Jia Deng is a Professor of Computer Science at Princeton University and directs the Princeton Vision & Learning Lab. His research focuses on computer vision, machine learning, and robotics, with an emphasis on advancing 3D vision and synthetic data generation. Ph.D., Princeton University, 2012 B.Eng., Tsinghua University, Computer Science His work spans optical flow, depth estimation, and visual reasoning, leveraging procedural scene generation and robust neural architectures. Recent publications highlight advancements in multi-layer depth estimation, stereo matching, and simulation environments for embodied AI. Alfred P. Sloan Research Fellowship, 2018 NSF CAREER Award, 2020 ONR Young Investigator Award, 2020 Multiple Best Paper Awards (ECCV, ICCV, 3DV) Deng leads the Princeton Vision & Learning Lab, which develops foundational tools for computer vision and machine learning. His mentorship extends to advising students and collaborating on interdisciplinary projects.
Noah Snavely is a Professor of Computer Science at Cornell Tech and the Cornell Ann S. Bowers College of Computing and Information Science. His research spans computer vision and graphics with a focus on 3D scene understanding from image collections. He also works at Google DeepMind in NYC and leads the Cornell Graphics and Vision Group. His research interests include computer vision and computer graphics , particularly in recovering 3D structure from large photo collections, neural radiance fields, image-based rendering, and scene understanding. His work enables applications in mapping technologies, immersive VR experiences, and synthesizing 3D worlds from text prompts with implications for game design and filmmaking. Recent publications show a strong trend toward generative 3D modeling and neural rendering , with significant contributions in neural radiance fields, view synthesis, and dynamic scene reconstruction. His research group explores unstructured photo collections to develop new technology for 3D world modeling and image analysis. PECASE Microsoft New Faculty Fellowship Alfred P. Sloan Fellowship SIGGRAPH Significant New Researcher Award ACM Fellow (2023) IEEE Fellow NSF CAREER Award Helmholtz Prize Snavely has mentored numerous PhD students and postdocs including Ruojin Cai, Qianqian Wang, and Zhengqi Li. His teaching includes CS5670: Computer Vision across multiple semesters. He has received the Cornell College of Engineering Mr. and Mrs. Richard F. Tucker Teaching Excellence Award (2012). At Cornell Tech, his research group develops technology for modeling the world in 3D from online photo collections and analyzing images for computer graphics applications. His work has been featured in major news outlets including the New York Times, New Scientist, and MIT Technology Review.
Derek W Hoiem is a Professor in the Siebel School for Computing and Data Science at the University of Illinois Urbana-Champaign, where he has been a faculty member since 2009. His research focuses on computer vision and related areas, and he is also the co-founder and Chief Science Officer of Reconstruct, an AI-based construction technology company. His educational background includes: PhD in Robotics, Carnegie Mellon University (2007) Beckman Postdoctoral Fellowship (2008) Prof. Hoiem's research spans computer vision, with a focus on object recognition, scene understanding, and graphics. His work also extends to mobile robotics and 3D scene reconstruction. He has made significant contributions in areas such as visual recognition, 3D modeling, and the application of computer vision in construction monitoring. His recent publications (2023-2025) demonstrate a strong focus on advancing multimodal understanding, particularly in region-based representations, 3D vision, and neural radiance fields. There is a clear trend towards integrating language and vision, improving efficiency in neural networks, and applying computer vision to real-world problems such as construction progress monitoring. His scientific awards and honors are extensive and include: IEEE Fellow (2022) University Scholar (2022) Koendrink Prize (2022) Dean's Award for Excellence in Research, Associate Professor (2021) Campus Distinguished Promotion Award (2015) Best Paper Award: IEEE Winter Conference on Applications in Computer Vision (WACV) (2015) CW Gear Junior Faculty Award (2014) IEEE PAMI Young Researcher Award (2014) Dean's Award for Excellence in Research, Assistant Professor (2014) Sloan Research Fellowship (2013) Intel Early Career Faculty Honor Program Award (2012) NSF CAREER Award (2011) ACM Doctoral Dissertation Award, Honorable Mention (2008) Carnegie Mellon University SCS Distinguished Dissertation Award (2008) Best Paper Award: IEEE Computer Vision and Pattern Recognition (CVPR) (2006) Prof. Hoiem has secured significant research funding, including an NSF CAREER award and an Intel Early Career Faculty award. He is also actively involved in technology transfer, having co-founded Reconstruct where he serves as Chief Science Officer. His teaching excellence is reflected in multiple "List of Teachers Ranked as Excellent" awards spanning from 2010 to 2021. Prof. Hoiem leads a research group at UIUC focused on computer vision and 3D scene understanding. Additionally, he co-founded and serves as Chief Science Officer at Reconstruct, which develops AI-based solutions for construction monitoring.
