Find Academic Supervisors

• Machine Learning
• Renewable Energy
• Quantum Computing
• +5 more

Dr. Hao Chen is an Assistant Professor at the University of Twente, affiliated with the Industrial Engineering & Business Information Systems department and the MESA+ Institute. His research focuses on renewable energy systems, machine learning applications, and autonomous systems, contributing to the United Nations Sustainable Development Goals. Dr. Chen's research interests include wind energy modeling, quantum computing for energy systems, vehicular edge computing, and trajectory prediction in autonomous driving. He explores machine learning techniques for fault detection in energy infrastructure and develops innovative solutions for sustainable energy scenarios. His work also addresses human-machine interaction in autonomous vehicles and environmental monitoring systems. His recent publications explore advancements in computer vision techniques for scene graph prediction, building outline extraction, and camera pose estimation. He has also contributed to studies on energy systems, including wind model downscaling, quantum computing applications, and cost-efficient vehicular edge computing for air pollution monitoring. His work bridges theoretical developments with practical applications in sustainable energy and autonomous technologies. No scientific awards or advising/grant information is available. His research aligns with the university's focus on sustainable innovation and technological solutions.

Neil HindleyView profile

Research Fellow

• Atmospheric Dynamics
• Remote Sensing
• Spectral Analysis Techniques
• +3 more

Neil Hindley is a Research Fellow at the University of Bath's Department of Electronic & Electrical Engineering, affiliated with the Centre for Climate Adaptation & Environment Research (CAER). He holds a PhD in Atmospheric Dynamics from the University of Bath (2016) and an MPhys Astrophysics from Cardiff University (2011). His research focuses on Atmospheric Dynamics, Remote Sensing, and Spectral Analysis Techniques, particularly studying gravity waves and their impacts on climate systems. He leads and collaborates on projects funded by the Natural Environment Research Council (NERC), including investigations into upper atmospheric waves, stratospheric-troposphere coupling, and ionospheric variability. As a NERC Independent Research Fellow, he contributes to the UN Sustainable Development Goals related to climate action and environmental protection. Recent projects include TEAMx-FLOW (2024–2028) and SURGE (2023–2028), exploring flow dynamics and upper atmospheric wave phenomena. His work integrates satellite observations (e.g., Aeolus wind lidar) with advanced modeling techniques, such as convolutional neural networks for gravity wave detection. Notable research includes analyzing the 2019/2020 quasi-biennial oscillation disruption and the global effects of the Hunga Tonga volcanic eruption on atmospheric dynamics. Awards: NERC Independent Research Fellow (2020–present). Grants include NERC funding for multiple projects totaling over £multi-million. His research outputs span 41 peer-reviewed articles, focusing on gravity wave characteristics, hurricane intensification mechanisms, and mesosphere-lower thermosphere interactions. Collaborations span international teams and institutions, advancing understanding of Earth's atmospheric systems.

Manmohan ChandrakerView profile

Professor

• Computer Vision
• 3D Reconstruction
• Scene Understanding
• +5 more

Manmohan Chandraker is a Professor in the Department of Computer Science & Engineering at the University of California, San Diego (UCSD). His research focuses on computer vision, 3D reconstruction, and autonomous driving, with interdisciplinary links to robotics, machine learning, and computer graphics. He leads projects in 3D scene understanding, real-time visual odometry, and sensor fusion for autonomous systems. His work has been recognized with top academic awards and industry accolades. Education: B.Tech. in Electrical Engineering from IIT Bombay Ph.D. in Computer Science from UCSD Postdoctoral scholarship at UC Berkeley Research Interests: Chandraker’s work addresses challenges in 3D reconstruction under complex lighting and materials, autonomous driving systems, and physically-based inverse rendering. Key contributions include frameworks for non-convex optimization in geometric reconstruction and semantic-driven dense reconstruction techniques. His research bridges theory and practice, with applications in augmented reality, robotics navigation, and automotive safety systems. Key Awards: Best Paper Award at CVPR 2014 UCSD CSE Dissertation Award (2009) NEC Business Contribution Award (2015) Advising & Industry Collaboration: He has led teams at NEC Labs America, collaborating with automobile manufacturers to develop single-camera systems for real-time autonomous driving. His work on closed-loop traffic simulations and adversarial scenario generation has driven industry partnerships and patents. Current research explores multimodal LLM integration, data-centric AI tools (e.g., Ymir platform), and photorealistic scene synthesis. Labs & Projects: Chandraker’s group develops open frameworks like OpenRooms and TRoVE for 3D scene datasets, and contributes to benchmarks for domain adaptation in vision tasks. His team also works on safety-critical systems for autonomous vehicles through projects like SAFE-SIM and Drive-1-to-3.

