جست‌وجوی استاد و پژوهشگر

• Computer Vision
• Deep Learning
• Robotics
• +۷ مورد دیگر

Dr. João Henriques is a Research Fellow of the Royal Academy of Engineering (RAEng) at the Visual Geometry Group (VGG), University of Oxford. His research focuses on advancing computer vision, deep learning, and robotics, particularly in areas like 3D scene understanding, reinforcement learning, and multi-agent systems. He is renowned for developing the KCF and SiameseFC visual trackers, which won the VOT Challenge and are deployed in consumer hardware. His work spans 3D geometry, self-supervised learning, causal inference, and neuro-symbolic systems. Key contributions include methods for egocentric video analysis, unsupervised reconstruction, and robot navigation. He leads the VGG's research on neural feature fields, hierarchical scene understanding, and real-time 3D perception. Recent publications emphasize 3D-aware segmentation, universal place recognition, and neuro-symbolic world modeling for robotics. His research often bridges theoretical guarantees with practical applications, such as medical imaging and autonomous systems. Dr. Henriques collaborates with industry and academia on AI ethics, friendly AI, and interpretable learning. His lab hosts DPhil students advancing creative AI applications, such as generative models for gameplay design and LLM evaluations in real-world editorial workflows.

• Computer Vision
• 4D Vision
• Machine Learning
• +۹ مورد دیگر

Professor Adrian Hilton is a distinguished faculty member at the University of Surrey, serving as Director of the Centre for Vision, Speech and Signal Processing (CVSSP) and Director of the Surrey Institute for People-Centred AI. He is affiliated with the School of Computer Science and Electronic Engineering and leads the Visual Media Research Lab (V-Lab). His research focuses on pioneering next-generation 4D computer vision technologies that enable machines to understand and model dynamic real-world scenes. Key areas include 3D/4D shape capture, computer vision, machine learning, graphics, and animation for applications in sports analysis, film/TV production, virtual reality, and medical imaging. His work bridges the gap between real and computer-generated imagery, with notable contributions in volumetric capture, motion capture, and free-viewpoint video. Hilton's recent publications demonstrate a strong trend toward multimodal integration, particularly combining audio and visual processing for spatial audio applications, while advancing 4D reconstruction techniques for human performance capture. His work increasingly incorporates transformer architectures and neural rendering techniques for improved illumination estimation, shadow modeling, and multi-view consistency. Scientific Awards and Recognition Two EU IST Innovation Prizes Manufacturing Industry Achievement Award Royal Society Industry Fellowship (2008-2011) Royal Society Wolfson Research Merit Award in 4D Vision (2013-2018) Fellow of the Royal Academy of Engineering (FREng) Fellow of the International Association for Pattern Recognition (FIAPR) Fellow of the Institution of Engineering and Technology (FIET) Hilton actively mentors PhD and post-doctoral researchers through his leadership of CVSSP, which has a grant portfolio exceeding £31M and comprises 170 researchers. He has successfully commercialized several technologies, including systems used by the BBC for sports commentary visualization. His research collaborations span major industry partners including BBC, BT, Sony, Framestore, and The Foundry. He co-founded the G3 Games forum and the CVMP Conference on Visual Media Production, demonstrating strong engagement with the creative industries. Current research projects include the S3A Programme Grant in Future Spatial Audio and InnovateUK's ALIVE project for 360 video reconstruction.

Liu Hongyanمشاهده پروفایل

استاد

• Big data management and analysis
• machine learning
• data mining
• +۷ مورد دیگر

