Find Academic Supervisors

Hazar DibView profile

Associate Professor

• Building Information Modeling (BIM)
• Information Systems
• Knowledge Management
• +7 more

Hazar Dib is an Associate Professor at the School of Construction Management Technology, Purdue Polytechnic Institute, Purdue University. His work bridges Building Information Modeling (BIM) , knowledge management , and interactive learning technologies to advance construction education and process efficiency. Ph.D. in Building Construction (2007, University of Florida) MSc. in Civil Engineering (2002, University of Balamand) BSc. in Civil Engineering (2000, University of Balamand) Research Interests focus on: BIM lifecycle integration AI/knowledge-based systems for construction Digital tools for surveying and steel connection education Collaborative communication frameworks Sustainability metrics in building practices Human factors in technology adoption Scientific Awards include multiple Charles B. Murphy Awards for undergraduate teaching and the Society of Leaders in Construction induction. His NSF-funded projects like VELS (Virtual Environment for Learning Surveying) and Steel Connection Teaching Tool highlight his innovation in STEM education . Articles span serious games , visual learning , and BIM maturity assessments , reflecting his interdisciplinary approach.

Michael BronsteinView profile

Professor

• Geometric Deep Learning
• Graph Representation Learning
• Computer Vision
• +4 more

Affiliations and Roles Michael Bronstein is a Professor at the Università della Svizzera italiana (USI) in the Faculty of Informatics and the Institute of Computational Science . He holds the Chair in Machine Learning and Pattern Recognition at Imperial College London and serves as Head of Graph Learning Research at Twitter . Previously, he was affiliated with the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI) as an Adjunct Professor. Education Ph.D. in Computer Science, Technion–Israel Institute of Technology (2007) Visiting appointments at Stanford University, MIT, Harvard University, and Tel Aviv University Research Interests Bronstein's work focuses on geometric deep learning , graph representation learning , and non-rigid shape analysis . He pioneered methods for extending machine learning to non-Euclidean domains like graphs and manifolds. His research combines theoretical advancements in spectral geometry with practical applications in computer vision, robotics, and medical imaging. Publications Trends His articles emphasize geometric deep learning frameworks, functional maps for shape correspondence, and spectral methods for manifold analysis. Key themes include invariant representations, partial shape matching, and applications in 3D reconstruction and graph neural networks. Awards and Honors Five ERC Grants Royal Society Wolfson Research Merit Award IEEE and IAPR Fellowships World Economic Forum Young Scientist Advising and Entrepreneurship Bronstein is a serial entrepreneur, founding companies like Novafora , Invision (acquired by Intel), and Fabula AI (acquired by Twitter). His academic advising spans PhD and Master’s students in machine learning and geometry processing. Labs and Teams Active in the Institute of Computational Science at USI and leads Twitter’s Graph Learning Research team, focusing on real-world applications of geometric deep learning.

• Computer Architecture
• Microarchitecture
• Compilers
• +7 more

Antonio Maria Gonzalez Colas is a Full Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture within the Faculty of Computer Science of Barcelona (FIB). He leads the ARCO research group focused on Microarchitecture and Compilers and is actively engaged in high-impact research in computer architecture, GPUs, and energy-efficient computing. His collaborations extend to the Barcelona Supercomputing Center and various national and European research initiatives. Research Interests: His primary research areas include computer architecture, microarchitecture, compilers, GPUs, and processor design. He focuses on energy-efficient computing, deep neural network (DNN) accelerators, GPU simulation and optimization, memory systems, and architectural support for machine learning and autonomous systems. His work often integrates compiler techniques with hardware design for performance and efficiency. Scientific Production Trends: His recent publications demonstrate a strong focus on energy-efficient hardware for AI workloads, particularly DNN and speech recognition acceleration, GPU architectural innovations, memory optimization, and real-time rendering. He frequently publishes in top-tier venues such as ISCA, MICRO, HPCA, and IEEE/ACM journals. ICREA Academia Award 2024 HiPEAC 2024 Paper Award ACM Senior Member (2020) Advising and Grants: He has advised numerous PhD students whose theses cover topics like energy-efficient architectures for autonomous driving, speech recognition, and neural networks. He leads competitive R&D projects, including an ERC Advanced Grant and projects funded by the Spanish National Program and the ICREA Academia program, focusing on domain-specific architectures and cognitive computing units. Labs and Teams: He is the principal investigator of the ARCO (Microarchitecture and Compilers) research group at UPC, a leading team in computer architecture research in Spain. The group is part of a larger collaborative network within UPC and with international partners.