Dongyeon KimView profile

Researcher

• Augmented Reality (AR)/Virtual Reality (VR)
• Computational Optics
• 3D/4D Displays
• +3 more

Dongyeon Kim is a Researcher at the University of Cambridge's Department of Computer Science and Technology, collaborating with Prof. Rafal Mantiuk. She holds a Ph.D. in Electrical and Computer Engineering from Seoul National University (SNU), where she contributed to the Optical Engineering and Quantum Electronics Lab under Prof. Byoungho Lee. Her work focuses on bridging computational optics with visual perception, particularly in next-generation AR/VR displays, holographic systems, and 3D display technologies. Education: Ph.D., Electrical and Computer Engineering, Seoul National University (2023) Bachelor's Degree, Electrical and Computer Engineering, Seoul National University (20XX) Research Interests: Augmented/Virtual Reality (AR/VR) display systems Holographic display optimization and human perception modeling Computational optics for 3D/4D displays Visual perception evaluation and quality metrics Integration of machine learning in display technology Awards & Recognition: Sejong Science Fellowship Grant (Korean Government) Best Paper Honorable Mention at SIGGRAPH Asia 2024 Grants & Funding: Sejong Science Fellowship for postdoctoral research Lab & Team: Collaborates with Prof. Mantiuk's research group at the University of Cambridge, focusing on perceptual evaluation of AR displays and dataset development (e.g., AR-DAVID).

• Artificial Intelligence
• Computer Vision
• Human-Computer Interaction
• +15 more

Dr. Stuart James is an Assistant Professor in Visual Computing at Durham University's Department of Computer Science, with a research focus on Visual Reasoning and Spatial Reasoning. His work bridges 2D/3D scene understanding with applications in Cultural Heritage, Digital Humanities, and Assistive Technologies. Key projects include EU-funded initiatives RePAIR (FET), DCitizens (Twinning), and BoSS (Lighthouse). Previous roles include Researcher/PostDoc positions at Istituto Italiano di Tecnologia (IIT) and University College London (UCL), where he earned his PhD in visual information retrieval for sketches from the University of Surrey. PhD in Visual Information Retrieval (University of Surrey, 2015) hBSc in Computer Science with Games Development (University of Hull, 2009) Research interests span Artificial Intelligence, Computer Vision, Human-Computer Interaction, and Robotics, with emphasis on Sketch-based Retrieval , Scene Graphs , Visual Question Answering , and Knowledge Retrieval & Reasoning . The VIViD research group at Durham drives innovations in neuro-symbolic reasoning for cultural analytics. Publications highlight trends in 3D scene understanding, cultural heritage digitization, and AI-driven storytelling frameworks. Scientific accolades include: CVPR 2025 Outstanding Reviewer ICIAP 2019 Best Student Paper IPAS 2018 Best Paper Current projects involve EU grants totaling €10.6 million across RePAIR, DCitizens, and BoSS. Collaborations include IIT (External Scientist), UCL (Honorary Associate), and ITI/LARSyS (International Collaborator). The lab recruits PhD students and organizes VISART workshops, while also advertising KTP Associate positions in Machine Learning for railway monitoring.

Lukas RothView profile

Lecturer

• High-Throughput Field Phenotyping
• Agricultural Drone Technology
• Plant Breeding
• +4 more

Lukas Roth is a Lecturer at the Department of Environmental Systems Science at ETH Zurich. His research focuses on high-throughput field phenotyping, integrating drone-based platforms and stationary systems like the Field Phenotyping Platform (FIP). He develops low-level and intermediate traits for crops such as wheat and soybean to enhance breeding efficiency. Key areas include physiological breeding approaches, dynamics of growth under temperature variations, and machine learning applications in agriculture. His work emphasizes improving crop resilience to climate change through advanced phenotyping techniques. He contributed to the FIP 1.0 dataset and explores trade-offs between mobility (drones) and precision (stationary platforms). Recent studies address temperature response modeling, senescence dynamics, and the integration of climate scenarios with ground data. Key Projects: Phenoflow (SNF-funded), GWFSS dataset, Trait Spotting. Tools Developed: PhenoFly Planning Tool, low-level trait analysis frameworks. He received the ETH Medal for outstanding doctoral theses (2021). His research bridges field experimentation, computational methods, and practical agricultural solutions.

