Find Academic Supervisors

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

Dr. Maryam Haghighat is a Lecturer at QUT's School of Electrical Engineering & Robotics, specializing in Machine Learning, Computer Vision, and Robotics. She holds a PhD from UNSW's School of Electrical Engineering and Telecommunications, where her thesis earned the 2020 UNSW Award for Outstanding Doctoral Thesis. Her postdoctoral research at the University of Oxford's Big Data Institute (2020–2022) focused on medical image analysis for the PathLAKE project. Currently, she leads AI projects with over $10M in funding, including ARC grants and industry collaborations. Her research spans applications in robotics, healthcare, and remote sensing. Notable contributions include developing machine learning algorithms for histopathology image analysis and robust multi-robot localization systems. Awards include the UNSW Doctoral Thesis Award (2020). She actively supervises PhD students in topics like multimodal fusion and 3D visual understanding. Publications highlight advancements in AI-driven medical imaging, hyperspectral transformers, and illumination-aware video compression. Her work bridges theoretical machine learning with practical applications in healthcare, robotics, and environmental monitoring.

• Signal Processing
• Biomedical Signal Processing
• Image Reconstruction
• +12 more

Rabab K. Ward is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia, Faculty of Applied Science, Vancouver, Canada. She is a leading figure in signal processing with extensive contributions to biomedical engineering, image reconstruction, and machine learning. She served as President of the IEEE Signal Processing Society (2016–2017) and continues to be actively involved in research and leadership. Her research focuses on signal processing , biomedical signal analysis , compressed sensing , medical image fusion and reconstruction , and deep learning for healthcare applications . She has pioneered methods in EEG and CT image processing, epileptic seizure detection, and 3D human pose estimation. The recent publications highlight her work in 3D face and human modeling , GAN-based forensics , brain-computer interfaces , and medical video analysis . Her research integrates advanced machine learning with practical engineering solutions for clinical and robotic systems. Her scientific awards include being an IEEE Fellow , Distinguished Lecturer of the IEEE Signal Processing Society , and serving as President of the IEEE Signal Processing Society . She has advised numerous students and researchers in areas such as compressed sensing , EEG signal processing , image reconstruction , and deep learning . Her collaborative work includes significant grants and projects in biomedical engineering , neurotechnology , and autonomous systems . Dr. Ward is associated with research labs and teams focused on signal processing for healthcare , medical imaging , and intelligent systems at UBC, fostering interdisciplinary collaboration in engineering and medicine.

• Virtual Reality
• Augmented Reality
• Computer Vision
• +6 more

Prof. Dr. Didier Stricker is a distinguished Professor of Computer Science at Rhineland-Palatinate University of Technology Kaiserslautern-Landau (RPTU) and serves as Scientific Director and Head of the Augmented Reality Research Department at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern. He leads the Augmented Vision Group, which comprises approximately 30 researchers working across various domains of computer vision and augmented reality. His work bridges academic research with industrial applications through collaborations with major companies including Sony, Google, and John Deere. His educational background includes electrical engineering studies at the Polytechnic Institute of Grenoble and the Technical University of Karlsruhe. He earned his doctorate from the Technical University of Darmstadt in 2002 with a dissertation on "Computer Vision-Based Calibration and Tracking Methods for Augmented Reality Applications." Prof. Stricker's research spans virtual and augmented reality, computer vision, human-computer interaction, cognitive interfaces, and on-body sensor networks. His work focuses on developing practical applications that enhance human capabilities through advanced visual computing technologies. He has pioneered approaches in video and sensor analytics, particularly in creating cognitive interfaces that respond intelligently to user needs and environmental contexts. His recent publications reveal a strong emphasis on 3D scene understanding, real-time processing for augmented reality applications, and the integration of large language models with spatial reasoning capabilities. There's a clear trend toward more sophisticated multimodal approaches that combine vision, language, and spatial understanding to create more natural and intuitive human-computer interactions. Among his notable achievements: Innovation Prize of the German Society of Computer Science (2006) Organized the first IEEE & ACM International Symposium on Mixed and Augmented Reality (ISMAR) in 2002 Member of the ISMAR steering committee from 2000-2007 Multiple best paper and demonstration awards at major conferences Several registered patents in tracking and augmented reality technologies Prof. Stricker has supervised numerous PhD and Master's students through his leadership of the Augmented Vision Group. His research is supported by significant funding from both European and national research organizations, as well as through industrial partnerships. He serves as an expert reviewer for various research funding bodies and contributes to the academic community through editorial roles for journals and conferences in VR/AR and computer vision. The Augmented Vision Group under his direction maintains strong connections with industry partners and participates in numerous collaborative research projects including LUMINOUS, SHARESPACE, I-Nergy, BIONIC, and VIDETE. These projects span applications in language-augmented XR systems, social experiences in hybrid spaces, AI for energy systems, personalized body sensor networks, and 4D scene analysis.