• 3D Light Measurement
• Optical Shape
• Structured Light
• +7 more

Robert Sitnik is a Professor and Dean of the Faculty of Mechatronics at Warsaw University of Technology, where he is affiliated with The Institute of Micromechanics and Photonics. He earned his Doctorate in 2002 and has established himself as a leading researcher in 3D optical measurement techniques and applications. His research focuses on 3D light measurement, optical shape analysis, structured light techniques, and 3D data processing with applications spanning cultural heritage documentation and medical imaging. As the leader of the 3D/4D Optical Surface Measurement Team, he has pioneered innovative approaches to surface measurement and documentation. His work demonstrates strong interdisciplinary connections between mechanical engineering, computer vision, and practical applications in both cultural preservation and healthcare. His research has led to significant advancements in 3D scanning technologies, particularly for human body analysis and cultural heritage conservation. His publications reveal a consistent research trajectory focused on improving accuracy, efficiency, and applicability of 3D measurement systems. His scientific impact is evidenced by 159 publications, an h-index of 18 in Scopus and 17 in Web of Science, and 103 promoted theses. His research has been supported by 22 projects, resulting in 1 patent and substantial contributions to both academic knowledge and practical applications. As an academic leader, he has supervised numerous students and researchers, contributing significantly to the development of expertise in optical measurement technologies. His work bridges theoretical research with real-world applications in medical diagnostics, cultural heritage preservation, and industrial measurement systems.

Ayush TewariView profile

Assistant Professor

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

Ayush Tewari is an Assistant Professor at the University of Cambridge. Previously, he was a postdoctoral researcher at MIT CSAIL under Bill Freeman, Josh Tenenbaum, and Vincent Sitzmann, and completed his Ph.D. at the Max Planck Institute for Informatics under Christian Theobalt. His research focuses on visual perception, developing methods to infer 3D structured representations from images and videos, aiming to bridge the gap between human perceptual capabilities and machine learning systems. Key research interests include neural rendering, inverse rendering, 3D reconstruction, and generative models. Notable contributions include advancements in Neural Radiance Fields (NeRF), diffusion models for inverse problems, and human-centric perception studies. His work has been published in top venues such as SIGGRAPH, CVPR, ICCV, and NeurIPS. Recent research trends emphasize ambiguity-aware inverse rendering, stochastic inverse problem solving using diffusion models, and integrating forward models for 3D scene inference. His work on Diffusion with Forward Models (NeurIPS 2023) proposes a novel framework for solving inverse problems without direct supervision. Awards: Best Paper Honorable Mention at BMVC 2022 (VoRF: Volumetric Relightable Faces). Labs/Projects: Core contributor to the DFM (Diffusion with Forward Models) project, advancing 3D scene understanding via probabilistic methods.

Daniel G. AliagaView profile

Associate Professor

• Computer Graphics
• Urban Computing
• Artificial Intelligence
• +4 more

Daniel G. Aliaga is an Associate Professor in the Department of Computer Science at Purdue University, part of the College of Science. His research focuses on urban computing, combining computer graphics, computer vision, and AI to develop tools for urban modeling and simulation. He leads the Computer Graphics and Visualization Laboratory (CGVLAB), pioneering work in inverse procedural modeling and generative AI for urban environments. Aliaga has a PhD from the University of North Carolina at Chapel Hill and has held visiting professorships at ETH Zurich and KAUST. Education: BS (Brown University), MS & PhD (UNC Chapel Hill) Research Areas: Urban Computing, Computer Graphics, AI, Robotics Notable Projects: WUDAPT initiative, urban weather modeling, 3D reconstruction techniques His work spans interdisciplinary collaborations with urban planners, meteorologists, and engineers. Over 150 peer-reviewed publications and $42M in grants highlight his impact. Awards include the Fulbright Scholar Award and Discovery Park Fellowship. Aliaga advises numerous PhD and undergraduate researchers, contributing to startups and patents.

Farida CherietView profile

Professor

• Biomedical technology
• Artificial intelligence
• Medical imaging
• +4 more

Dr. Farida Cheriet is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal . She serves as Director of the 4D Imaging and Vision Laboratory (LIV4D) and holds membership in the Institute of Biomedical Engineering and the Institute for Data Valorization (IVADO) . Her research spans medical imaging , biomedical technology , artificial vision , and 3D movement estimation . Ph.D. (Montreal), DEA (France), Ing. (Algeria) Her work focuses on interpretable AI models for retinal pathology diagnosis, 3D anatomical reconstruction from medical imaging, and automated segmentation of vascular and spinal structures. She leads innovations in deep learning applications for diabetic retinopathy screening, coronary artery analysis , and spine surgery planning . Recent publications highlight her leadership in computer vision for biomedical applications, with 15 featured articles covering ICU readmission prediction, retinal vessel analysis, dental crown design, and spinal ultrasound processing. She received CFI funding (2021) for cutting-edge facilities and collaborates on pan-Canadian ophthalmic image databases (2018). Dr. Cheriet has supervised 28 Ph.D. students and 46 Master's students in biomedical computing, including projects on scoliosis analysis, retinal imaging, and cardiac MRI. Her teaching includes digital communications and computer graphics courses.