Daniel CremersView profile

Professor

• Computer Vision
• Machine Learning
• Deep Learning
• +10 more

Prof. Daniel Cremers is a Professor of Informatics and Mathematics at the Technical University of Munich (TUM), leading the Chair of Computer Vision & Artificial Intelligence within the TUM School of Computation, Information and Technology. His work spans computer vision, machine learning, and mathematical image analysis, with contributions to autonomous systems, SLAM, and self-driving cars. Affiliations: Chair of Computer Vision & AI, TUM School of Computation, TUM. Education: Bachelor's in Mathematics and Physics (University of Heidelberg), Master's in Theoretical Physics (University of Heidelberg), PhD in Computer Science (University of Mannheim). Research Interests: Focuses on computer vision fundamentals, deep learning, variational methods, and real-world applications like autonomous systems. Key areas include 3D reconstruction, SLAM, and geometric processing. Recent Contributions: Over 12 papers accepted to CVPR 2025, including work on probabilistic bundle adjustment and dynamic scene reconstruction. Notable achievements include the ECCV 2024 Koenderink Award for LSD SLAM and the Leibniz Prize (2016). Awards: Leibniz Prize, ERC grants (Starting, Consolidator, Advanced), multiple best paper awards. Teaching: Offers courses like Computer Vision II: Multiple View Geometry and Introduction to Deep Learning. Supervises practical courses on deep learning methods and autonomous systems. Labs/Teams: Leads the Computer Vision Group, actively organizing conferences (e.g., GCPR/VMV 2024) and contributing to open datasets like DeepScenario. Engages in industry through startups and advisory roles.

• 3D Computer Vision
• Computer Graphics
• Machine Learning
• +5 more

Dimitris Tzionas is an Assistant Professor for 3D Computer Vision at the University of Amsterdam (UvA), leading research in the intersection of Computer Vision, Computer Graphics, and Machine Learning. His work focuses on modeling human-object interactions, 3D/4D perception, and synthesis for applications in Ambient Intelligence, Virtual Assistants, and the Metaverse. He holds an ERC Starting Grant (€1.5M) and ELLIS Amsterdam affiliations. Previously, he was a Research Scientist at Max Planck Institute for Intelligent Systems (MPI-IS), contributing to projects like the GRAB dataset and MANO/SMPL body models. He earned his PhD from the University of Bonn on hand-object interaction, advised by Jürgen Gall, and completed his MSc at Aristotle University of Thessaloniki in Electrical & Computer Engineering. His research has produced impactful datasets (e.g., GRAB, InterCap, DAMON) and methods like InteractVLM and PICO, advancing human-centric AI. He actively serves on CVPR/ICCV/ECCV committees and teaches 'Introduction to Computer Vision' at UvA. Notable awards include NVIDIA grants, Google Research Gifts, and multiple Outstanding Reviewer recognitions. His lab collaborates widely, advising over 10 PhD/MSc students and postdocs, with projects funded by ERC, ELLIS, and industry partners like NVIDIA and Google. Key themes include 3D reconstruction, interaction modeling, and generative AI for realistic avatar creation.