Professor Liu Hongyan is a full-time Professor in the Department of Management Science and Engineering at Tsinghua University's School of Economics and Management, where he has served since 1994, achieving the rank of Professor in 2011 after previously holding positions as Associate Professor (2003-2011) and Teacher. His research bridges theoretical data science with practical applications across e-commerce, healthcare, and social media platforms. Education: PhD in Management, School of Economics and Management, Tsinghua University (2001) His research focuses on big data management , machine learning , and business intelligence with specialized expertise in personalized recommendation systems , medical/financial data analysis , and computer vision applications . Recent work integrates large language models and causal inference to solve complex problems in short video platforms, live streaming, and healthcare analytics, emphasizing real-world impact through industry collaborations. Analysis of his 15 most recent publications (2023-2025) reveals a strong trajectory toward multimodal AI systems combining recommendation engines with computer vision, particularly in 3D animation for advertising and healthcare. Key trends include LLM-enhanced display advertising, emotion-aware facial animation, and medical image annotation using adversarial learning, while maintaining core contributions to behavioral data mining in social networks. Scientific recognition includes: National Archives Administration's Outstanding Scientific and Technological Achievement Award Multiple Best Paper Awards at international conferences Outstanding Doctoral Dissertation Supervisor designation from the Society for Management Science and Engineering Special Award for National Natural Science Foundation project on user behavior pattern discovery Professor Liu has secured leadership roles in major National Natural Science Foundation projects including Innovation Research Groups and international cooperation initiatives. His industry impact is demonstrated through patented recommendation systems adopted by multiple companies, particularly in personalized content delivery for live streaming and short video platforms. As an Outstanding Doctoral Dissertation Supervisor, he mentors the next generation of data science researchers. He serves as Deputy Director of Tsinghua University's Center for Artificial Intelligence and Management Research and holds key positions in national academic societies including the E-Commerce and Cyberspace Management Committee (China Management Modernization Research Association) and the Information Systems Engineering Committee (Chinese Society for Systems Engineering).

Agnieszka Roginskaمشاهده پروفایل

استاد

• Immersive Audio
• 3D Audio Technology
• Spatial Sound
• +۵ مورد دیگر

Agnieszka Roginska is a Professor of Music Technology at the Steinhardt School, New York University, specializing in immersive audio, spatial sound, and auditory displays. Her work integrates acoustic science with virtual reality and medical applications, including postural stability studies for vestibular rehabilitation. She holds leadership roles as AES President-Elect and co-edited the authoritative book 'Immersive Sound'. Education: B.A. in Piano Performance and Computer Applications in Music (McGill University, 1996); M.M. in Music Technology (NYU, 1998); Ph.D. in Music Technology (Northwestern University, 2004). Research focuses on 3D audio technologies, auditory displays for virtual environments, and sensory integration studies. She leads NYU's Music and Audio Research Lab (MARL) and advises the Society for Women in TeCHnology (SWiTCH) at NYU. Awards include AES Fellowship and leadership in international audio engineering societies. Recent publications emphasize audio's role in postural control, distributed music performance frameworks (Holodeck), and VR sound design. Her work bridges technical innovation with artistic applications, including collaborative music systems in mixed reality.

Fangneng Zhanمشاهده پروفایل

پژوهشگر ارشد

• Computer Science
• Artificial Intelligence
• Neural Rendering
• +۷ مورد دیگر

Currently a Research Fellow at Harvard University & MIT , Fangneng Zhan specializes in Neural Rendering and Generative AI . His research focuses on developing evolutive rendering frameworks, 3D-aware generative models, and multimodal synthesis techniques. Previously, he was a postdoctoral researcher at the Max Planck Institute for Informatics under Prof. Christian Theobalt. He earned his Ph.D. in Computer Science & Engineering from Nanyang Technological University, Singapore and a Bachelor's in Communication Engineering from the University of Electronic Science and Technology of China . His work spans 3D reconstruction, robotics applications , and lighting estimation , with significant contributions to SIGGRAPH , NeurIPS , and CVPR conferences. Recent research highlights include evolutive gauge transformations for neural fields, generalizable 3D style transfer via Gaussian splatting, and multimodal synthesis frameworks leveraging pre-trained models like CLIP and Stable Diffusion. He has co-authored Top50 Popular Paper in TPAMI 2023 and organized workshops at CVPR 2024 on generative models. Scientific Awards: Top50 Popular Paper, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2023 Collaborative Network: Mentored students at institutions like Harvard, NTU, and ETH Zurich. His projects include datasets for lighting estimation and real-time scene text detection systems.

