Deva Kannan Ramanan is a Professor at the Robotics Institute of Carnegie Mellon University , focusing on computer vision , machine learning , and human-centered robotics . His work bridges neurorobotics and visual perception , with applications in autonomous driving and 4D reconstruction . Research Topics Computer Vision 3-D Vision and Recognition Visual Servoing Neurorobotics Human-Centered Robotics Graphics & Creative Tools His recent publications in CVPR , ICRA , and ICCV emphasize 4D human reconstruction , neural rendering , and vision-language models for autonomous systems. He serves as General Chair of CVPR 2027 and Program Chair of CVPR 2018 , with IARPA funding for aerial-ground rendering (2023-2027). Current students include PhD candidates Sally Chen, Kangle Deng, and Zhiqiu Lin, while past advisees like Arun Vasudevan and Olga Russakovsky now hold positions at Amazon and Meta respectively.
Michael Kaess is an Associate Professor at the Robotics Institute, Carnegie Mellon University (CMU), within the School of Computer Science. He leads the Robot Perception Lab (RPL) and contributes to the Field Robotics Center (FRC) and Computer Vision Group (CV). His research focuses on efficient perception algorithms for mobile robots, particularly in 3D mapping, SLAM, and sensor fusion using vision, LiDAR, inertial, and sonar data. Kaess holds a PhD in Computer Science from Georgia Tech and was a postdoc at MIT's Marine Robotics Lab. Education: Georgia Institute of Technology, PhD in Computer Science (2008) MIT, Postdoctoral Associate (2008–2010) Research Interests: Kaess develops algorithms for robust and efficient inference in robotics, emphasizing factor graphs and linear algebra. His work spans underwater robotics, aerial systems, tactile SLAM, and multi-sensor integration. Key areas include SLAM with planes/lines, imaging sonar reconstruction, and neural field methods for LiDAR-visual fusion. Publications: Over 145 papers, including work on EDPLVO (visual odometry), HoloOcean (underwater simulation), and neural radiance fields with LiDAR. Recent trends focus on robust incremental smoothing, acoustic-optical fusion, and real-time volumetric mapping. Awards: Recognized with the RSS Test of Time Award (2020), Outstanding Associate Editor (2022), and paper awards at ICRA/ICRA. Active in conference organization (IROS/ICRA program committees). Advising & Grants: Supervises 10+ current PhD/MSc students, with past advisees contributing to CoRL/ICRA work. Manages grants in perception, autonomy, and marine robotics. Teaches courses like Robot Localization and Mapping (16-833). Labs/Teams: Directs RPL, collaborates with FRC on field robotics. Develops open-source tools like GTSAM (GNU Toolkit for Smoothing and Mapping).
Xingxing Zuo is an Assistant Professor (tenure-track) in the Robotics Department at MBZUAI. He holds a PhD from Zhejiang University (2021) and a Bachelor’s from UESTC (2016). Previously, he was a Postdoctoral Scholar at Caltech (2024–2025), a Postdoc at ETH Zurich (2019–2021), and held visiting roles at TU Munich, University of Delaware, and University of Technology Sydney. His research focuses on robotics, 3D computer vision, and embodied AI, with emphasis on robot-human collaboration, state estimation, and sensor fusion. Educations: PhD in Robotics, Zhejiang University (2021, with honors) Bachelor’s in Computer Science, University of Electronic Science and Technology of China (2016, with honors) Research Highlights: Develops novel methods for LiDAR-camera-inertial fusion, neural radiance fields, and radar-cameras systems Pioneered techniques like Flying Co-Stereo (long-range aerial mapping) and FMGS (vision-language embedded 3D splatting) Focuses on real-time SLAM, robust depth estimation, and photorealistic scene reconstruction Awards & Recognition: Best Paper Finalist at ICRA 2021 (CodeVIO) Oral Presentation at ICCV 2021 (MBA-VO) Recipient of Google Visiting Faculty Researcher (2023) Grants & Labs: Organized Thermal Infrared in Robotics workshop at ICRA 2025 Leads research on embodied AI and multi-sensor SLAM systems Develops open-source tools like LIC-Fusion and Coco-LIC frameworks
