جست‌وجوی استاد و پژوهشگر

Andrea Vedaldiمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Unsupervised Learning
• +۳ مورد دیگر

Andrea Vedaldi is a Professor of Computer Vision and Machine Learning at the University of Oxford's Department of Engineering Science, affiliated with the Visual Geometry Group (VGG). He specializes in unsupervised methods for understanding images and videos, focusing on 3D geometry and semantics. His research bridges foundational AI and practical applications, with contributions to generative models, neural fields, and self-supervised learning. Education: PhD in Computer Science (2008), University of California, Los Angeles MSc in Computer Science (2005), UCLA BSc in Information Engineering (2003), University of Padua Research Interests: Unsupervised learning, 3D perception, generative AI, neural rendering, and scalable vision systems. His work emphasizes ethical, responsible AI aligned with ERC-funded projects like UNION (ERC Consolidator Grant). Key Contributions: Co-developer of VLFeat and MatConvNet libraries Leader in 3D reconstruction and diffusion models (e.g., CatFree3D) Recipient of the PAMI Thomas S. Huang Prize and multiple best paper awards Grants & Service: Principal Investigator on £2.3M ERC Consolidator Grant (UNION) Co-organizer of major conferences (ECCV 2020 Program Chair, CVPR 2023 Area Chair) Reviewer for top journals/conferences (PAMI, CVPR, NeurIPS) Labs & Teams: VGG Group at Oxford, collaborating on projects like Meta 3D Gen and Common Objects in 3D (CO3D).

• Machine Learning
• Computer Vision
• Data Mining
• +۶ مورد دیگر

Dr. Chang Xu is an Associate Professor in Machine Learning and Computer Vision at the University of Sydney's School of Computer Science. He holds a Bachelor of Engineering from Tianjin University and a PhD from Peking University. His research focuses on machine learning, data mining, and their applications in AI and computer vision, including multi-view learning, visual search, and face recognition. He is an ARC Future Fellow and a member of the Sydney Southeast Asia Centre and The Net Zero Institute. Education: B.E. in Engineering (Tianjin University), Ph.D. in Computer Science (Peking University). His research interests emphasize handling heterogeneous data, exploring data variety, and developing algorithms for robust AI systems. His work includes adversarial robustness, neural architecture search, and efficient deep learning models. Research trends in his articles include adversarial robustness in neural architectures, efficient vision transformers, multimodal 3D style transfer, and underwater image restoration. Key contributions span image restoration, video super-resolution, and lightweight network design. He has advised multiple PhD and master's students on topics like diffusion models, radar image synthesis, and graph similarity. Awards: ARC Future Fellow. Collaborations focus on cross-domain data integration and AI applications. His labs and teams explore generative models, robust learning, and scalable robotics policies. Recent work includes diffusion models for action segmentation and robust vision-language systems.

Muhannad S Bakirمشاهده پروفایل

استاد

• Heterogeneous Integration
• 3D IC Packaging
• Advanced Cooling Systems
• +۷ مورد دیگر