Yao-Jen ChangView profile

Researcher

• Computer Vision
• Machine Learning
• 3D Reconstruction
• +3 more

Yao-Jen Chang is a researcher specializing in computer vision and machine learning, with significant contributions to 3D reconstruction, image registration, and biometric authentication systems. His work often involves collaboration with Tsuhan Chen at Cornell University and explores active learning frameworks, multi-view object recognition, and reinforcement learning applications. Key research areas: 3D modeling, multimedia security, and video processing Collaborations include National Tsing Hua University and Cornell University Research Trends Recent publications focus on: Deep reinforcement learning for image alignment User-interactive 3D reconstruction Biometric key generation for security Multi-view object recognition algorithms

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +5 more

Cem Eteke is a doctoral candidate at the Chair of Media Technology within the Munich Institute of Robotics and Machine Intelligence (MIRMI) at the Technical University of Munich (TUM). His research focuses on semantic image and video synthesis, with an emphasis on explainable generative models, diffusion models, and their applications to semantic communication systems. Education: B.Sc. in Computer Engineering and Electrical & Electronics Engineering (2017), M.Sc. in same field (2019) from Koc University, Istanbul. Professional Experience: Former Data Scientist and AI Engineer, with brief research on high-dimensional non-convex optimization algorithms. Research Trends: His recent publications (2022-2025) highlight advancements in real-time semantic communication, diffusion models for video synthesis, and neural rendering techniques for indoor scene segmentation. Key themes include lossy coding strategies, spatially adaptive conditioning, and temporally consistent generative modeling. Projects: Involved in research initiatives like the Centre for Tactile Internet with Human-in-the-Loop (CeTI) , 5G Testbed Bayern , and KMU-Innovativ: KIMaps , focusing on haptic communication, indoor navigation, and semantic scene understanding.