Olga Sorkine-Hornung is a Professor of Computer Science at ETH Zurich and head of the Institute of Visual Computing. She leads the Interactive Geometry Lab, focusing on theoretical and practical advancements in digital content creation, geometry processing, and shape modeling. Current position: ETH Zurich, Department of Computer Science Previous roles: Courant Institute (NYU), Technical University of Berlin Education: BSc and PhD from Tel Aviv University, postdoc at TU Berlin Her research spans shape representation, digital fabrication, computer animation, and fundamental geometry processing. Key contributions include Laplacian surface editing, as-rigid-as-possible deformation, and generalized winding numbers. She works on applications in VR, AR, and autonomous systems. Awards include Test of Time Awards (2024), ACM Fellow (2020), ERC Consolidator Grant (2020), and EUROGRAPHICS Young Researcher Award (2008). She has supervised numerous students and co-developed software libraries like libigl and Instant Meshes . Co-chair roles for SIGGRAPH, Eurographics, and Pacific Graphics Editorial board member for ACM Transactions on Graphics and other journals Keynote speaker at VMV, CVPR, and SIAM conferences Her work bridges mathematical rigor with practical implementation, advancing computer graphics and geometry processing through intuitive algorithms that maintain surface detail while enabling efficient computation.
Wei-Lun (Harry) Chao is an Associate Professor in the Department of Computer Science and Engineering at the Ohio State University (OSU), College of Engineering. Promoted to this role in May 2025, he is also an Innovation Scholar and Distinguished Assistant Professor of Engineering Inclusive Excellence. His work spans machine learning, computer vision, and their applications in autonomous driving, healthcare, biology, and natural language processing. Research Focus: Machine learning with imperfect data, interpretable and personalized learning, robust perception for autonomous systems, and visual recognition in real-world scenarios. Awards: 2025 OSU Early Career Distinguished Scholar Award, CVPR Best Student Paper Award (2024), CSE Faculty Teaching Award (2024), Lumley Research Award (2023). Grants: Funded by NSF, NIH, ONR, Cisco, AWS, and Google. Notable Research Trends: The 15 most recent articles highlight his work on vision foundation models, federated learning, diffusion models for biological species generation, interpretable vision transformers, and robust perception systems for autonomous driving. Key subfields include sparse autoencoders, 3D object detection, semi-supervised learning, and anomaly detection in scientific domains. Scientific Awards: 2025 Early Career Distinguished Scholar Award (OSU) CVPR Best Student Paper Award (2024) CSE Faculty Teaching Award (2024) Lumley Research Award (2023) Mentoring & Grants: As an advisor for the OSU Buckeye AutoDrive Team and AI Club, he mentors graduate and undergraduate students. His research is supported by major grants from NSF, NIH, ONR, and industry partners like Cisco and Google.
Ajmal Mian is a Professor of Computer Science at the University of Western Australia (UWA), affiliated with the School of Physics, Maths and Computing. He holds an Australian Research Council Future Fellowship (2022) and leads research in Artificial Intelligence, Computer Vision, and Machine Learning. His work focuses on 3D computer vision, adversarial AI defense, and explainable AI. His research interests include 3D point cloud analysis, face recognition, human action recognition, and remote sensing. He has published over 300 papers and secured major grants from ARC, NHMRC, and DARPA, totaling millions in funding. He has supervised 29 PhD students and mentored 12 postdoctoral researchers. Key projects include 3D diffusion models for scene generation, robust 3D vision systems, and defense against AI deception attacks. He serves as a fellow of IAPR, an ACM Distinguished Speaker, and has editorial roles at IEEE Transactions on Neural Networks and Pattern Recognition. Research Awards: HBF Mid-Career Scientist of the Year, West Australian Early Career Scientist of the Year, IAPR Best Scientific Paper Award. Grants: ARC Discovery Projects, National Intelligence & Security Discovery grants, DARPA grants for AI security. His teaching spans computer vision, machine learning, and programming courses. Collaborations include defense, medical, and agricultural applications.