Muhannad S. Bakir is the Dan Fielder Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology and serves as the Director of the 3D Systems Packaging Research Center. His research focuses on heterogeneous integration of microsystems, including 2.5D and 3D ICs and packaging technologies, with significant contributions to advanced cooling systems, electrical and photonic interconnects, and biosensor integration with CMOS. Dr. Bakir's research interests span heterogeneous microsystem design and integration, advanced cooling and power delivery for emerging architectures, electrical and photonic interconnect technologies, biosensor technologies, and nanofabrication. His work addresses critical challenges in next-generation electronics, enabling polylithic integration that concatenates heterogeneous ICs of various functionalities while mimicking monolithic-like densities. His research particularly focuses on co-design of thermal technologies, power delivery networks, and signaling networks for silicon nanoelectronic systems. His recent publications demonstrate strong trends in fused-silica stitch-chip technology for heterogeneous integration, with particular emphasis on RF and mm-wave applications, power delivery for AI accelerators, and thermal management solutions. His work bridges electrical engineering, materials science, and thermal management to solve critical bottlenecks in computing performance and efficiency. 2013 Intel Early Career Faculty Honor Award 2012 DARPA Young Faculty Award 2011 IEEE CPMT Society Outstanding Young Engineer Award 2012 National Academy of Engineering Frontiers of Engineering Symposium Invited Participant 2015 IEEE CPMT Society Distinguished Lecturer 2014 Best Paper of the IEEE Transactions on Components Packaging and Manufacturing Technology More than 25 conference and student paper awards Twelve issued US Patents Dr. Bakir leads the Integrated 3D Systems Lab (I3DS) at Georgia Tech, which is actively researching advanced packaging, interconnects, electrical and thermal design, and system integration. His team has received significant recognition for their work, including multiple best paper awards from major conferences like ECTC, IITC, and CICC. The lab is currently seeking postdoctoral researchers and research faculty to advance next-generation electronics through collaborative research. His lab focuses on enabling the next phase of Moore's Law through polylithic integration, which concatenates heterogeneous ICs of various functionalities (digital, analog, photonic, and mm-wave) using advanced off-chip '2.5D' and '3D' heterogeneous interconnects and packaging. This work impacts applications in high-performance computing, machine learning, edge intelligence, autonomous vehicles, augmented/virtual reality, and healthcare.

• Cognitive Robotics
• Developmental Robotics
• Human-Robot Interaction
• +۵ مورد دیگر

Tetsuya Ogata is a Professor at the Faculty of Science and Engineering, School of Fundamental Science and Engineering at Waseda University. He also holds joint appointments as a Fellow at the Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), and as a visiting professor at the National Institute of Informatics (NII). Professor Ogata serves as director of the Institute of AI and Robotics at Waseda University, chairperson of the AI Robot Association (AIRoA), and research supervisor of JST CREST "Fundamentals and Core Technologies for Embodied AI" since 2025. Professor Ogata received his B.S., M.S., and D.E. degrees in mechanical engineering from Waseda University in 1993, 1995, and 2000, respectively. He began his academic career as a Research Associate at Waseda University (1999-2001), followed by positions as a Research Scientist at RIKEN Brain Science Institute (2001-2003), Lecturer (2003-2005) and Associate Professor (2005-2012) at Kyoto University's Graduate School of Informatics, before becoming a Professor at Waseda University in 2012. He was also a JST PRESTO Researcher from 2009 to 2015. Professor Ogata's research focuses on cognitive developmental robotics, deep predictive learning, and the integration of vision, language, and action (VLA) for human-robot interaction. His work explores how robots can learn through experience and interaction with humans and environments, developing models that allow for flexible behavior generation and adaptation. His research has significant implications for creating robots that can understand human intentions, collaborate effectively in real-world settings, and continuously learn from their experiences. The integration of multimodal information processing with predictive models forms the core of his approach to embodied artificial intelligence. Analysis of Professor Ogata's recent publications reveals a strong focus on deep predictive learning frameworks that enable robots to anticipate future states and generate appropriate actions. His research spans multiple domains including soft object manipulation, bimanual coordination, transparent object grasping, and integration of large language models with robotic systems. A notable trend is the increasing emphasis on multimodal integration, combining visual, tactile, and linguistic information to create more robust and adaptable robotic systems. His recent work also shows growing interest in the application of foundation models to robotics, aiming to create more general-purpose robotic capabilities. Best Paper Award, Advanced Robotics, The Robotics Society of Japan (2024) Best paper award Nomination Finalist, IEEE/SICE International Symposium on System Integration (2024) Frontiers of Science Awards, The International Congress for Basic Science (2023) Minister of Education, Culture, Sports, Science and Technology Award (2023) Fellow, Japan Society of Mechanical Engineering (2023) Fellow, The Society of Instrument and Control Engineers (2022) Fellow, The Robotics Society of Japan (2022) IBM Academic Awards (2017) Professor Ogata has been instrumental in establishing collaborative research frameworks between academia and industry, serving in leadership roles for multiple professional organizations including the Robotics Society of Japan, Japanese Society for Artificial Intelligence, and Japan Deep Learning Association. His research has been supported by significant grants including JST CREST funding for "Fundamentals and Core Technologies for Embodied AI." He has mentored numerous researchers who have gone on to contribute to the field of robotics and AI, with many of his former students now holding positions in leading academic and industrial research institutions. Professor Ogata directs the Ogata Laboratory at Waseda University, which focuses on cognitive developmental robotics and deep predictive learning. The lab maintains strong collaborations with industry partners including major robotics companies and technology firms. In 2025, he played a key role in establishing the AI Robot Association (AIRoA), which brings together companies like Toyota, Nissan, and KDDI to advance the development of practical AI robotics technologies. His research group is known for its interdisciplinary approach, combining insights from neuroscience, cognitive science, and engineering to create more human-like robotic capabilities.