Haibin Ling is the SUNY Empire Innovation Professor in the Department of Computer Science at Stony Brook University, part of the College of Engineering and Applied Sciences. His research focuses on computer vision, medical image analysis, augmented reality, and AI applications in science. He holds a Ph.D. from the University of Maryland (2006) and prior degrees from Peking University. Previously, he worked at Temple University (2008–2019) and held roles at Siemens Corporate Research, UCLA, and Microsoft Research Asia. Professor Ling's work spans biomedical imaging, AI for science, and human-computer interaction. He leads the CV Lab and collaborates with the AI Institute at Stony Brook. Awards include the NSF CAREER Award (2014), Best Student Paper (ACM UIST 2003), and IEEE Fellow (2020). He serves on editorial boards for IEEE Trans. PAMI, Pattern Recognition, and CVIU, and chairs major conferences like CVPR. His research group includes over 50 students and alumni, with active projects in tracking benchmarks (LaSOT), Leafsnap, and medical imaging tools. Notable publications address OCTA flow estimation, backdoor attacks on vision models, and topology-guided medical learning. Collaborations involve institutions like Temple University and Stony Brook's Department of Applied Mathematics and Statistics.
Professor Hanumant Singh leads the Electrical and Computer Engineering department at Northeastern University, with a joint appointment in Mechanical and Industrial Engineering , and serves as Program Director for the Master of Science in Robotics. He earned his Ph.D. from MIT/WHOI Joint Program in 1995 and has conducted over 60 expeditions globally, focusing on marine geology, polar studies, and coral reef ecology. His research emphasizes field robotics , including SLAM, underwater manipulation, and imaging in extreme environments. He developed the Seabed AUV and Jetyak ASV , widely used in scientific research. His labs include the Field Robotics Lab and the Institute for Experiential Robotics . Research Interests: Machine Learning for Fisheries SLAM with dynamic objects Underwater imaging and manipulation Autonomous surface and aerial systems Polar and marine robotics Awards: ICRA Best Student Paper Award, IEEE Oceanic Engineering Society Distinguished Faculty Award (2025), Lifetime Achievement Award (2022), and IEEE Fellow status. His work has been featured in Nature Geoscience , Polar Biology , and media outlets like WGBH. Students & Collaborations: Advises students like Srinidhi Pattala (MS Robotics) and Dennis Giaya (PhD Computer Engineering). Collaborates with institutions on projects such as Antarctic sea ice thickness estimation and deep-sea submersible missions.