Matthew O'Toole is an Associate Professor at Carnegie Mellon University's School of Computer Science, holding joint appointments in the Robotics Institute and Computer Science Department. His research focuses on computational imaging, integrating optics, electronics, and computational processing to innovate visual information capture and display. Education: PhD (Computer Science, University of Toronto, 2016), MSc (2009), BSc (Honors Computer Science and Mathematics, University of British Columbia, 2007). Prior roles include Banting Postdoctoral Fellow at Stanford University and visiting scholar at MIT Media Lab's Camera Culture group. Research interests emphasize programmable imaging systems, transient imaging, non-line-of-sight sensing, and holographic displays. Key innovations include vibration sensing via dual-shutter optics and radar super-resolution for autonomous vehicles. Awards include runner-up best paper recognitions at ICCV 2007, CVPR 2014, and SIGGRAPH 2017 dissertation honors. Advisees include Dorian Chan and Arjun Teh. Grants supported by Canadian Banting Fellowships. Active in workshop organization (CVPR Computational Cameras 2016-2017) and course development on computational imaging at SIGGRAPH 2014. Labs/Teams: Leads research in computational imaging and robotics at CMU, collaborating with industry partners like NVIDIA and MDA. Current projects explore LiDAR-radar fusion, holographic projection systems, and dynamic scene reconstruction.
Prof. Alois Christian Knoll is a full professor at the Technical University of Munich (TUM) in the School of Computation, Information and Technology. His academic career includes roles at Bielefeld University and leadership in major EU initiatives like the Human Brain Project and ECHORD++. He specializes in robotics, AI, and autonomous systems, with a focus on medical robotics, sensor-based systems, and neuromorphic engineering. Knoll has supervised over 100 doctoral theses and authored/co-authored over 1,000 publications. Education: Diploma in Electrical Engineering (University of Stuttgart, 1985); PhD in Computer Science (Technical University of Berlin, 1988); Habilitation (TU Berlin, 1993). He has been at TUM since 2001, leading the Robotics, AI, and Real-Time Systems department. Research interests span autonomous systems, neuro-IT integration, and traffic simulation. Key projects include fortiss (Bavarian State Institute for Computer Science) and TUM-CREATE (Singapore collaboration). Awards include IEEE Fellow, University of Tokyo Fellow, and the Carl-Ramsauer-Prize (1990). Current roles include editorships in robotics journals, leadership in EU flagship projects, and teaching across multiple programs. His work bridges computer science, neuroscience, and engineering, with applications in healthcare, automotive systems, and urban mobility.
Zahra Gharineiat is an Associate Professor at the University of Southern Queensland , affiliated with the School of Surveying and Built Environment . She has over 8 years of tertiary teaching experience and 11 years of administrative responsibilities. Bachelor of Surveying (BSurv), University of Tabriz Master of Engineering Management (MEngMgt), University of Melbourne PhD, University of Newcastle Research Interests : Zahra specializes in Geomatic Engineering and Machine Learning , with a focus on applications like Unmanned Aerial Vehicles (UAVs) , LiDAR , Digital Twins , and Remote Sensing . Her work spans Earth Science Observations , Satellite altimetry , and Geodetic data capturing , integrating Computational Modelling and Geoinformatics for innovative solutions. Professional Affiliations : She is a member of the Surveying and Spatial Sciences Institute (SSSI) and the International Union of Geodesy and Geophysics (IUGG) . Her research affiliations include the Centre for Future Materials (CFM) , Institute for Advanced Engineering and Space Sciences (IAESS) , and Centre for Astrophysics (CA) .