Matthieu EXBRAYATView profile

Lecturer

• Machine Learning
• Data Analysis
• Data Visualization
• +7 more

Matthieu EXBRAYAT is a Lecturer in Computer Science at the University of Orleans, affiliated with the Fundamental Computer Science Laboratory of Orléans (LIFO). He holds significant administrative responsibilities including Vice President for Digital and Educational Innovation at the University of Orleans and previously served as Co-head of the IT department (2019-2021) and Head of the IMIS Computer Science Masters (2015-2021). His research spans multiple domains with a primary focus on Machine Learning applications in data analysis and visualization, time series analysis, and computer vision applications in archaeology. His work demonstrates strong interdisciplinary connections between computer science and archaeological documentation, particularly in ceramic sherd classification using deep learning techniques. Earlier in his career, he contributed significantly to high-performance databases and probabilistic relational learning, especially Markov logic networks. Analysis of his publication history reveals an evolving research trajectory: beginning with database systems and parallel join algorithms in the early 2000s, progressing through Markov logic networks and clustering algorithms in the 2010s, and more recently focusing on machine learning applications in archaeological imaging and programming language analysis. His recent publications show a clear shift toward practical applications of machine learning in diverse domains while maintaining theoretical rigor. Throughout his career, EXBRAYAT has maintained extensive collaborative research networks, with Lionel Martin appearing as a co-author on 20 publications, followed by Guillaume Cleuziou (14), Jacques-Henri Sublemontier (9), and Quang-Thang Dinh (7). These collaborations span multiple research domains and demonstrate his ability to work across disciplinary boundaries. As an educator, he teaches distributed information systems, programming languages, databases, AI/data mining, and geographic information systems. He has also been actively involved in scientific mediation, delivering numerous public lectures on artificial intelligence topics since 2017, including conferences such as "Living in harmony with AIs" (2023) and "Artificial Intelligence: My new toaster is an AI!" (2019). His administrative contributions are substantial, including roles as Communications Correspondent for the Computer Science Disciplinary Center, Facilitator of the DataCenters ComUE Centre Val de Loire reflection group, and Vice President of Digital Resources at the Leonardo da Vinci Confederal University. He also served as LIFO website webmaster for over a decade (2002-2014) and was responsible for the STIC degree program (now IT degree) from 2004-2008.

• Virtual Reality
• Augmented Reality
• Computer Vision
• +4 more

Prof. Dr. Didier Stricker is a leading academic in computer science, serving as Scientific Director at the German Research Center for Artificial Intelligence (DFKI) and Professor at the University of Kaiserslautern-Landau (RPTU). His career spans over two decades, including leadership roles at Fraunhofer IGD and founding the Augmented Vision research unit at DFKI/RPTU, which now includes ~30 researchers. Education: Electrical Engineering (Technical University of Grenoble, Karlsruhe) PhD: Computer Vision-based Calibration and Tracking Methods for Augmented Reality (2002, TU Darmstadt) His research focuses on virtual and augmented reality , computer vision , human-computer interaction , and on-body sensor networks . He leads major EU/national projects like LUMINOUS (Language-Augmented XR) and SHARESPACE (Ethical Hybrid Shared Spaces), with industrial partnerships including Sony, Google, and John Deere. Recent publications emphasize 3D reconstruction , neural network optimization , and XR systems . Key trends include event camera processing , scene flow estimation , and multimodal AI for industrial applications . He holds patents in AR tracking and has received the 2006 Innovation Prize from the German Society of Computer Science. Scientific Awards : Innovation Prize (2006) Best Paper/Demonstration Awards at ISMAR, EUSIPCO, CVPR, and ICRA As a reviewer for journals and conferences in VR/AR and computer vision, he contributes to shaping research standards. His lab ( AG Augmented Vision ) combines academic and industrial collaborations to advance cognitive interfaces and extended reality systems.

Jin Young LeeView profile

Assistant Professor

• Image Processing
• Video Processing
• 3D Video Coding
• +6 more

Jin Young Lee is an Assistant Professor at the School of Intelligent Mechatronics Engineering , Sejong University , specializing in image and video processing. He previously worked as a Staff Engineer at Samsung Electronics from 2008 to 2018. Education : Ph.D. (2018), M.S. (2008), B.S. (2006) from KAIST and Sungkyunkwan University Research Focus : Video coding standards, super-resolution, image denoising, and defect detection His research explores 3D video coding , multi-view compression , and deep learning for image restoration . Recent articles highlight applications in brain MR imaging and industrial quality control . Key article trends include lightweight neural networks (e.g., DCP-based U-Net), quaternion transformers for noise reduction, and distributed feature fusion in defect detection. Most works intersect computer vision , deep learning , and signal processing .