Claus Brøndgaard MadsenView profile

Associate Professor

• Computer Vision
• Virtual Reality
• Augmented Reality
• +7 more

Claus Brøndgaard Madsen is an Associate Professor in the Department of Architecture, Design and Media Technology at Aalborg University, under The Technical Faculty of IT and Design. He is based at Campus Aalborg and is affiliated with the Computer Graphics Group and VR Light research environment. His work bridges computer vision, virtual reality, and architectural design, with a strong focus on real-time visualization and human interaction in digital environments. Research Interests: His primary research areas include Computer Vision , Virtual Reality (VR) , Augmented Reality (AR) , 3D Reconstruction , Structure-from-Motion (SfM) , and Image-Based Lighting . These are applied in industrial VR, urban design evaluation, and sensory augmentation systems. His research fingerprint highlights strong engagement with Virtual Reality (100%), Augmented Reality (71%), Illumination Conditions (41%), and Wind Turbine Engineering (28%). The recent publications (2023–2025) reflect a consistent trend in enhancing user experience in VR/AR environments, with emphasis on spatial cognition, locomotion techniques, exposure control, and machine learning integration for lighting classification and part direction prediction in industrial contexts. These works are published in top-tier conferences such as EG, GRAPP, WACV, and CAADRIA. Scientific Awards: No specific awards or fellowships are mentioned in the provided text. Advising and Grants: Madsen has supervised PhD students and served as a supervisor in research projects like AAIVR. He is Principal Investigator (PI) in multiple funded projects, including BUMUS: Sustainable Expansion of Mussel Production (2024–2027), CityVR: Evaluating Parametric City Designs in VR (2020–2022), and V-BRT: Virtual Bus Rapid Transit (2019). These projects reflect substantial grant acquisition and leadership in applied VR research. Labs and Research Groups: He is a key member of the Computer Graphics Group and VR Light at Aalborg University, which focus on real-time visualization, lighting techniques, and immersive technologies for industrial and architectural applications.

Jianke ZhuView profile

Professor

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

Jianke Zhu is a Professor at the College of Computer Science and Technology of Zhejiang University . He obtained his Ph.D. in Computer Science and Engineering from The Chinese University of Hong Kong and conducted postdoctoral research at the BIWI Computer Vision Lab, ETH Zurich . His research focuses on Computer Vision and Machine Learning , with a particular emphasis on 3D scene understanding, LiDAR-based mapping, and neural rendering. Dr. Zhu’s research spans several subfields, including 3D Reconstruction , Semantic Segmentation , Multimodal Learning , and Autonomous Driving . His work integrates Neural Networks , LiDAR Processing , and Uncertainty Quantification to address challenges in real-time and adverse conditions. Selected Recent Trends: 2025 publications highlight his work in Hexagonal Mesh-based Neural Rendering , Instance-aware 3D Scene Understanding , and Efficient Visual Projectors for Multimodal LLMs . Earlier works include Box2Mask for Instance Segmentation (2024) and Token Selection for Point Cloud Learning (2025). Scientific Awards : Senior member of the IEEE Advising and Grants : As a Doctoral Supervisor , he mentors students in advanced topics like LiDAR Odometry and Multi-view Stereo Recovery . His projects have attracted funding for autonomous driving , 3D scene modeling , and neural rendering .

Iliyan GeorgievView profile

Researcher

• Computer Graphics
• Light Transport Simulation
• Monte Carlo Rendering
• +4 more

Iliyan Georgiev is a research scientist at Adobe, specializing in advanced computer graphics and physically based rendering. He holds a Bachelor's degree in Computer Science from Sofia University, Bulgaria, and a Master's degree from Saarland University, Germany, supported by a fellowship from the Max-Planck Institute. His work focuses on improving rendering efficiency through Monte Carlo methods, light transport simulation, and neural rendering techniques. Georgiev's research bridges the gap between theoretical and applied graphics, with contributions to bidirectional rendering algorithms, importance sampling, and 3D scene modeling. His publications highlight innovations in variance reduction, path sampling, and material-aware rendering. He has collaborated with leading institutions and companies, including Intel Visual Computing Institute, Disney Research Zürich, Weta Digital, Chaos Group, and Autodesk. Notable scientific awards include the Best Student Paper Award at ICPRAM 2025 and the Best Paper Award at EGSR 2024.