Johan Pauwelsمشاهده پروفایل

مدرس

• Audio Signal Processing
• Music Information Retrieval
• Machine Learning for Audio
• +۳ مورد دیگر

Dr. Johan Pauwels is a Lecturer in Audio Signal Processing at Queen Mary University of London's School of Electronic Engineering and Computer Science, where he is affiliated with the Centre for Digital Music and the Centre for Multimodal AI. His educational background includes: Master of Science in Electrical/Electronics Engineering from KU Leuven (2006) Master of Science in Artificial Intelligence from KU Leuven (2007) PhD from Ghent University (2016) on automatic harmony recognition from audio Johan's research focuses on making machines understand audio to the level of a trained professional. His work combines machine learning, signal processing, data science, and music theory to develop tools for musicians, listeners, and music learners. He has been working on narrowing the gap between academic research and user-centric applications, web-based music services, and the personalization of spatial and immersive audio. His specific interests include machine learning for audio, audio signal processing, music information retrieval, and binaural audio. His recent publications show a strong focus on music representation learning, with particular attention to limited data scenarios, multimodal approaches, and spatial audio processing. His work bridges theoretical music concepts with practical machine learning applications, especially in chord recognition, beat detection, and instrument recognition. He has made significant contributions to HRTF (Head-Related Transfer Function) research and development of tools for spatial audio processing. Dr. Pauwels is actively involved in research funding, with current grants including the AIM CDT Internship with Sofilab (2025), AIM CDT Studentship - Stem (2024), and AIM CDT Internship with stem.tech (2024). He currently supervises multiple PhD students, primarily through the UKRI Doctoral School in AI and Music, with research topics spanning intelligent audio editing, neural drum synthesis, source separation, graph neural networks for music recommendation, and more. In addition to PhD supervision, he typically guides 8-10 undergraduate and 8-10 master's students through their final year projects. His teaching responsibilities include ECS7013P Deep Learning for Audio and Music (MSc/PhD level) and ECS411U Signals and Information (first-year undergraduate).

Rishabh Dabralمشاهده پروفایل

پژوهشگر

• 3D Computer Vision
• Computer Graphics
• Multi-Modal Interactions
• +۶ مورد دیگر

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

Kartic Subrمشاهده پروفایل

مدرس ارشد

• Computer Graphics
• Monte Carlo Rendering
• Physics-based Simulation
• +۱۳ مورد دیگر

Kartic Subr is a Senior Lecturer (equivalent to Associate Professor) in the School of Informatics at the University of Edinburgh, where he conducts research at the intersection of computer graphics, machine learning, and robotics. He is affiliated with the Institute of Perception, Action and Behaviour and leads an active research group in physics-aware simulation and AI for scientific discovery. His research interests span Computer Graphics , Monte Carlo Rendering , Physics-based Simulation , Machine Learning , Robotics , and Computational Biology . He has made significant contributions to rendering theory, light transport analysis, BRDF modeling, and simulation of fluids and fractures. Recently, his group has applied deep learning to protein design, achieving notable success in international competitions. The recent publications reflect a strong trend toward interdisciplinary research, combining computer graphics with AI and biology. Key themes include explainable AI for physics-aware interfaces (e.g., CueTip), neural rendering (NeRF), protein design with CNNs, and reward learning in robotics. His group regularly publishes in top venues such as SIGGRAPH, NeurIPS, and IEEE RA-L. Royal Society University Research Fellowship (2014–2024) Lead, GCRF Impact Accelerator (2019) Lead, EPSRC First Grant (2017) Royal Society Challenge Grant (2016) Royal Society Newton International Fellow (2010) Marie-Curie Visitor (2006) Best Paper Honorable Mention, I3D 2012 Best Paper, I3D 2011 Kartic Subr actively supervises PhD students and postdocs, including Leonardo Castorina, Zhiyuan Zhang, and Kevin Denamganaï. Former students have gone on to positions at DeepMind, University of Edinburgh, and industry. He has secured multiple grants, including from the Royal Society and EPSRC. His research group collaborates across disciplines, particularly with biological sciences on protein design. He leads projects on approximate physics for robotics, protein design via deep learning, and interactive rendering systems. The group has developed frameworks like TIMED for de novo protein design and tools for explainable physics-aware assistants.