• Machine Learning
• Computer Vision
• 3D Geometry
• +2 more

Dr. Jun Li is a Senior Lecturer at the School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney (UTS), Australia. He received his Ph.D. in Computer Science from Queen Mary University of London in 2009 and is affiliated with the Australian Artificial Intelligence Institute (AAII) at UTS. His research spans multiple domains within artificial intelligence, with primary focus on Machine Learning applications in computer vision and 3D geometry. Dr. Li has published extensively in high-impact journals including IEEE Transactions (TPAMI, TIP, TNNSLS) and Pattern Recognition, with recent work expanding into interdisciplinary research in earth science and marine applications. His research output demonstrates consistent productivity with numerous publications each year across diverse AI application areas. Dr. Li's work shows strong thematic progression from foundational computer vision techniques to applied interdisciplinary research. Early work focused on face hallucination and video super-resolution, while more recent publications address environmental applications using Graph Neural Networks for wave prediction and damage classification for disaster response. His research consistently bridges theoretical AI advances with practical real-world applications across healthcare, autonomous systems, and environmental science. AI to assist disaster emergency response (2023-2026) Applying Generative Adversarial Network in Medical Image Analysis (2020-2021) Big Massive Open Online Course (MOOC) Data Retrieval (2017-2020) As an educator, Dr. Li teaches core courses including '31005 Machine Learning' and '32513 Advanced Data Analytics Algorithms' at UTS, and is available for Masters Research and PhD student supervision, contributing to the development of next-generation AI researchers.

• Data Science
• Artificial Intelligence
• Healthcare Analytics
• +3 more

Dr. Andrew Peng is a Lecturer (Research) at the Australian Artificial Intelligence Institute (AAII) within the Faculty of Engineering and Information Technology at University of Technology Sydney (UTS), Australia. With dual PhDs from UTS (2015) and Beijing Institute of Technology (2013), he has published 45 peer-reviewed papers across top venues like IEEE ICDM, COLING, and Frontiers in Molecular Biosciences. Education: Dual PhD (2013-2015) from Beijing Institute of Technology and University of Technology Sydney His research focuses on Data Science , Artificial Intelligence , and Healthcare Analytics , addressing challenges in medical data analysis, unstructured clinical text processing, and federated learning frameworks. Recent publications explore: Deep graph clustering for community detection Privacy-preserving medicine shortage detection via social media Time-aware medication recommendation using dynamic treatment regimes Knowledge tracing enhancements for online education Contrastive learning approaches for ICD coding Hypergraph-based sequential diagnosis prediction Dr. Peng has secured over AUD $1M in external research grants and serves as Subject Coordinator for undergraduate/postgraduate courses. He contributes to professional leadership through roles as Web Chair at AJCAI 2021 and ADMA 2021, PC member for major conferences, and reviewer for journals like NeurIPS and AAAI. His work spans collaborations with universities, industry, and government agencies.

Vincent TourreView profile

Associate Professor

• Urban Data Visualization
• Morphological Analysis of Urban Spaces
• Natural Lighting Simulation
• +5 more

Vincent Tourre is an Associate Professor in the Department of Computer Science at Centrale Nantes, affiliated with the CRENAU Research Team (UMR CNRS 6051), a joint unit involving CNRS, Université Grenoble Alpes, and multiple architecture schools. His academic career spans urban data visualization, morphological analysis of urban spaces, and natural lighting simulation. His research interests focus on Urban Data Visualization , Morphological Analysis of Urban Spaces , and Virtual Reality Applications for urban environments. Recent work examines 360° image perception, urban soundscapes classification, and immersive geospatial data visualization. Tourre's publications demonstrate consistent output in top venues including ISPRS Annals, International Journal of Geographical Information Science, and the Journal of the Acoustical Society of America. Tourre's 15 most recent articles reveal a strong trajectory in interdisciplinary urban computing , with growing emphasis on machine learning applications for urban data analysis (particularly in acoustics and visual perception) since 2020. His work increasingly integrates virtual reality with multi-sensor urban monitoring systems. As course leader at Centrale Nantes, Tourre teaches urban data analysis, scientific visualization, and knowledge representation. His research training contributions include doctoral supervision and development of the CORAULIS VR Platform for immersive urban studies. The CRENAU team's collaborative structure provides access to extensive urban datasets and interdisciplinary expertise across architecture, computer science, and environmental studies.