Mohit Iyyerمشاهده پروفایل

دانشیار

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Mohit Iyyer is an Associate Professor in Computer Science at the University of Maryland, College Park (UMD), affiliated with the Manning College of Information and Computer Sciences (CICS) and the CLIP lab. Previously, he held roles at UMass Amherst, AI2, and completed his PhD at the University of Maryland. His research focuses on advancing NLP and machine learning, particularly in long-context LLMs, factuality evaluation, and human-LLM collaboration. Education : PhD, Computer Science, University of Maryland, College Park (2017) MS, Computer Science, University of Maryland, College Park (2014) BS, Computer Science, Washington University (2012) Research Interests : His work emphasizes improving instruction-following in LLMs, evaluating long-form text, detecting AI-generated content, and enhancing LLM robustness. He actively explores multilingual LLMs, literary translation, and creative writing support through AI. Key Contributions : Developed influential frameworks like ELMo and QuAC, and introduced benchmarks such as BEARCUBS and BooookScore. His work on watermarking (PostMark) and adversarial attacks (OverThink) addresses security and reliability in AI systems. Awards & Recognition : NSF CAREER Award (2021) Samsung AI Researcher of the Year (2022) Best Paper Awards at NAACL 2018 and CCS 2023 Advising & Grants : Advises a dynamic research group and has received grants for projects like multilingual QA and long-context LLM evaluation. Collaborates with industry partners like AI2 and DeepMind. Labs & Teams : Leads the CLIP lab at UMD, focusing on foundational NLP research and applications in literature, dialogue systems, and computational storytelling.

• Computer Graphics
• Digital Photography and Video
• Non-Photorealistic Rendering
• +۷ مورد دیگر

David Salesin is an Affiliate Professor in the Department of Computer Science & Engineering at the University of Washington and a Principal Scientist/Director at Google Research since 2019. He has held academic roles at Cornell University (Visiting Assistant Professor, 1991-92) and guest professorships at Zhejiang University. His career spans academia and industry, including leadership at Adobe's Creative Technologies Lab (2005-17) and Microsoft Research (1999-2005). PhD, Stanford University (1991) Sc.B., Brown University (1983) His research focuses on computer graphics, particularly non-photorealistic rendering, digital typography, color science, and adaptive document layout. He pioneered techniques in image-based rendering, pen-and-ink illustration, and facial animation, with applications in multimedia and user interface design. Article Trends : His work bridges procedural content generation, 3D visualization, and artistic computing, emphasizing user-driven tools for creative industries. Key subfields include texture advection, multiresolution modeling, and real-time camera control for virtual cinematography. Scientific Awards : ACM Fellow (2002) ACM SIGGRAPH Achievement Award (2000) Carnegie Foundation Professor of the Year (1998) NSF Presidential Faculty Fellow (1995-98) Alfred P. Sloan Research Fellowship (1995-97) Numerous industry grants and lab donations He has advised over 30 PhD and Master's students, including leaders at Microsoft, Pixar, and Google. His labs at UW and Adobe focused on graphics, imaging, and creativity tools.

