developing algorithms for illumination estimation, pedestrian tracking, and neural rendering. Grants and collaborations are centered on industry partnerships enhancing creative technology sectors. Future work aims to expand outdoor scene modeling and AI-driven medical imaging applications.

Yuankai QiView profile

Lecturer

• Vision-and-Language for Robot Action
• Video Captioning
• Medical Image Processing
• +8 more

Dr. Yuankai Qi is a Lecturer in Artificial Intelligence at Macquarie University's School of Computing and an Adjunct Lecturer at the Australian Institute for Machine Learning (AIML), University of Adelaide. His research focuses on Vision-and-Language for Robot Action, Video Captioning, Medical Image Processing, and Movie Dubbing. He holds a PhD from Harbin Institute of Technology, specializing in visual object tracking, and has won awards such as the DAVIS 2017 Challenge and CAAI Outstanding Doctoral Dissertation. He has published over 60 papers in top-tier venues like CVPR and NeurIPS, and serves as an Area Chair for NeurIPS, ICCV, and ICLR. His current projects include AI-enhanced CT image analysis and generative AI for banking solutions. Dr. Qi collaborates with the Data Horizons, Smart Green Cities, and Frontier AI Research Centres. He teaches COMP 3420 (AI for Text and Vision) and COMP 2250 (Data Communication). His work spans robotics navigation, medical imaging, and video anomaly detection, recognized globally as a Top 2% Scientist in 2024. Education: PhD (Harbin Institute of Technology, China) Key Awards: ACM Multimedia 2024 Best Paper Award, ICPR 2024 Best Student Paper Award Grants: Projects on AI in healthcare and banking (2024–2028) Labs/Teams: Data Horizons Research Centre, Smart Green Cities Research Centre

• Medical Image Analysis
• Artificial Intelligence
• Planetary Science
• +3 more

Christos Matsoukas is a Researcher and Industry doctoral student at the Division of Computational Science and Technology, KTH Royal Institute of Technology. His work bridges Artificial Intelligence and Planetary Science, focusing on medical image analysis via advanced machine learning techniques and exploring Titan's surface composition using remote sensing data from missions like Cassini. His research interests span Medical Image Analysis, leveraging transformer models and self-supervised learning for diagnostic applications, as well as Planetary Science analyzing Titan’s chemical composition through spectral and morphological analysis of craters and geological features. Recent studies investigate the efficacy of foundation models in low-data medical contexts and domain adaptation strategies for high-content imaging. Publications highlight innovations like Random Token Fusion for multi-view diagnosis, Metadata-guided consistency learning, and compositional mapping of Titan’s surface using Cassini/VIMS and RADAR data. His work contributes to both AI-driven healthcare solutions and understanding Titan’s habitability potential.

Stefan PosladView profile

Senior Lecturer

• Internet of Things (IoT)
• Ubiquitous Computing
• Artificial Intelligence
• +3 more

Dr. Stefan Poslad is a Senior Lecturer in the School of Electronic Engineering and Computer Science at Queen Mary University of London (QMUL), affiliated with the Centre for Multimodal AI and IoT2US Lab. He specializes in IoT, Ubiquitous Computing, and Artificial Intelligence, focusing on smart devices, distributed systems security, and human-centric technologies. His teaching includes modules like Introduction to IoT and Mobile Services, emphasizing hands-on experience with system architectures and middleware. Research interests span IoT applications, pervasive computing, and privacy-preserving technologies. Notable projects include developing localization systems (e.g., ARLO, BEV-Locator) and health monitoring frameworks using UWB and radar sensors. He has contributed to urban mobility studies, environmental sensing, and wearable technologies, such as soft tactile displays using electroactive polymers. Poslad has received recognition as an Alan Turing Fellow and ranks among the top 2% of global scientists. His work integrates AI, IoT, and cybersecurity to address real-world challenges like smart cities and healthcare. He leads the IoT2US Lab, fostering interdisciplinary research on ubiquitous computing and its societal impacts.

Jinglan ZhangView profile

Senior Lecturer

• Artificial Intelligence
• Big Data Analysis
• Computer Human Interaction
• +7 more

