Find Academic Supervisors

Ruihui LiView profile

Professor

• Deep Geometry Learning
• Generative modeling
• 3D Vision
• +3 more

Ruihui Li - Academic Summary Professional Overview Prof. Ruihui Li is a faculty member at the College of Computer Science and Electronic Engineering, Hunan University , specializing in Deep Geometry Learning , Generative Modeling , and 3D Vision . His work focuses on advancing 3D reconstruction and generation with high controllability, particularly in applications like point cloud upsampling and neural rendering. Education & Career Dr. Li earned his Ph.D. in 2021 (oral defense passed in June 2021). His career includes significant contributions to research in geometric deep learning and 3D computer graphics. He actively collaborates with institutions like the Chinese University of Hong Kong and Tel Aviv University on projects such as PU-GAN and SP-GAN . Research Interests His research spans cutting-edge topics in: Neural Wavelet-Domain Diffusion for 3D Shape Generation Topology-Aware 3D Mesh Reconstruction Camera-Based 3D Scene Completion Point Cloud Analysis and Upsampling Professional Activities Dr. Li has delivered invited talks at prestigious events such as the Asia Graphics Webinar and Peking University . He serves as a reviewer for top conferences (CVPR, ICCV, ECCV) and journals (TPAMI, TVCG). His research is supported by grants from the National Natural Science Foundation of China and the Chinese Academy of Sciences. Technical Contributions Key projects include: PU-GAN : A point cloud upsampling adversarial network (ICCV 2019). SP-GAN : Sphere-guided 3D shape generation (SIGGRAPH 2021). Neural Wavelet-domain Diffusion : 3D shape generation and manipulation (TOG 2024). Labs & Teams He leads a research group at Hunan University, mentoring Ph.D./MPhil students, RAs, and postdocs. His team focuses on interdisciplinary projects combining AI, computer graphics, and geometric deep learning.

Oscar Mendez MaldonadoView profile

Lecturer

• Robotics
• Artificial Intelligence
• Computer Vision
• +3 more

Oscar Mendez Maldonado is a Lecturer in Robotics and Artificial Intelligence at the University of Surrey's School of Computer Science and Electronic Engineering, affiliated with the Robotics Department and CVSSP Centre. He holds a PhD (2018) and BEng (2013) from the University of Surrey. His research focuses on Machine Learning, Computer Vision, and Robotics, with emphasis on autonomous systems, localisation, and SLAM applications. Key projects include the Autonomous Valet Parking (AVP) system for indoor navigation and the SMILE project for sign language assessment using AI. He has supervised students like James Ross (Autonomous Vehicles), Xihan Bian (Reinforcement Learning), and Nimet Kaygusuz (Visual Odometry). Notable achievements include the Sullivan Thesis Prize (2018) and impactful publications in IEEE conferences (e.g., ICRA, CVPR, IROS). Research spans topics like 3D hand pose estimation via diffusion models, graph-based visual odometry fusion, and Raman spectroscopy for localisation. He contributes to open-source tools (e.g., RaSpectLoc GitHub) and collaborates with industry partners like Parkopedia. His work bridges theoretical advances with real-world applications in autonomous systems and healthcare.

Hannu HyyppäView profile

Researcher

• Laser Scanning
• 3D Modeling
• Geoinformatics
• +5 more

Hannu Hyyppä is a Research Director and Project Employee at Aalto University's Department of Built Environment, affiliated with the MeMo research group. He leads the Research Institute of Measuring and Modelling for the Built Environment, focusing on advanced laser scanning, 3D modeling, and geoinformatics. His work spans interdisciplinary collaborations across engineering, geography, and arts, with a strong emphasis on applications in cultural heritage preservation, urban planning, and environmental monitoring. Education: Doctoral degree (D.Sc.) in Engineering and Technology, Helsinki University of Technology (2000) Licentiate degree in Engineering and Technology, Helsinki University of Technology (1989) Master's degree in Engineering and Technology, Helsinki University of Technology (1986) Research Interests: Laser scanning technologies, point cloud utilization in forestry and urban mapping, virtual reality for cultural heritage, and sustainable infrastructure modeling. His expertise includes photogrammetry, geographic information systems (GIS), and decision support systems for environmental management. Recent Contributions: Over 550 publications and 30+ active projects, including the Centre of Excellence in Laser Scanning Research (2014-2019) and the Pointcloud project (2015-2021). His work advances applications in autonomous road inspection, 3D cultural reconstructions, and smart city technologies. Awards: Recipient of the 2019 Kansallinen avoimen tieteen palkinto for innovative open science contributions. Grants & Leadership: Principal Investigator for projects like DICA (Digital Cultural Heritage) and ToToRo (Automatic Road Inspection). Active in organizing workshops and international conferences on 3D technologies and laser scanning. Labs/Teams: Oversees the MeMo group and collaborates with national organizations like the Finnish Geospatial Research Institute. Develops tools for real-time 3D mapping and virtual environments.