Mark Foster is an Associate Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, with a primary appointment in the Whiting School of Engineering. He is also a Fellow of the Hopkins Extreme Materials Institute. His research focuses on developing ultrahigh-speed optical systems at the intersection of photonics and electronics, emphasizing photonic devices and information theory to advance imaging, sensing, and communications technologies. Applications include quantum-optical systems, ultrawide-bandwidth microwave photonics, and terahertz-rate imaging systems. Dr. Foster received his BS (2003), MS (2007), and PhD (2008) in Applied and Engineering Physics from Cornell University. Before joining Johns Hopkins in 2010, he served as a postdoctoral associate there. His work has been funded by the NSF, IARPA, DTRA, and NIH, resulting in over 200 publications and eight patents. He has held leadership roles, including chairing the IEEE Photonics Society’s Baltimore chapter (2011–2014). Research Highlights: World-leading imaging systems achieving terahertz frame rates Quantum-optical platforms and nonlinear photonic materials (e.g., NbTiOx) Secure authentication via physically unclonable functions (PUFs) Applications in fusion energy diagnostics and medical imaging His awards include the NSF CAREER Award (201?), DARPA Young Faculty Award, and ONR Young Investigator Award. Current projects explore machine learning-resistant PUFs, multi-modal imaging systems, and photonics for extreme environments.
Perla Maiolino serves as an Associate Professor in Engineering Science at the University of Oxford and Principal Investigator of the Soft Robotics Lab (SRL) within the Oxford Robotics Institute. Her academic foundation includes BEng, MEng, and PhD degrees in Robotics and Automation from the University of Genoa, where she pioneered CySkin technology for distributed tactile sensing in robots—later exhibited at the Science Museum in London. She expanded her expertise during a 2017-2018 postdoctoral fellowship at Cambridge University's Biologically Inspired Robotics Lab, focusing on soft robotics and tactile perception. Dr. Maiolino's research centers on developing artificial skin systems, soft robotic actuators, and distributed sensing architectures. Her work bridges biological inspiration with engineering innovation to create robots capable of safe human interaction and dexterous manipulation in unstructured environments. Key contributions include compliant beaded-string jamming mechanisms for anthropomorphic fingers, monolithic 3D-printed soft pneumatic arms (JAMMit!), and distributed time-of-flight sensor networks for robotic self-awareness. Recent publications (2024-2025) reveal a strong convergence of tactile sensing with machine learning, featuring optical flow for gesture recognition, diffusion models for artificial skin simulation, and zero-shot sim-to-real transfer techniques. Her team has made significant advances in multi-modal sensing integration, variable stiffness actuation, and scene flow estimation for robots operating in dynamic surroundings. Scientific Awards No specific awards were documented in the provided institutional materials. Advising and Grants While her leadership of the Soft Robotics Lab implies active student supervision and grant management, detailed information about advisees or funded projects was not included in the source documentation. Labs and Teams As Principal Investigator of the Soft Robotics Lab at Oxford Robotics Institute, Dr. Maiolino directs research on tactile perception systems, soft actuation mechanisms, and sensor-integrated robotic structures. The lab's work focuses on applications requiring safe physical interaction, including healthcare robotics and human-robot collaboration scenarios, with emphasis on multi-material 3D printing and embedded sensing technologies.
Karsten Lambers is Professor of Digital and Computational Archaeology at the Faculty of Archaeology, Leiden University, where he leads research and teaching in the application of computational methods to archaeological data. His work integrates machine learning, remote sensing, text mining, and citizen science to advance archaeological prospection and heritage management. He is affiliated with the Department of Archaeological Sciences and plays key roles in research groups and university-wide initiatives such as SAILS and ARCHON. His research interests span Digital Archaeology , Machine Learning in Archaeology , Remote Sensing , Geoarchaeology , and Human-Environment Interaction . He investigates how computational tools can extract meaningful archaeological information from large datasets, including LiDAR imagery and excavation reports. His fieldwork spans Central Europe and Latin America, with a focus on prehistoric landscapes and cultural heritage. The analysis of his recent publications reveals a strong trend toward automated detection using deep learning (e.g., R-CNN, WODAN), named entity recognition in archaeological texts (e.g., ArcheoBERTje), and citizen science integration for data validation. His work bridges archaeology with computer science, geomatics, and environmental science, emphasizing interdisciplinary collaboration and methodological rigor. His scientific awards include: Best Thesis Award (University of Zurich, 2005) EUROPA NOSTRA Award (2020, 2022) Membership in the German Archaeological Institute (since 2022) Lambers actively supervises students and leads major research projects such as ABMA, EXALT, and Heritage Quest. He has secured substantial research funding and collaborates widely with computer scientists, geophysicists, and palaeoecologists. His teaching includes digital methods, modeling, and simulation, often linked to ongoing research. He has also contributed to open educational resources and digital textbooks in archaeology. He leads or participates in several research labs and teams, including the Digital Archaeology Research Group (which he chairs), the Heritage Quest citizen science project, and interdisciplinary teams focusing on alpine terraces and Iraqi prospection. His work emphasizes the integration of digital tools into practical archaeological workflows, advocating for complementary human-computer strategies.