Jeff Schneiderمشاهده پروفایل

استاد پژوهشی

• Machine Learning
• Autonomous Systems
• Reinforcement Learning
• +۷ مورد دیگر

Jeff Schneider is a Research Professor at the Robotics Institute within the School of Computer Science at Carnegie Mellon University. His research focuses on active learning, data mining, reinforcement learning, optimization, and intelligent control , applied to industrial and commercial challenges. Current PhD Students : Anoushka Alavilli, Benjamin Freed, Tejus Gupta, Albert Xu, Brian Yang Current Masters Students : Wen-Tse Chen, Xintong Duan, Aman Mehra, Vedant Mundheda, Zhouchonghao Wu Past PhD Students : J. Andrew Bagnell, Viraj Mehta, Matthew Tesch Past Masters Students : Ravi Tej Akella, Swapnil Pande, Siddharth Venkatraman Schneider's research bridges machine learning and autonomous systems , particularly in reinforcement learning , multi-robot coordination , and self-driving car technology . His work emphasizes practical applications of learning algorithms in real-world scenarios. His recent publications highlight advancements in offline reinforcement learning , multi-agent policy coordination , and behavior planning for autonomous vehicles . These studies often integrate deep learning and probabilistic modeling to address complex control and decision-making problems. Labs: Auton Lab CMU Center for Autonomous Vehicle Research Consulting & Industry Impact: Schneider has consulted for organizations like Uber ATG, Psychogenics, and Schenley Park Research, applying machine learning to domains such as self-driving cars , nuclear fusion , marketing , and drug discovery .

Jiwoong Parkمشاهده پروفایل

استاد

• Nanomaterials
• Atomically-thin materials
• Materials Chemistry
• +۷ مورد دیگر

Jiwoong Park is Professor of Chemistry and Chair of the Department of Chemistry at the University of Chicago, and simultaneously Professor of Molecular Engineering in the Pritzker School of Molecular Engineering. His interdisciplinary research group, the Park Group, is jointly affiliated with the James Franck Institute and the Materials Research Science and Engineering Center (MRSEC) at UChicago, and operates from the Gordon Center for Integrative Science. Education & Training Ph.D., University of California, Berkeley (2003) B.S., Seoul National University (1996) Junior Fellow, Rowland Institute, Harvard University (2003–2006) Assistant → Associate Professor, Department of Chemistry and Chemical Biology, Cornell University (2006–2016) Research Interests Park’s research centers on the science and technology of precisely engineered nanomaterials, particularly atomically-thin two-dimensional (2D) crystals and van der Waals solids. Spanning chemistry, physics, materials science and electrical engineering, his group develops novel synthetic, imaging and characterization techniques to uncover new physical phenomena and translate them into scalable device technologies. Key thrusts include growth of wafer-scale molecular crystals, optical and transport spectroscopy of 2D semiconductors, mechanical behavior of polycrystalline nanomembranes, and integration of these materials into photonic, electronic and energy-harvesting devices. Scientific Awards Elected Fellow of the American Physical Society (2022) – “for the development of synthetic, imaging, and characterization techniques of atomically thin materials and the discovery of novel properties of van der Waals solids.” Clarivate Highly Cited Researcher (2023) – recognition for multiple papers ranking in the global top 1% by citations in Materials Science and Chemistry. Group & Collaborations The Park Group is an interdisciplinary team of postdocs, graduate researchers and undergraduates housed in the Gordon Center for Integrative Science. The group actively collaborates with colleagues across the Department of Chemistry, Department of Physics, and the Pritzker School of Molecular Engineering, leveraging shared facilities at the James Franck Institute and MRSEC to push the frontiers of 2D material science.