Dr. Jinglan Zhang is a Senior Lecturer at Queensland University of Technology (QUT), specializing in Artificial Intelligence and Information Systems. She holds a PhD from QUT and has over 15 years of research experience, focusing on multimedia processing, big data analysis, and human-computer interaction. Her work spans environmental monitoring, health informatics, and web applications. Dr. Zhang has published over 100 refereed papers and secured over $1M in research funding. She actively supervises PhD and Masters students and mentors colleagues through QUT's mentoring program. Education: Bachelor of Engineering (1987) and Master of Engineering (1990), Beijing University of Aeronautics and Astronautics (now Beihang University), China. Master of Computing (1998–1999), Macquarie University, Australia. PhD (2003), Queensland University of Technology. Research Interests: Artificial Intelligence: Visual/Acoustic Information Processing, Machine Learning. Big Data Analysis and Visualization. Computer-Human Interaction, including accessibility for people with disabilities. Software Engineering, Mobile/Web Applications. Environmental Monitoring using Acoustic Sensors. Academic Contributions: Dr. Zhang has led projects in bioacoustics, healthcare AI, and accessibility tools. She organizes international conferences (e.g., IEEE e-Science 2010) and collaborates with institutions in China and Australia. Her recent work explores transfer learning, data scarcity solutions, and AI ethics. Grants & Advising: Over $1M in research grants, including projects on assistive technologies for people with intellectual disabilities. Supervised 10 PhD and 2 Masters students to completion. Current mentor for 3 PhD students and collaborator on interdisciplinary teams. Labs & Teams: Core member of QUT's Centre for Data Science, leading the Environment and Natural Systems research theme. Active in collaborative initiatives with Chinese universities and research institutions.

Khanh LuongView profile

Research Fellow

• Machine Learning
• Data Mining
• Multi-Aspect Data Clustering
• +7 more

Dr. Khanh Luong is a Research Fellow at the School of Communication within the Faculty of Creative Industries, Education & Social Justice at Queensland University of Technology (QUT). She holds a PhD in Computer Science specializing in Data Science (2019) from QUT. Her research focuses on multi-aspect data, addressing challenges in high-dimensional, sparse datasets through innovative machine learning and data mining methods. She contributes to real-world applications in diverse domains like text, image, and bioinformatics data. Teaching roles include tutoring in Discrete Mathematics, Programming Principles, and Database Management for undergraduate/postgraduate students. She also designed the online unit 'Introduction to Programming' for the Master of IT program. Research interests span machine learning, data mining, multi-aspect data clustering, dimensionality reduction, and unsupervised learning. She works on the Applied Data Science project at QUT's Centre for Data Science, developing solutions for complex industry datasets. Publications emphasize multi-view clustering, manifold learning, and domain adaptation. Collaborations include roles in organizing the 2021 AusDM conference and reviewing for IEEE journals.

Josef KittlerView profile

Professor

• Pattern Recognition
• Computer Vision
• Image Analysis
• +5 more

Josef Kittler is a Professor at the University of Surrey, affiliated with the School of Computer Science and Electronic Engineering and the Centre for Vision, Speech and Signal Processing (CVSSP). He holds editorial roles in journals like IEEE Transactions on Pattern Analysis and Machine Intelligence and serves on the IAPR's KS Fu Prize Committee. His research focuses on pattern recognition, computer vision, and applications in system identification, medical diagnosis (ECG), and automatic inspection. He has received prestigious awards, including the Honorary Medal from the Czech Technical University. His work spans theoretical advancements and practical implementations, with notable contributions to multimodal tracking, deep learning, and fusion techniques. Education & Roles: Research Assistant, Engineering Department, University of Cambridge (1973–75) SERC Research Fellow, University of Southampton (1975–77) Royal Society European Research Fellow, Paris (1977–78) IBM Research Fellow, Balliol College, Oxford (1978–80) Principal Research Associate/Scientific Officer, Rutherford Appleton Laboratory (1980–85) Research Interests: Kittler's work emphasizes theoretical and applied aspects of pattern recognition, including computer vision applications in security, healthcare, and robotics. His recent publications address challenges in multimodal tracking, self-supervised learning, and fusion algorithms. Awards: Best Paper Awards from Pattern Recognition Society, British Machine Vision Association, and IEE Honorary Medal (Czech Technical University, 1995) Advising & Grants: While specific student/advisor details aren’t listed, his leadership in CVSSP and editorial roles indicates extensive mentorship and collaborative projects. His research has been supported by grants enabling advancements in AI and computer vision. Labs & Teams: He leads the CVSSP, a world-renowned center for vision, speech, and signal processing research, fostering interdisciplinary projects in AI and multimedia systems.