Di Zhu is a Presidential Young Professor in the Department of Materials Science and Engineering at the National University of Singapore (NUS). He holds a B.Eng. from Nanyang Technological University and M.Sc./Ph.D. from MIT, both in Electrical Engineering. His postdoctoral research at Harvard focused on lithium niobate integrated photonics and superconducting detectors. He previously worked as a research scientist and PI at A*STAR's Institute of Materials Research and Engineering (IMRE). Research Interests : Integrated quantum photonics, superconducting detectors, nonlinear optics, and nanofabrication. His group develops scalable quantum photonic devices using lithium niobate and superconducting materials, emphasizing applications in quantum computing, communication, and sensing. Awards : National Research Foundation (NRF) Fellowship Harvard Quantum Initiative (HQI) Postdoctoral Fellowship MIT Jin-Au Kong Thesis Award Advising & Recruitment : Actively recruiting postdocs, PhD students, and interns in areas like integrated photonics, quantum optics, and superconducting detectors. Positions include work on thin-film lithium niobate, quantum simulation, and microwave-optical transduction. Group website: dizhulab.org .
Prof. Marc Stamminger is a Professor of Visual Computing at FAU since 2002, leading the Chair of Computer Science 9 (Computer Graphics). His work focuses on algorithms for synthesizing and analyzing images through 3D modeling, LiDAR/Radar capture, and light simulation. He co-leads FAU Solar, applying 3D modeling for environmental lighting analysis under varying conditions. Stamminger has published over 250 papers, winning prestigious awards like the Siggraph Test-of-Time Award. He holds executive roles in Eurographics and is Vice Dean of FAU's Technical Faculty. Research interests span neural rendering , 3D reconstruction , radar imaging , and medical visualization . Recent work emphasizes radiance field rendering (e.g., VR-Splatting, INPC) and radar-based human motion tracking. His lab's FAU Solar project integrates large-scale 3D models with environmental lighting simulations. Publications trends highlight neural rendering optimizations , radar-MIMO systems , and agricultural digital twins . Key collaborations involve medical imaging (e.g., vocal fold reconstruction) and autonomous driving data generation. Awards: Siggraph Test-of-Time (2023?), 2× Siggraph Best-Of-Show Grants/Teams: FAU Solar Lab, Eurographics leadership, FAU Vice Dean Labs: Chair of Computer Science 9, FAU Solar Initiative
Bharat Lohani is a Professor at the Department of Civil Engineering in Indian Institute of Technology Kanpur . His work focuses on Geoinformatics , utilizing LiDAR and GIS for 3D modeling and spatial analysis. Education : PhD (University of Reading, UK), ME (IIT Roorkee), BE (MMM Engineering College Gorakhpur) Research Interests : LiDAR simulation, motion correction in scanning, flood propagation modeling, historical urban mapping, GPS signal analysis, agricultural land consolidation Article Trends : His publications emphasize LiDAR data processing , sensor integration , and optimization algorithms for geospatial applications. Scientific Awards : ISRS National Award (2012), Best Paper at INCA Congress (2012), Silver Award at ISPRS (2008) Advising : Mentored 8+ students in LiDAR and spatial data analysis Labs/Teams : Leads a research group at IITK including Aswani Kumar Munnangi and Salil Goel, affiliated with the Wadhwani School of AI & Intelligent Systems .