Minchen LiView profile

Assistant Professor

• Computer Graphics
• Machine Learning
• Robotics
• +4 more

Minchen Li Assistant Professor at Carnegie Mellon University's School of Computer Science (Computer Science Department). Formerly an Assistant Adjunct Professor at UCLA's Mathematics Department. Holds a Ph.D. from the University of Pennsylvania's SIG Center for Computer Graphics, followed by a postdoctoral position there. Research focuses on physics-based simulation, integrating numerical analysis, high-performance computing, and machine learning. Notable contributions include the IPC method for frictional contact simulation and large-scale material point methods. Education Ph.D., Computer and Information Science, University of Pennsylvania (2020) M.Sc., Computer Science, University of British Columbia (2018) B.Eng., Computer Science and Technology, Zhejiang University (2015) Research Interests Advances in physical simulation for visual computing, robotics, and manufacturing. Specializes in robust and efficient methods for solid/fluid dynamics, contact modeling, and GPU acceleration. Combines numerical analysis with machine learning to address challenges in simulation accuracy and versatility. Awards 2021 ACM SIGGRAPH Outstanding Doctoral Dissertation Award 2024 SCA Early Career Researcher Award Advising & Labs Leads the Simulation Intelligence Group (SIG) at CMU Graphics Lab. Advises PhD students Guying Lin, Juntian Zheng, Michael Liu, and Zhaofeng Luo. Collaborates with industry and academic partners on projects like VR-based modeling systems and scalable simulation frameworks.

Prabhakara AkarshView profile

Assistant Professor

• Wireless Systems
• Cyber-Physical Systems
• Machine Learning for RF
• +6 more

Akarsh Prabhakara is an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin–Madison, with an additional affiliation in the Department of Electrical and Computer Engineering. He earned his Ph.D. from Carnegie Mellon University in 2024, where he worked under Professors Anthony Rowe and Swarun Kumar. Ph.D., Electrical and Computer Engineering, Carnegie Mellon University, 2024 B.Tech, Electronics and Communication Engineering, National Institute of Technology Karnataka, 2018 His research focuses on building high-fidelity wireless systems for perception and communication, particularly in cyber-physical and robotic applications. He explores machine learning-driven RF systems, novel communication paradigms, wireless-robotics integration, and embedded wireless sensing. His work aims to enable robust perception in challenging environments such as smoke or fog using millimeter wave radar and deep learning. His recent publications in CVPR, ICRA, MobiCom, and ICCV demonstrate a strong trend in using neural methods for radar simulation, super-resolution, and wireless intelligence. Key themes include implicit neural rendering for radar, end-to-end learning for perception, and high-resolution point cloud generation from low-cost sensors. His scientific contributions have been recognized through publications in top-tier venues, though specific awards are not mentioned in the provided text. He is actively involved in mentoring and recruiting students for research in wireless and robotics. He teaches courses such as Intro to Computer Networks and Big Ideas in Wireless: Perception and Communication . He leads research projects like RadarHD, which enables lidar-like perception from mmWave radar, and is developing tools and datasets for community use. His lab emphasizes practical, real-world applications of wireless systems in robotics and autonomous systems.

Yao LiuView profile

Assistant Professor

• Immersive video streaming
• Virtual/augmented/mixed reality
• Multimedia systems
• +4 more

Dr. Yao Liu is an Assistant Professor in the Department of Electrical and Computer Engineering at Rutgers University, New Brunswick, since Fall 2021. Previously, she held an Associate Professor (tenured) position at Binghamton University, SUNY. Her research focuses on immersive streaming technologies, including 360-degree and volumetric video delivery, edge/cloud computing, and distributed systems. She has led projects such as SGSS for 6-DoF navigation in 3DGS scenes and EVASR for edge-based video enhancement. Her work has been recognized with awards like the NSF CAREER Award and Best Paper Awards at MMSys (2017, 2020). Research interests include immersive video streaming, virtual/augmented reality, mobile systems, and network optimization. Notable contributions include the 👁️NavGS dataset for VR navigation and the Dynamic 6-DoF Volumetric Video toolkit. She advises PhD students like Mufeng Zhu and Na Li, with past advisees receiving accolades such as the Binghamton Distinguished Dissertation Award. Publications span conferences like ACM Multimedia Systems (MMSys), IEEE ICME, and AAAI. Her work emphasizes practical solutions for bandwidth efficiency, real-time streaming, and energy optimization in immersive media. Grants include NSF CAREER funding for immersive streaming research. Labs and collaborations involve open-source projects hosted on GitHub (e.g., symmru repositories), emphasizing reproducibility and accessibility.