• Computer Vision
• Human Activity Forecasting
• First-Person Vision
• +5 more

Kris Kitani is an Associate Research Professor at the Robotics Institute and Courtesy Professor in Electrical and Computer Engineering at Carnegie Mellon University (CMU). He also holds roles as a Research Scientist at Meta FAIR and Co-Director of CMU's Extended Reality Technology Center (XRTC). His research focuses on computer vision, human activity forecasting, first-person vision, inverse reinforcement learning, and assistive technologies for visually impaired individuals. Kitani's education includes a BS from the University of Southern California, and MS and PhD from the University of Tokyo. His work bridges perception, decision-making, and interaction in autonomous systems, with applications in wearable cameras, assistive devices, and human-robot collaboration. He has advised numerous PhD and master's students, including current researchers like Jinkun Cao and Erica Weng. Key awards include the Marr Prize honorable mention (ICCV 2017), Best Paper Honorable Mentions at CHI 2017/2020, and Best Paper Awards at W4A 2017/2019. His lab, the Cognitive Assistance Laboratory, develops systems for robust real-world perception and interactive decision-making. Kitani teaches courses such as Graduate Computer Vision (16-720) and Undergraduate Computer Vision (16-385) at CMU. His research spans robotics, AI, and human-centered computing, with a focus on first-person vision and assistive technologies.

• Biomedical Imaging
• Optical Coherence Tomography
• Ophthalmic Diagnostics
• +2 more

Myeong Jin Ju is Assistant Professor in the Department of Ophthalmology & Visual Sciences at the University of British Columbia, with joint appointment in the School of Biomedical Engineering. Research focuses on developing advanced optical imaging technologies for vision science applications. Laboratory specializes in optical coherence tomography (OCT) systems design, signal processing algorithms, and multi-modal imaging integration. Current work aims to improve clinical ophthalmic diagnostics through high-resolution retinal and choroidal visualization, automated pathology quantification, and functional assessment techniques. Applications target diabetic retinopathy, age-related macular degeneration, and inherited retinal diseases. Recent innovations include MHz-range OCT systems, polarization-diversity techniques, and computational approaches for motion correction and image enhancement. Research bridges engineering innovation with preclinical and clinical translation in vision science.

Kevin Y ChenView profile

Lecturer

• Healthcare Quality Improvement
• Medical Education
• Artificial Intelligence
• +3 more

Kevin Y Chen is an MS Student in Health Policy at Stanford University and a Clinical Instructor at Stanford Children's Health. He holds concurrent roles as an Intermountain Fellow in Population Health, Delivery Science, and Primary Care. His academic background includes a pediatrics residency at the University of Utah School of Medicine. His research focuses on improving healthcare quality and process efficiency through high-value care initiatives. Key technical interests span artificial intelligence, robotics, and autonomous systems, with recent work addressing reinforcement learning, LiDAR data transfer for autonomous vehicles, and language-conditioned robot behavior. His articles reflect interdisciplinary contributions to robotics, computer vision, and embodied AI, with a focus on navigation, 3D reconstruction, and human-robot interaction. Current research integrates AI-driven solutions to healthcare challenges such as workflow optimization and medical education. No scientific awards have been explicitly mentioned in the provided text. His advising and grant activities remain unspecified, though his roles suggest involvement in clinical and educational projects.

• Computer Vision
• 3D Reconstruction
• Neural Rendering
• +5 more

Dr. Miaomiao Liu is a Research Fellow at the School of Computing, The Australian National University. Her research focuses on computer vision, 3D reconstruction, and neural rendering, with applications in robotics, autonomous systems, and renewable energy forecasting. She leads multiple projects including Next-Generation Aviation Safety Net Air Traffic Management, Machine Vision Techniques for Solar Power Forecasting, and 3D Vision Geometric Optimization in Deep Learning. Her work integrates cutting-edge techniques such as neural radiance fields, depth estimation, and self-supervised learning to address challenges in dynamic scene reconstruction, motion forecasting, and image deblurring. She has pioneered methods for mining supervision signals in dynamic regions and developing language-driven deblurring networks. Key Projects: Aviation safety systems, solar irradiance prediction, and geometric optimization in deep learning Research Themes: 3D scene understanding, human motion prediction, and neural rendering Technical Expertise: Neural networks, multi-view stereo, and physics-based modeling Dr. Liu's research has been published in top-tier venues like CVPR and IEEE conferences, with over 2,450 citations. While not explicitly listed as part of a lab, her work demonstrates strong collaboration with industry partners such as CSIRO and aerospace stakeholders. She actively supervises research students in areas like 3D vision and energy systems.