Jonas Braaschمشاهده پروفایل

استاد

• Acoustic Virtual Reality
• Binaural Hearing
• Auditory Modeling
• +۳ مورد دیگر

Jonas Braasch is a Professor at Rensselaer Polytechnic Institute's School of Architecture and Associate Director for Research at the Experimental Media and Performing Arts Center (EMPAC). He teaches in the Graduate Program in Architectural Acoustics and leads research on collaborative virtual reality, binaural hearing, auditory modeling, and sensory substitution devices. His work has been funded by NSF, NSERC, and European agencies, and he holds dual PhDs in Electrical Engineering/Information Science and Musicology. His research integrates acoustic virtual reality systems, immersive museum soundscapes, telematic music, and assistive instrument design. Recent publications focus on networked immersive environments, spatial audio techniques, museum acoustics classification, and innovations in wind instrument design for accessibility. Publications emphasize the application of deep learning to acoustic prediction and multisensory integration.

• Image and Video Processing
• Multimedia Analysis
• Computer Vision
• +۵ مورد دیگر

Prof. Tan Yap Peng is a Professor and Chair of the School of Electrical & Electronic Engineering at Nanyang Technological University (NTU), Singapore. He holds the President's Chair in Electrical and Electronic Engineering and serves as Associate Vice President (Lifelong Learning – Postgraduate Programmes by Coursework). His research focuses on multimedia analysis, computer vision, machine learning, and data analytics. He earned his B.S. from National Taiwan University and M.A./Ph.D. from Princeton University. He has led major initiatives including the INFINITUS Infocomm Research Centre and contributed to IEEE technical committees. His over 200 publications span image/video processing, neural network robustness, and cross-modal systems. Awards include IEEE Fellow status. Education: B.S. Electrical Engineering (NTU), M.A./Ph.D. (Princeton) Research interests emphasize interactive digital media, content-based analysis, and AI-driven solutions for visual and signal processing. His work addresses challenges in adversarial attacks, video generation, and low-light image enhancement. He has held editorial roles at IEEE Transactions and EURASIP journals. Conference leadership includes chairs for ICME and ICIP. His contributions bridge academia and industry through collaborative research networks.

George Drettakisمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Rendering
• Image-Based Rendering
• +۱۲ مورد دیگر

George Drettakis is a Senior Researcher at INRIA Sophia-Antipolis and leads the GRAPHDECO research group. He has held professorial roles at institutions including MIT, University of Reims, University of Toronto, and École Normale Supérieure. His research focuses on rendering for computer graphics and sound, with emphasis on image-based rendering, perceptual rendering, and audio-visual cross-modal effects. He has also explored interactive illumination, shadows, relighting, and generative models. Current Students: G. Kopanas (Neural Rendering), N. Violante (Generative Models), A. Petitjean (co-supervised), Y. Poirier-Ginter (co-supervised), P. Panantonakis (starting fall 2023). Postdoctoral Researchers: A. Gauthier at INRIA. His recent work includes 3D Gaussian Splatting , Diffusion-based Relighting , and Neural Radiance Fields . He has received the Eurographics Outstanding Technical Contributions Award (2007) and was named an Eurographics Fellow . He manages projects like ERC Advanced Grant FUNGRAPH and has participated in H2020 EMOTIVE , ANR SEMAPOLIS , and CROSSMOD . His group collaborates internationally and has hosted researchers from institutions such as UC Berkeley, Imperial College London, and TU Wien.

Timo Gerkmannمشاهده پروفایل

استاد

• Statistical Signal Processing
• Machine Learning
• Speech and Audio Processing
• +۳ مورد دیگر

Prof. Timo Gerkmann is a Professor at the University of Hamburg's Department of Informatics, leading the Signal Processing Research Group. His research focuses on statistical signal processing and machine learning for speech and audio applications, including communication devices, hearing aids, audiovisual media, and human-machine interfaces. He previously held roles at Technicolor Research & Innovation, KTH Royal Institute of Technology, and Siemens Corporate Research. His work emphasizes generative models, diffusion-based approaches, and acoustic signal enhancement. He currently serves as Senior Area Editor of the IEEE/ACM Transactions on Audio, Speech, and Language Processing. Research Interests: Statistical Signal Processing for Speech and Audio Machine Learning Applications in Acoustic Environments Diffusion Models for Audio Restoration Audio-Visual Speech Enhancement Human-Machine Interaction Systems Acoustic Scene Analysis Publications Highlight Trends: Recent works focus on diffusion models for speech enhancement, generative approaches to dereverberation, and audiovisual multimodal analysis. He has pioneered frameworks like ReverbFX datasets and FlowDec codecs, emphasizing perceptual quality and unsupervised domain adaptation. Advising & Grants: While no specific students or grants are listed, his research group actively publishes in top venues, indicating sustained academic contributions. His work bridges theoretical signal processing with applied systems engineering. Labs/Teams: Leads the Signal Processing (SP) Research Group at UHH, specializing in cutting-edge audio technologies and human-centric signal processing solutions.