Diana MarinView profile

Researcher

• Computer Graphics
• Computational Geometry
• Point Cloud Processing
• +3 more

Diana Marin is a PostDoc Researcher at TU Wien's Institute of Visual Computing & Human-Centered Technology. She holds a BSc, MEng, and Dr.techn. (PhD) in technical fields. Her research focuses on computational geometry, point cloud processing, and distributed computing for large-scale datasets. She has contributed to projects like Distributed Surface Reconstruction and RE:STOCK INDUSTRY. Education: BSc, MEng, Dr.techn. (PhD) Her work emphasizes curve and surface reconstruction from unorganized point clouds, leveraging proximity graphs and distributed computational methods. Key projects include optimizing surface reconstruction for massive datasets and developing parameter-free algorithms for connectivity analysis. Her publications span topics like SING neighborhood graphs, Riemannian manifold curve reconstruction, and distributed processing techniques. She collaborates on projects such as PostDisaster and Mixed Reality Lab.

Xianzhi LiView profile

Associate Professor

• 3D Vision
• Computer Graphics
• Point Cloud Processing
• +3 more

Xianzhi Li is an Associate Professor at the School of Computer Science and Technology, Huazhong University of Science and Technology (HUST). Before joining HUST, he was a postdoctoral fellow at The Chinese University of Hong Kong (CUHK) and the Hong Kong Center for Logistics Robotics. He earned his Ph.D. in Computer Science and Engineering from CUHK under Professors Pheng-Ann Heng and Chi-Wing Fu, preceded by an M.Sc. in Biomedical Engineering (CUHK, 2015) and a B.Eng. in Biomedical Engineering from Sichuan University (2014). His research focuses on 3D vision, computer graphics, point cloud processing, and deep learning, with a specific emphasis on designing machine learning algorithms for 3D data analysis. Recent work includes advancements in 3D point cloud analysis, semantic segmentation, and sim-to-real robotics applications. Key research trends include the integration of large language models with 3D data (e.g., MiniGPT-3D), open-world semantic segmentation (PDF), and joint 2D-3D learning frameworks (Joint-MAE). His contributions span robotics, computer vision, and graphics, with notable applications in industrial bin picking and 3D reconstruction. Awards: Teaching Assistant of Merit (2018), Biomedical Engineering Scholarship (2015), and National Scholarship in China (2013). Teaching: Courses include Computer Vision (Spring 2022), Advanced Topics in Computer Graphics and Visualization, and Principles of Computer Graphics.

Rishabh DabralView profile

Researcher

• 3D Computer Vision
• Computer Graphics
• Multi-Modal Interactions
• +6 more

Rishabh Dabral is a Research Group Leader at the Max Planck Institute for Informatics since August 2024, leading the "3D Visual Intelligence" group. He is also affiliated with the Research Training Group on Neuro-Explicit Models of Language, Vision, and Action at Saarland University. Expertise: 3D computer vision, computer graphics, human-object interaction modeling, and motion synthesis. Leadership: Conducts cutting-edge research on 3D human performance capture and physical plausibility in motion. His research focuses on: 3D human pose estimation under gravity constraints Multi-modal gesture synthesis using neural architectures Quantum auto-encoding for 3D representations Wearable robotics informed by human behavior Temporal dynamics in human-object interaction Recent publications at top venues like SIGGRAPH , CVPR , and ICCV demonstrate his work on: Music-driven motion synthesis Egocentric motion capture systems Reactive two-person interaction models Diffusion-based gesture generation Object-aware motion prediction Wearable robotic limb design

• Virtual Reality
• Human-Computer Interaction
• Collaborative Virtual Environments
• +4 more

Thierry Duval is a Professor in the Department of Computer Science (INFO) at IMT Atlantique , Brest campus. His work focuses on Virtual Reality (VR) , Human-Computer Interaction (HCI) , and 3D Interaction in collaborative environments.

Michael WimmerView profile

Professor

• Computer Graphics
• Real-Time Rendering
• Procedural Modeling
• +4 more

Michael Wimmer is a Full Professor at TU Wien, leading the Rendering and Modeling Group and directing the Center for Geometry and Computational Design. He holds a M.Sc. (1997) and Ph.D. (2001) from TU Wien. His research focuses on real-time rendering, procedural modeling, computational design, and point-based graphics. He has co-authored over 200 papers and the book *Real-Time Shadows*. He serves as Co-Editor-in-Chief of Computer Graphics Forum , chairs SIGGRAPH Asia 2025, and received the Eurographics Outstanding Technical Contributions Award (2023). Education: M.Sc. in Computer Science (1997), TU Wien Ph.D. in Computer Science (2001), TU Wien Research Interests: Real-time rendering and visualization Procedural modeling and computational design Point-based graphics and neural rendering Applications in computer games and urban environments Awards: Eurographics Outstanding Technical Contributions Award (2023) Wolfgang Straßer Award (Best Paper, 2022) Eurographics Fellow (2018) Outstanding Service Award (2012) Advising & Grants: Coordinator of the Special Research Programme Advanced Computational Design Key researcher at VRVis Research Center Labs & Teams: Rendering and Modeling Group Center for Geometry and Computational Design