Florian Shkurtiمشاهده پروفایل

استادیار

• Robotics
• Machine Learning
• Computer Vision
• +۶ مورد دیگر

Florian Shkurti is an Assistant Professor in the Department of Computer Science at the University of Toronto Mississauga (UTM), affiliated with the UofT Robotics Institute, Vector Institute, and Acceleration Consortium. His research focuses on robotics, machine learning, and computer vision, emphasizing safe and effective autonomous systems in dynamic environments. He directs the Robot Vision and Learning (RVL) lab, exploring areas like environmental monitoring, autonomous navigation, and mobile manipulation. Research Interests: His work spans robotics, machine learning, and computer vision. Key areas include robot perception, planning under uncertainty, safe exploration, imitation learning, and applications in field robotics, autonomous vehicles, and chemistry lab automation. He develops methods enabling robots to perceive, reason, and act safely in collaboration with humans. Publications: Recent work includes advancements in safe multitask learning, interactive crowd navigation, and diffusion models for trajectory planning. His research bridges theoretical foundations with real-world applications in environmental science and autonomous systems. Affiliations: Faculty Member, UofT Robotics Institute; Faculty Affiliate, Vector Institute; Faculty Member, Acceleration Consortium. He also holds positions at UTM's Mathematical & Computational Sciences department. Teaching: Courses include Imitation Learning for Robotics, Neural Networks, and Mobile Robotics. He emphasizes hands-on experience with autonomous systems through projects involving RC cars and simulation tools. Labs & Teams: Leads the RVL lab, collaborating on projects like RoboCulture (automated biological experimentation) and SICNav (safe crowd navigation systems). The lab focuses on cross-disciplinary robotics solutions for real-world challenges.

• Image Processing
• Computer Vision
• Remote Sensing
• +۴ مورد دیگر

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).

• Autonomous Driving
• Sensor Fusion
• Intelligent Transportation Systems
• +۵ مورد دیگر

Prof. Dr.-Ing. Christoph Stiller is a full professor at the Karlsruher Institut für Technologie (KIT) and serves as the director of the Institute of Measurement and Control Technology (Institut für Mess- und Regelungstechnik, MRT). His work focuses on autonomous driving, sensor fusion, probabilistic estimation, HD mapping, motion planning, and intelligent transportation systems. Education: Details on his academic degrees are not provided in the text, but he holds the title of Dr.-Ing. indicating a doctoral degree in engineering. Research Interests: Prof. Stiller's research spans a wide array of topics critical to the development of autonomous vehicles. His work includes: Sensor Fusion: Integrating data from LiDAR, cameras, and radar to create robust perception systems. HD Mapping & Localization: Developing high-definition maps and precise localization techniques for urban and highway environments. Motion Planning & Decision Making: Creating algorithms for safe and efficient trajectory planning under uncertainty. Machine Learning & AI: Applying deep learning and reinforcement learning to perception, prediction, and control tasks. Publication Trends: His recent publications (2023–2025) emphasize robust traffic light detection, image stitching for panoramic views, motion prediction using redundancy reduction, and safety-enhanced model predictive control. The work increasingly integrates learning-based methods with classical control and estimation theory. Scientific Awards: No specific awards are listed in the provided text. Teaching & Supervision: Prof. Stiller teaches foundational and advanced courses in measurement and control systems, probabilistic estimation, and autonomous driving. He holds regular office hours during both summer and winter semesters and is actively involved in advising students and researchers. Labs & Teams: He leads the Institute of Measurement and Control Technology (MRT) at KIT, which is engaged in cutting-edge research in autonomous systems. The institute collaborates with industry and academia on large-scale projects such as UNICARagil and various European initiatives.

Patrick Mitranمشاهده پروفایل

استاد

• 5G/6G Communication Systems
• Millimeter-wave and Sub-THz Technologies
• Digital Predistortion
• +۳ مورد دیگر

Patrick Mitran is a full-time Professor at the University of Waterloo's Department of Electrical and Computer Engineering, within the Faculty of Engineering. His research focuses on advanced wireless communication systems, including 5G/6G technologies, millimeter-wave and sub-THz communication, digital predistortion techniques, MIMO systems, and beamforming architectures. He leads projects addressing challenges in transmitter linearization, network resource allocation, and hardware-efficient signal processing. Key research interests include optimizing frequency multiplier-based transmitters, mitigating inter-cell interference in massive MIMO networks, and developing algorithms for reconfigurable intelligent surfaces (RIS). His work often intersects hardware design, signal processing, and network optimization, with applications in next-generation wireless infrastructure. Recent publications highlight innovations in ultrawideband signal generation for 6G testing, practical RIS configurations, and FPGA-based real-time digital predistortion implementations. His contributions emphasize both theoretical advancements and practical system-level solutions. Dr. Mitran's research group collaborates on cutting-edge topics such as hybrid NOMA in multi-cell networks, adaptive coding modulation for Gaussian channels, and interference decoding strategies. His work has been published in top-tier journals and conferences, reflecting a sustained impact on modern wireless communication technologies.