Georgia Gkioxari is an Assistant Professor in the Division of Computing and Mathematical Sciences at Caltech , with a part-time affiliation at Meta AI . Her work focuses on extending visual perception models through advanced 2D and 3D representation learning, spatial reasoning, and generative models. Education: Not explicitly mentioned in the text Research interests span 3D perception , spatial reasoning , and vision-language integration , with projects like Visual Agentic AI for Spatial Reasoning and Token-by-Token Multimodal Alignment . Her publications emphasize 3D object detection , reconstruction , and generative modeling techniques including diffusion models and transformers . Scientific recognition includes the Meta LLM Evaluation Research Grant , Okawa Research Grant , Google Faculty Scholar Award 2024 , and Amazon Research Award . She teaches courses like Large Language & Vision Models (EE/CS 148) and Learning & 3D (CS 101) at Caltech. Labs & Teams: Leads Glab with members including Ilona Demler, Ziqi Ma, and Damiano Marsili
Professor Gabriel Brostow is a faculty member in the Department of Computer Science at University College London (UCL), where he leads research in Computer Vision and Human-Computer Interaction. He also serves as Chief Research Scientist and Senior Director of the R&D Team at Niantic, the company behind Pokémon GO. His work bridges academic research and industry applications, focusing on developing AI systems that enhance human capabilities through what he terms 'Human in the Loop AI'—now commonly referred to as Human-Centered AI. Brostow completed his BS in Electrical Engineering at UT Austin, followed by a PhD with Irfan Essa at Georgia Tech. He then pursued postdoctoral research with Roberto Cipolla's Computer Vision & Robotics Group at Cambridge University as a Marshall Sherfield Fellow, and with Marc Pollefeys in ETH Zurich's CVG Group. His research explores how AI, particularly Computer Vision, can serve as 'super-tools' for professionals across various domains including filmmaking, architecture, robotics, and scientific research. Specific interests include assistive technology for everyday life, authoring systems that maximize user effort, 3D reconstruction, depth estimation, and vision-language models. His work often involves creating systems that are validated through real-world human interaction to ensure practical utility. Analysis of his recent publications reveals a strong focus on practical applications of Computer Vision that directly interact with humans. His research spans 3D scene understanding, depth estimation, sketch-based interfaces, and multimodal AI systems. There's a clear emphasis on creating benchmarks and tools that facilitate human-AI collaboration, with applications in assistive technology, urban planning, filmmaking, and biodiversity monitoring. His work frequently appears at top conferences including CVPR, NeurIPS, ECCV, and CHI. Marshall Sherfield Fellowship Brostow actively mentors PhD students, with current advisees including Ross Murphy, Skanda Koppula, Gizem Unlu, Omiros Pantazis, and Jamie Watson. His alumni include numerous PhD graduates and MSc students who have gone on to successful careers in academia and industry. He emphasizes selecting students based on passion and potential rather than just academic credentials, valuing traits like helpfulness, drive, and hunger to learn. His research is supported through collaborations with major institutions and companies including DeepMind, MIT, and the University of Edinburgh. He leads a research group at UCL that collaborates closely with Niantic's R&D team, creating a unique bridge between academic research and industry application. His team's work frequently involves developing novel Computer Vision techniques that are validated through real-world human interaction, ensuring practical utility alongside technical innovation. The group explores blue-sky research problems with applications ranging from assistive technology to professional tools for filmmakers, architects, and scientists studying diverse environments.
Baris Fidan is a Professor in Mechanical & Mechatronics Engineering at the University of Waterloo, with cross appointments in System Design Engineering and Electrical & Computer Engineering. He is a senior member of IEEE and AIAA. His research focuses on cooperative/adaptive control, autonomous systems, multi-agent networks, and vehicular control applications. He leads the Cooperative & Adaptive Mechatronic Systems (CAMS) Lab, which develops control strategies for autonomous vehicles, robotic systems, and intelligent transportation. Education: PhD in Electrical Engineering, University of Southern California (2003) Masters in Electrical & Electronic Engineering, Bilkent University (1998) Bachelor's in Electrical & Electronic Engineering & Mathematics, Middle East Technical University (1996) Research Interests: His work spans adaptive control theory, sensor networks, multi-agent coordination, autonomous vehicle networks, and biomedical systems control. He emphasizes practical applications in intelligent transportation, robotic navigation, and distributed system optimization. Grants & Projects: He has led major grants including NSERC Discovery Programs on cooperative mechatronic systems and 3D autonomous vehicle coordination. Industrial projects include autonomous driving strategies, vehicle control optimization, and high-precision gear manufacturing technologies. Labs/Teams: Directs the CAMS Lab, which collaborates on projects involving distributed motion planning, sensor localization, and autonomous vehicle networks. Current projects address challenges in urban autonomous driving, cooperative robotic systems, and resilient sensor networks.