Raymond Ka Wai WongView profile

Associate Professor

• Statistical Learning
• Causal Inference
• Functional Data Analysis
• +4 more

Raymond Ka Wai Wong is an Associate Professor and Director of the PhD Program in the Department of Statistics at Texas A&M University. He holds a PhD in Statistics from the University of California, Davis (2014), an MPhil from The Chinese University of Hong Kong (2010), and a BSc with minors in Mathematics and Risk Management Science (2008). His research focuses on causal inference, functional data analysis, low-rank modeling, reinforcement learning, and statistical learning with applications in astronomy, brain imaging, and genomics. Wong's professional roles include Associate Editor for Journal of Computational and Graphical Statistics , Journal of the American Statistical Association , and member of the Research Institute for Foundations of Interdisciplinary Data Science (FIDS). He has secured grants from NSF, NASA, and NIH, including leadership in projects like Virtual Assistant for Spacecraft Anomaly Treatment and phenomic selection in maize hybrids. His awards include Top Reviewer distinctions at NeurIPS (2023) and ICML (2020), and a 2016 Discussion Paper in the Annals of Applied Statistics. He has advised numerous doctoral students, with notable advisees receiving awards such as the ICSA Student Paper Award and Emanuel Parzen Fellowship. Wong’s recent work emphasizes methodological advancements in reinforcement learning (e.g., distributional off-policy evaluation) and matrix/tensor completion under informative missingness. His research bridges theoretical statistics with practical applications in interdisciplinary domains such as neuroscience, agriculture, and space exploration.

Yu-Wing TaiView profile

Associate Professor

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

Yu-Wing Tai is an Associate Professor of Computer Science at Dartmouth College, one of the Ivy League institutions. His research focuses on Computer Vision, Deep Learning, and Multimodalities Generative AI. He holds a PhD from the National University of Singapore and has served as an Associate Editor for IEEE TPAMI and as an Area Chair for major conferences like CVPR, ICCV, and NeurIPS. He is a Senior Member of the IEEE. Previously, he held roles as a Senior Research Director at Kuaishou Technology (2020-2023), Adjunct Professor at HKUST (2017-2023), and Research Director at Tencent (2017-2020). His work spans low-level vision, image/video editing, 3D reconstruction, and applications of large generative models. His research highlights include Motion-Agent for human motion generation, ChatCam for camera control, and Gear-NeRF for 3D reconstruction. He advises a team of PhD students and has mentored alumni now in academia and industry worldwide. His teaching includes courses on Multi-modalities Generative AI and Computer Graphics.

Md Abul BasharView profile

Research Fellow

• Deep Learning
• Natural Language Processing
• Machine Learning Applications
• +2 more

Md Abul Bashar is a Research Fellow at the Queensland University of Technology's Centre for Data Science and School of Computer Science. His research focuses on developing deep learning and machine learning solutions for natural language processing tasks, including abusive content detection, misinformation analysis, and social media mining. Industrial applications include unconscious bias detection systems deployed at Fortune 500 companies and automated marketing strategy generation commercialized by Robotic Marketer. Research areas span multimodal fusion (uncertainty-guided meta-learning in 2025, pre-gating attention mechanisms in 2024), longitudinal data analysis (GAN-based imputation in 2024), and cybersecurity applications (Log4Shell threat detection in 2023). Articles demonstrate strong emphasis on NLP techniques adapted for low-resource scenarios through transfer learning and generative models. Significant Applications Unconscious bias detection for enterprise content management Automated marketing report generation (commercialized) Misogyny detection featured in Forbes, Daily Mail Energy payment propensity prediction Indigenous heritage repatriation support systems

Krzysztof WolskiView profile

Researcher

• Computer Graphics
• Perceptual Quality Assessment
• Image Processing
• +3 more

Krzysztof Wolski is a Researcher in the Department of Computer Graphics at the Max Planck Institute for Informatics, Saarbrücken, specializing in perceptual quality metrics for computer graphics applications. He holds a PhD from Saarland University (2023) and maintains active research collaborations within the Saarland Informatics Campus ecosystem. His educational background includes: PhD in Computer Science (Computer Graphics), Saarland University and Max Planck Institute for Informatics (2018-2023) Master's in Computer Science (Computer Graphics), West Pomeranian University of Technology (2015-2016) Bachelor of Engineering in Computer Science (Computer Graphics), West Pomeranian University of Technology (2011-2015) Wolski's research centers on human visual perception applied to computer graphics, developing quantitative models for image, mesh, and display quality assessment. His work bridges computational methods with perceptual psychology to optimize rendering pipelines, texture mapping, and adaptive display systems, with emphasis on measurable human-visible differences in digital imagery. Analysis of his 2018-2024 publications reveals consistent focus on perceptual metrics, evolving from foundational visible difference prediction (2018-2019) to neural representations (2023) and adversarial scaling (2024). Key themes include dataset creation (Geo-metric for facial distortions), low-light depth perception (Dark Stereo), and neural denoising techniques, demonstrating increasing integration of deep learning with perceptual modeling. No scientific awards were documented in available sources. Information regarding student advising or research grants was not provided in the source material. He operates within the Computer Graphics department (Department 4) at the Max Planck Institute for Informatics, part of the Saarland Informatics Campus consortium that includes DFKI and Saarland University. His work connects with ELLIS Unit SAM and the Saarbrücken Research Center for Visual Computing, facilitating interdisciplinary research in perceptual AI and graphics.