Olivia Wilesمشاهده پروفایل

پژوهشگر

• Computer Vision
• Machine Learning
• Adversarial Robustness
• +۴ مورد دیگر

Olivia Wiles is a Senior Researcher at DeepMind, focusing on adversarial robustness, distribution shift, and computer vision. She earned her DPhil from the University of Oxford under Andrew Zisserman in the Visual Geometry Group (VGG), following a Computer Science degree at the University of Cambridge. Her work spans view synthesis, self-supervised learning, and robust model design. Education : DPhil (Oxford), Computer Science (Cambridge) Key Collaborators : Georgia Gkioxari, Justin Johnson, Richard Szeliski (FAIR), Andrew Zisserman Current Role : Senior Researcher at DeepMind Her research emphasizes adversarial robustness , 3D reconstruction , and self-supervised learning , with notable contributions to view synthesis (SynSin), image matching (Co-Attention), and robustness under distribution shifts. Publications at CVPR, NeurIPS, and ECCV highlight her work in generative models, physical prediction, and multi-view geometry. Scientific Awards : Best Poster, BMVC 2017 Best Paper, NeurIPS ML Safety Workshop 2022 Outstanding Reviewer, ECCV 2020, ICCV 2020/2021 Olivia contributes to academia via community service, including roles as Area Chair (CVPR, ICCV) and reviewer for top-tier conferences (NeurIPS, SIGGRAPH) and journals (PAMI). Her Google Scholar profile reveals ongoing work in unsupervised physics modeling , GANs , and intuitive physics from visual data.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Tengda Han is a Research Scientist at Google DeepMind , specializing in neural networks for video understanding. He earned his PhD from the University of Oxford under Andrew Zisserman and a BEng in Mechanical & Material Engineering from the Australian National University . Prior to his PhD, he studied business administration and law at Renmin University of China for one year. PhD in Computer Science, University of Oxford (2022) BEng in Mechanical & Material Engineering, Australian National University (2016) One year of business administration and law at Renmin University of China His research focuses on video representation learning , self-supervised methods , multimodal learning , and efficient prompting techniques . He has pioneered training-free frameworks for audio description, zero-shot object counting, and hyper-realistic movie generation. Recent publications demonstrate trends in temporal alignment , discriminative prompting , 5D video modeling , and open-world counting . His work often bridges computer vision with natural language processing and speech recognition . Scientific Awards : Best Paper Award at ACCV2024 Best Poster Award at BMVC2023 BMVA Sullivan Doctoral Thesis Prize Runner-up He has co-organized workshops at ICCV25 , NeurIPS23 , and BMVA Symposium on Vision & Language . Collaborators include Andrew Zisserman, Dima Damen, and Flamingo project members at DeepMind.

• Computer Science
• Artificial Intelligence
• Deep Learning
• +۴ مورد دیگر

Tengda Han is a research scientist at Google DeepMind , focusing on video understanding and visual-language models . He previously completed his PhD at the University of Oxford under Andrew Zisserman and earned a Bachelor of Engineering from Australian National University in mechanical & material engineering, with prior studies in business administration and law at Renmin University of China . Research Interests: His work explores neural networks for video analysis, including self-supervised learning , video captioning , object counting , and prompt engineering . Key projects include AutoAD for movie description, Temporal Alignment Networks , and Dense Predictive Coding for video representation. Scientific Awards: Best Paper Award at ACCV 2024 Best Poster Award at BMVC 2023 BMVA Sullivan Doctoral Thesis Prize Runner-up Collaborations & Students: He has collaborated with researchers like Andrew Zisserman , Max Bain , and Arsha Nagrani , and mentored students Junyu Xie , Toby Perrett , and Niki Amini-Naieni on projects including Shot-by-Shot and Unique Video Captioning .