Clark Olson is a Professor in the Division of Computing & Software Systems at the University of Washington Bothell, part of the School of Science, Technology, Engineering & Mathematics. He earned his Ph.D. in Computer Science from UC Berkeley (1994), M.S. in Electrical Engineering (1990), and B.S. in Computer Engineering (1989) from the University of Washington, Seattle. Education: Ph.D. in Computer Science (2017) from University of California, Berkeley M.S. in Electrical Engineering (1990) from University of Washington, Seattle B.S. in Computer Engineering (1989) from University of Washington, Seattle His research focuses on computer vision, robot navigation, and clustering algorithms. He has developed techniques for Mars rover terrain mapping, subspace clustering, and geometric feature matching. His work bridges theory and application in autonomous systems and image analysis. Analysis of his publications reveals expertise in computer vision (8 papers), clustering algorithms (4 papers), and robotics (5 papers). Key subtopics include Mars exploration (3 papers), Hough transforms (3 papers), and probabilistic methods (3 papers). Professor Olson teaches courses ranging from introductory programming (CSS 161-162) to advanced topics in computer vision (CSS 487-587) and algorithm design (CSS 549). He also advises on the CSSE Capstone (CSS 497) projects requiring rigorous prerequisites and structured evaluation criteria.
Prof. Christian Heipke is a distinguished academic serving as Dean of the Faculty of Civil Engineering and Geodetic Science at Leibniz University Hannover, Germany. He also holds the position of Executive Director at the Institute of Photogrammetry and GeoInformation (IPI), one of the leading research institutions in geospatial sciences within the faculty. His leadership extends across multiple committees including the Curriculum and Teaching Committee, Admissions and Examination Boards for Geodetic Science and Geoinformatics, and Navigation and Environmental Robotics. As a Professor at IPI, he maintains active research while overseeing significant academic and administrative responsibilities at the university. Professor Heipke's research spans multiple domains within geospatial sciences, with particular emphasis on: Advanced photogrammetric techniques and algorithms Remote sensing applications for environmental monitoring Computer vision approaches for geospatial data analysis Urban development monitoring using satellite imagery Machine learning applications in geoinformatics Disaster prediction and management systems His recent scholarly output reveals a strong focus on integrating cutting-edge computer vision and deep learning techniques with traditional photogrammetric methods. Analysis of his 15 most recent publications shows a clear trajectory toward more sophisticated AI-driven approaches for processing geospatial data, with particular attention to time-series analysis, uncertainty quantification, and multi-view systems. His work bridges theoretical advancements with practical applications in flood forecasting, deforestation monitoring, urban planning, and construction materials analysis. The geographic scope of his research has expanded significantly, with recent projects focusing on international case studies in the Philippines and tropical regions. Professor Heipke leads the Institute of Photogrammetry and GeoInformation, a major research hub that has celebrated 75 years of contributions to the field. His leadership extends to the Graduiertenkolleg 2159: "Integrity and Collaboration in Dynamic Sensor Networks," where he serves as a professor overseeing doctoral research. The institute maintains state-of-the-art facilities for processing satellite imagery, aerial photography, and developing novel algorithms for geospatial data analysis. Under his direction, the institute has strengthened its international collaborations and interdisciplinary research approaches, particularly in addressing Sustainable Development Goals through geospatial technologies.