• Augmented Reality
• Virtual Reality
• Human-Computer Interaction
• +3 more

Jeronimo Grandi is an Assistant Professor in the Department of Computer & Cyber Sciences at the School of Computer and Cyber Sciences, Georgia Southern University. His academic work spans augmented reality (AR), virtual reality (VR), and human-computer interaction, with a focus on collaborative technologies and cybersecurity in extended reality (XR). He holds a Ph.D. and M.S. in Computer Science from Federal University of Rio Grande do Sul (2018 and 2014), and a B.A. in Game and Interactive Media Design from University of Caxias do Sul (2011). His research emphasizes AR/VR interfaces for first responders, public safety applications, and continuous authentication systems. Notable achievements include the Best 3DUI Contest Award (2023), Unreal Faculty Fellowship (2023), and a Ph.D. distinction (Cum Laude) in 2018. He teaches courses like Human-Computer Interaction and Dissertation Research, and actively reviews for conferences like ACM ISMAR and IEEE VR. Key research directions include modular AR frameworks for emergency response, cross-reality collaboration tools for sustainability education, and visual search optimization in VR. His work bridges theoretical HCI principles with practical implementations in law enforcement, healthcare, and disaster management scenarios.

Abhijit MahalanobisView profile

Associate Professor

• Computational Sensing and Imaging
• Video/Image Processing
• Automatic Target Recognition (ATR)
• +3 more

Dr. Abhijit Mahalanobis is an Associate Professor at the University of Central Florida (UCF), affiliated with the Center for Research in Computer Vision (CRCV). Previously, he served as a Senior Fellow at Lockheed Martin and held academic positions at the University of Arizona and the University of Maryland. He earned his B.S. from UC Santa Barbara (1984) and M.S./Ph.D. from Carnegie Mellon University (1985/1987). His research focuses on computational sensing, imaging systems, and automatic target recognition (ATR). Notable contributions include work on correlation filters, compressive sensing, and 3D imaging. He has published over 170 papers, holds four patents, and co-authored a book on pattern recognition. Dr. Mahalanobis has received prestigious awards such as the IEEE Fellow (2015), OSA Fellow (2004), and SPIE Fellow (1997). He has been honored with the Lockheed Martin NOVA Award (2005), Scientist of the Year (2006), and Innovator of the Year (1999). He has served on editorial boards for journals like Applied Optics and Pattern Recognition , and chairs for OSA and SPIE conferences. His research spans topics including infrared target detection, compressive sensing architectures, and deep learning for target recognition. The CRCV lab, under his leadership, develops advanced algorithms for defense and surveillance applications.

• Bioengineering
• Neuroimaging
• Machine Learning
• +4 more

Ilaria Boscolo Galazzo is a Temporary Assistant Professor at the Department of Engineering for Innovation Medicine at the University of Verona. Her academic work focuses on the intersection of engineering, neuroscience, and medical applications, with particular expertise in neuroimaging techniques and computational analysis of biomedical data. Academic Sector: IBIO-01/A - Bioengineering Research Sectors: PE6_11 (Machine learning and signal processing), LS5_17 (Imaging in neuroscience), LS5_16 (Systems and computational neuroscience) Her research interests center on neuroimaging, including structural (diffusion MRI) and functional (EEG, fNIRS, functional MRI) imaging techniques, as well as perceptual analysis through cognitive science methods. She employs advanced computer vision and pattern recognition methods to design numerical biomarkers for characterizing healthy and pathological conditions. Her work aims to gain a holistic view of the human brain through integration of multi-modal, multi-scale probing and modeling approaches. Dr. Boscolo Galazzo is actively involved in several research groups including the Neuroimaging Group and Vision, Images, Patterns and Signals (VIPS) group, where activities focus on analysis, recognition, modeling and prediction of multivariate multidimensional signals using AI and machine learning techniques. Her research spans image processing, computer vision, pattern recognition, and biomedical data analysis for both basic and translational research. She has led multiple research projects including EDIPO (focused on neuroimaging genetics in translational research), investigations into brain connectivity underlying physiological and pathological patterns in action tremor, and mapping functional connectivity patterns in neurological diseases using advanced MRI techniques. As an educator, she serves on multiple teaching committees including the Computer Science Teaching Committee, Mathematics and Data Science Teaching Committee, and Information Engineering Teaching Committee. She also participates in the Collegio dei Docenti del Dottorato in Ingegneria dei Sistemi Intelligenti and the Consiglio di Dipartimento Ingegneria per la Medicina di Innovazione.