• Computer Vision
• 3D Reconstruction
• Unsupervised Learning
• +۳ مورد دیگر

Iro Laina is a Departmental Lecturer in Computer Vision at the University of Oxford's Visual Geometry Group. She holds a PhD (Dr. rer. nat.) from the Technical University of Munich (TUM), where her dissertation earned the ECVA PhD Award. Her research focuses on unsupervised and language-supervised learning for 3D scene understanding, image/video perception systems, and geometric reconstruction. Education: PhD in Computer Science (TUM), MSc in Biomedical Computing (TUM), Diploma in Electrical & Computer Engineering (NTUA). Research Interests: 3D Reconstruction and Generation Unsupervised Learning Multi-View and Video Analysis Generative Diffusion Models Geometry-Aware Networks Her recent work emphasizes scalable 3D scene synthesis, training-free methods, and cross-modal fusion with LLMs. Over 15+ publications since 2021 reflect her leadership in geometric deep learning. Awards: ECVA PhD Award (2020), Recognized in multiple international conferences. Advising: Mentors DPhil students in creative AI applications (e.g., gameplay design). Active in Oxford's Robotics and Biomedical Engineering networks. Labs/Tech: Core member of the Visual Geometry Group, collaborating on projects like IMAD2025 with the ZERO Institute.

Sara Nabilمشاهده پروفایل

استادیار

• Human-Computer Interaction (HCI)
• E-Textiles
• Smart Materials
• +۳ مورد دیگر

Sara Nabil is an Assistant Professor at Queen's University's School of Computing within the Faculty of Arts and Science. She leads the HCI Design Studio and previously held a postdoctoral position at Carleton University's Creative Interactions Lab. Her research focuses on integrating interior design, fashion, and product design with interactive technologies, emphasizing e-textiles, smart materials, and shape-changing interfaces. She holds a PhD from Newcastle University and prior experience as an HCI Lecturer, interior designer, and senior software developer. Education: PhD in Computing, Newcastle University (2019) MSc in Computing BSc in Computing Research interests include designing computational spaces, wearable technology, and e-textile interactions. Her work explores sustainable fabrication methods, user-centered design for smart environments, and bridging traditional crafts with digital technologies. Notable projects include exhibitions like 'Living with Adaptive Architecture' and 'Persuasive Pharmacy Space,' showcasing interactive wearables and spaces. Her recent articles (2023–2025) emphasize e-textile innovations, sustainable design, and human-building interactions. Key themes include smart materials in fashion, modular wearables, and community-driven interaction systems. Awards: None explicitly mentioned. Advising/Grants: No details provided. Labs/Teams: Head of HCI Design Studio and part of iStudio Lab, focusing on interioraction design.

• Computer Vision
• Computer Graphics
• Numerical Methods
• +۸ مورد دیگر

Richard Szeliski is a Distinguished Scientist at Google DeepMind and Affiliate Professor at the University of Washington's Department of Computer Science & Engineering. He previously led the Interactive Visual Media Group at Microsoft Research and founded the Computational Photography group at Facebook. His research focuses on computer vision, computer graphics, and numerical methods, with specialties in 3D modeling from imagery, computational photography, and neural rendering. Education details are not explicitly listed, but his career trajectory indicates advanced academic training in computer science. Research interests include algorithms for 3D reconstruction, image stitching, and optimization techniques. His recent work emphasizes neural rendering, volumetric representations, and large-scale scene modeling. Key contributions include the widely cited textbook Computer Vision: Algorithms and Applications and foundational papers on multi-view stereo, panorama stitching, and energy minimization in MRFs. Publications span 40+ years, with recent focus on radiance fields (NeRF), 3D scene understanding, and real-time rendering systems. Though no explicit awards are listed, his textbook adoption and industry roles reflect significant academic and industrial impact. He advises through his academic role and has contributed to open-source projects like the Bundle Adjustment Library (BAL). Labs/teams include collaborations with Google DeepMind, prior work at Microsoft Research's Interactive Visual Media Group, and academic partnerships at UW's Graphics & Imaging Lab. His work bridges theory and application, addressing challenges in both academic research and industrial-scale systems.