Raquel Urtasun is a Full Professor in the Department of Computer Science at the University of Toronto and a co-founder of the Vector Institute for AI. She is also the Founder and CEO of Waabi, an autonomous vehicle company. Previously, she was Chief Scientist and Head of R&D at Uber ATG (2017–2021) and held faculty positions at the Toyota Technological Institute at Chicago (TTIC) and as a visiting professor at ETH Zurich. Her research spans machine learning, computer vision, robotics, and AI with a strong focus on autonomous driving and 3D perception. Education: Bachelor's degree, Universidad Pública de Navarra, 2000 Ph.D., Computer Science, École Polytechnique Fédérale de Lausanne (EPFL), 2006 Postdoctoral studies, MIT and UC Berkeley Raquel Urtasun's research focuses on developing AI systems for self-driving cars, emphasizing efficient perception using minimal sensors. Her work includes 3D scene understanding, stereo vision, optical flow, semantic segmentation, and object detection. She has developed the KITTI benchmark suite, widely used in autonomous driving research. Her lab is an NVIDIA NVAIL lab, reflecting its leadership in AI innovation. Her recent publications show a consistent trend in deep learning for visual perception, particularly in stereo matching, optical flow, 3D object detection, and semantic segmentation. These works integrate deep neural networks with structured models like CRFs and MRFs, pushing the boundaries of accuracy and efficiency in scene understanding for autonomous systems. Scientific Awards: NSERC E.W.R. Steacie Fellowship NVIDIA Pioneers of AI Award Google Faculty Research Awards (multiple) Amazon Faculty Research Award Connaught New Researcher Award Fallona Family Research Award Best Paper Runner Up at CVPR 2013 and 2017 UPNA Alumni Award Chatelaine 2018 Woman of the Year Adweek 2018 Toronto's Top Influencers Urtasun has advised numerous PhD and Master’s students, many of whom now hold faculty or research scientist positions at institutions like UIUC, NYU, UBC, MIT, and companies including Google, Amazon, NVIDIA, and Apple. She has secured significant research grants from NSERC, Google, Amazon, and NVIDIA. Her leadership extends to organizing workshops and serving as Area Chair and Program Chair at top conferences like CVPR, ICML, and NeurIPS. Labs and Teams: She leads a research group at the University of Toronto focused on AI for autonomous systems. Her team has been recognized as an NVIDIA NVAIL lab, and she continues to mentor students and postdocs working on cutting-edge problems in robotics and machine learning, both at UofT and through her company Waabi.
Dr James Herbert-Read is an Associate Professor and Whitten Lecturer in Marine Biology at the Department of Zoology, University of Cambridge. He serves as Deputy Head of Department (Postgraduate Education) and leads the Marine Behavioural Ecology Group. His research focuses on understanding how animals, particularly marine organisms, collect and process information from their environments to make behavioral decisions, with emphasis on social interactions, adaptation mechanisms, and ecological constraints. His group employs theoretical frameworks, controlled experiments, and quantitative field studies to investigate behavioral diversity in marine species. Key themes include collective behavior, predator-prey dynamics, camouflage strategies, and the impacts of environmental stressors on animal decision-making. Recent publications highlight work on lionfish vocalization mechanisms, cuttlefish camouflage, citizen science applications in marine research, and behavioral responses to visual and acoustic noise. Scientific awards and affiliations include: Whitten Lecturer in Marine Biology Associate Professor, University of Cambridge He has supervised research projects on topics such as: Social attraction in invasive fish species Evolution of coordinated movement Neurophysiological basis for leadership in shoals Maternal effects on offspring exploration
Kostas Daniilidis is the Ruth Yalom Stone Professor at the University of Pennsylvania in the School of Engineering and Applied Science , specifically the Department of Computer and Information Science . He is also affiliated with the GRASP Laboratory and Archimedes, Athena Research Center, Greece . Education : PhD in Computer Science (1992) from the University of Karlsruhe with Hans-Hellmut Nagel Diploma in Electrical Engineering (1986) from the National Technical University of Athens Research Interests : Kostas Daniilidis is a leading researcher in Computer Vision and Robotics , with significant contributions to event-based vision , equivariant learning , 3D human pose estimation , and hand-eye calibration . His work spans neural rendering , dynamic scene modeling , and low-latency sensing systems . Article Trends : Daniilidis’s recent publications focus on event cameras for low-light and high-speed applications, Gaussian splatting for real-time 3D reconstruction, and equivariant neural architectures for robust motion estimation. His work bridges deep learning with geometric vision , emphasizing human mesh recovery and multi-agent coordination . Scientific Awards : Best Conference Paper Award at ICRA 2017 IEEE Fellow (2012) Teaching : He has taught courses such as CIS580: Machine Perception and CIS121: Data Structures , alongside advanced topics in robotics and computer vision. Lab & Collaborations : As director of the GRASP Laboratory (2008–2013), he fostered interdisciplinary research in robotics, and currently collaborates with institutions like the Athena Research Center in Greece.