• Network engineering
• Multimedia Communication
• Distributed Multimedia Systems
• +4 more

Prof. Hermann Hellwagner is a Full Professor at the Department of Information Technology, University of Klagenfurt. He has held roles such as Vice President (Natural and Technical Sciences) at the Austrian Science Fund (FWF) and Vice Dean of the Faculty of Technical Sciences. His research focuses on multimedia communication, network engineering, and future internet architectures. Notable projects include work on adaptive streaming, edge computing, and drone networks. He holds a Ph.D. in Systolic Architectures from the University of Linz (1988). Research interests span distributed multimedia systems, information-centric networking (ICN), and optimizing video streaming quality-of-experience (QoE). Recent work emphasizes edge computing solutions for low-latency streaming and dynamic codec adaptation. His contributions include frameworks like ALPHAS and MEDUSA for bitrate optimization, and studies on point cloud streaming in augmented reality. Publications (2021–2025) highlight advancements in edge-assisted streaming, hybrid P2P-CDN architectures, and transcoding techniques. His work often bridges theoretical models with real-world implementations, addressing challenges in latency, cost, and device adaptability. Current projects involve 6DoF video streaming and multi-robot system optimization. Labs/Teams: Part of the Institute of Information Technology (ITEC), Klagenfurt. Collaborates on EU-funded projects and industry partnerships in 5G edge computing and drone networks. Active in standards groups for HTTP adaptive streaming protocols.

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.

• 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.

• Nonlinear Filtering
• Multi-Object Tracking
• Deep Machine Learning
• +3 more

Lennart Svensson is a Professor at Chalmers University of Technology in the Signal Processing research group. His work focuses on nonlinear filtering, multi-object tracking, Bayesian statistics, and deep machine learning with applications in autonomous systems and sensor fusion. Research Interests Nonlinear Filtering and Bayesian Inference Multi-Object Tracking and Sensor Fusion Deep Learning for Autonomous Systems Performance Metrics (GOSPA, T-GOSPA) Lidar-Camera Fusion and Radiance Fields 5G SLAM and mmWave Sensing Publications Trends Recent work emphasizes uncertainty-aware multi-object tracking metrics, trajectory estimation using Poisson Multi-Bernoulli Mixtures, and sensor fusion techniques for autonomous driving. His research integrates Bayesian methods with deep learning for applications in automotive radar, lidar, and 5G positioning systems. Contact Email: lennart.svensson@chalmers.se

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.

• Signal Processing
• Bayesian Statistics
• Deep Learning
• +4 more

Lars Hammarstrand is an Associate Professor at Chalmers University of Technology specializing in the Signal Processing research group. His work integrates model-based Bayesian statistics with deep machine learning for applications in visual localization, sensor fusion, and autonomous systems, with emphasis on robustness in real-world environments. His research focuses on bridging Bayesian inference and deep learning to solve challenges in autonomous vehicle perception. Key areas include visual localization under appearance changes, radar-camera sensor fusion, and out-of-distribution detection for safety-critical systems. Recent work explores neural radiance fields for radar, semi-supervised learning for mapping, and probabilistic hierarchical classification to address real-world uncertainties in autonomous driving. Analysis of his 2020-2025 publications reveals a trajectory toward unifying geometric and semantic understanding in autonomous systems. His work demonstrates increasing integration of neural radiance fields with traditional filtering techniques, while advancing open-set recognition capabilities. Notable contributions include road geometry estimation frameworks, extended object tracking with PHD filters, and methods to mitigate data leakage in localization benchmarks. No scientific awards were mentioned in the provided materials. Hammarstrand has contributed to academic supervision methodology through his publication on improving master's thesis supervision efficiency, though specific student names are not listed. The provided information contains no details about research grants or funding sources. He operates within Chalmers University's Signal Processing research group, which develops advanced algorithms for automotive perception systems, focusing on sensor fusion techniques that combine radar, camera, and motion data for robust environmental understanding in autonomous vehicles.

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.