Oisin Mac Aodha is a Reader (Associate Professor) in Machine Learning at the School of Informatics, University of Edinburgh. He is also an ELLIS Scholar and founder of the Turing interest group on biodiversity monitoring and forecasting, having previously served as a Turing Fellow from 2021-2025. Mac Aodha completed his undergraduate degree in electronic engineering from the University of Galway in Ireland, followed by his MSc and PhD at University College London (UCL). His academic journey includes postdoctoral positions at UCL (2013-2016) working with Prof. Gabriel Brostow and Prof. Kate Jones, and at Caltech (2016-2019) in Prof. Pietro Perona's Computational Vision Lab as part of the Visipedia team. His research centers on computer vision and machine learning with emphasis on 3D understanding, human-in-the-loop methods, and AI for conservation and biodiversity monitoring. He has made significant contributions to monocular depth estimation (including the influential Monodepth2 paper), fine-grained visual categorization, and biodiversity monitoring systems. His work bridges theoretical machine learning with practical ecological applications, developing tools for species identification, range estimation, and conservation efforts. Recent publications reveal a strong trend toward ecological applications while maintaining fundamental contributions to 3D vision and representation learning. His major scientific achievements include: Turing Fellow (2021-2025) ELLIS Scholar Founder of the Turing interest group on biodiversity monitoring and forecasting Co-organizer of the Fine-Grained Visual Categorization (FGVC) workshop series at major vision conferences Mac Aodha advises multiple PhD students and postdocs working on computer vision for biodiversity monitoring, 3D understanding, and human-in-the-loop learning. His team has developed practical tools like Whombat (an open-source annotation tool for bioacoustics) and contributed to field-deployed biodiversity monitoring systems. He has served as Area Chair for top conferences including NeurIPS, CVPR, ICCV, and ICML, demonstrating his standing in the computer vision community. His research group collaborates extensively with ecologists at University College London, particularly with Prof. Kate Jones' team, bridging machine learning expertise with ecological domain knowledge. The Vision at Edinburgh group he contributes to focuses on developing practical AI tools that address real-world conservation challenges while advancing fundamental computer vision research.
Gabriele Facciolo is a Professor at the Centre Borelli, ENS Paris-Saclay, France. He is a Senior Member of the Institut Universitaire de France (IUF) and holds an Innovation Chair (2025). His research focuses on image and video processing, remote sensing, and super-resolution techniques. Current affiliations: Centre Borelli (ENS Paris-Saclay), Institut Universitaire de France His research explores advanced algorithms for satellite stereo pipelines, real-time deblurring, denoising, and explainable AI systems for legal evidence enhancement. He coordinates projects like ANR SURECAVI (Super-resolution for visible camera systems) and ANR IMPROVED (video enhancement for judicial use), with recent work on Gaussian Splatting for Earth Observation and multi-date satellite super-resolution. Notable scientific achievements include the IGARSS 2025 Top 10 Student Paper Award and leadership in projects funded by ANR (€890k) and Prime Minister's entities (SGDSN/ANSSI). His work bridges computational imaging, defense applications, and digital forensics. Project leadership: SURECAVI, IMPROVED, BOFOR Key technologies: GPU acceleration, real-time processing, optical flow estimation, RPC refinement Gabriele actively contributes to open-source tools like S2P (Satellite Stereo Pipeline), MGM (MultiGlobal Matching), and OMNIflip. He teaches in the Master MVA program and collaborates across institutions (ENPC, UPF).
