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.
Sanjiv Singh is a Research Professor at the Robotics Institute within Carnegie Mellon University's School of Computer Science. His academic journey at CMU spans from Systems Scientist (1995-2001), to Senior Research Scientist (2001-2003), Associate Research Professor (2003-2007), and finally Research Professor since 2007. He also holds an adjunct faculty position in Mechanical Engineering since 2009. Singh serves as Editor-in-Chief of the Journal of Field Robotics, demonstrating his leadership in the robotics community. His educational background includes a Ph.D. and M.S. in Robotics from Carnegie Mellon University (1995, 1992), an M.S. in Electrical Engineering from Lehigh University (1985), and a B.S. in Computer Science from the University of Denver (1983). Dr. Singh's research focuses on three primary themes: Autonomous Navigation (developing motion planning and control for ground and air vehicles with applications in agriculture, exploration, and low-flying aircraft), Coordinated Multi-Robots (examining team-based tasks like structure assembly and search/rescue operations), and Forceful Interaction with the world (using physical models to enable robots to handle complex, high-force interactions). His work spans aerial robotics, agricultural and forestry robotics, mining robotics, 3D vision, sensing and perception, visual servoing, motion planning, and field service robotics. Analysis of his recent publications (2016-2020) reveals a strong focus on collision avoidance algorithms, sensor fusion techniques, and real-time navigation systems. His research demonstrates consistent advancement in SLAM (Simultaneous Localization and Mapping) technologies, particularly in GPS-denied environments, with increasing sophistication in handling complex aerial maneuvers and multi-robot coordination. Editor-in-Chief of Journal of Field Robotics Dr. Singh has advised numerous graduate students throughout his career, with current advisees including Matt Aasted (Ph.D), Andrew Chambers (M.S), Hugh Cover (M.S), Michael Dille (Ph.D), and Justin Haines (M.S). His past students include prominent researchers like Sebastian Scherer, Joe Djugash, Fred Heger, Geoff Hollinger, and Ji Zhang who have gone on to make significant contributions in robotics. His research has been supported through various projects including CASC (agricultural applications), Riverine, Transformer, Trestle, and Ember. His laboratory work focuses on developing practical robotic systems capable of operating in challenging real-world environments, with particular emphasis on agricultural applications, search and rescue operations, and coordinated multi-robot teams that can work effectively alongside humans.
Giorgio Grisetti is a Full Professor at Sapienza University of Rome within the Department of Systems and Computer Science, maintaining active research roles in the RoCoCo lab at Sapienza since November 2010 and the Autonomous Intelligent Systems Lab at Freiburg University where he previously served as a Post Doc under Wolfram Burgard starting in 2006. His educational background includes a M.Sc. in Computer Engineering from the University of Rome (2001) and a Ph.D. from Sapienza University of Rome's Intelligent Systems Lab (2006), supervised by Daniele Nardi. His doctoral thesis focused on SLAM using Rao-Blackwellized particle filters. Dr. Grisetti's research centers on mobile robotics with emphasis on robust solutions for autonomous navigation systems. His work spans theoretical and practical advancements in Simultaneous Localization and Mapping (SLAM), robot localization, path planning, and sensor fusion, particularly leveraging LiDAR and multi-sensor configurations. Recent publications demonstrate strong focus on optimization techniques, sensor calibration, and real-time performance for autonomous systems operating in complex environments. His publication trends reveal deep specialization in LiDAR-based SLAM (7 of 15 recent articles), bundle adjustment methods (4 articles), and sensor calibration/perception (3 articles), with consistent contributions to top robotics venues like IEEE Robotics and Automation Letters and ICRA. Key recognitions include: Nomination for the best IROS paper award (2010) Open Source achievement award from Willow Garage (2010) Best paper award at the International Conference and Exhibition on Unmanned Areal Vehicles (2010) Best Paper award at ICRA 2009 (2009) His research is conducted through the RoCoCo lab at Sapienza University of Rome and the Autonomous Intelligent Systems Lab at Freiburg University, focusing on developing foundational algorithms for mobile robot autonomy. Current projects emphasize robust perception systems, optimization frameworks for sensor fusion, and practical implementations for real-world navigation challenges.
Kim Jae-ho serves as Associate Professor in the Department of Electronic Information and Communication Engineering at Sejong University since September 2020, concurrently directing the Metaverse Autonomous Twin Research Center (ITRC) under the Ministry of Science and ICT. His leadership extends to the National Smart City Committee and TTA Internet of Things/Smart City Platform PG, with research focusing on hyper-connected autonomous intelligence systems for smart city applications. His research program centers on three interconnected pillars: (1) On-Device/Edge/Cloud-based autonomous intelligence architectures enabling distributed decision-making, (2) Spatial/situational awareness systems for intelligent environments, and (3) Collaborative intelligence frameworks for unmanned vehicle networks. This work bridges theoretical AI with real-world deployment in IoT ecosystems and metaverse applications, emphasizing practical implementations for societal benefit. Recent publications (2023-2025) reveal a strategic shift toward metaverse-autonomous system integration, with 68% of articles addressing digital twin alignment, radar/vision sensor fusion, and multimodal AI for robotics. Key trends include UAV swarm coordination (23% of works), battery life prediction for industrial IoT (15%), and large language model integration for robotic perception (12%), demonstrating consistent focus on deployable autonomous intelligence solutions. His scientific recognition includes six major awards: Minister of Land, Infrastructure and Transport Award for Smart City contributions (2020) National Academy of Engineering of Korea's '100 Technologies Leading Korea 2025' (2017) Prime Minister's Commendation for Science/Technology Promotion (2016) Minister of Trade, Industry and Energy Technology Award (2016) KETI Person of the Year (2016) Minister of Science ICT Future Planning SW R&D Award (2014) Professor Kim actively mentors graduate researchers through doctoral and master's thesis supervision while managing $12.7M in active grants including the 7-year Metaverse Autonomous Twin ITRC (2021-2028) and Connected Intelligent Sensor Platform project (2022-2028), with recent funding targeting UAV safety interfaces and industrial IoT battery systems. He leads the Autonomous Intelligent Systems (AISL) Laboratory at Ocean AI Center 529, which integrates government-funded research with industry partnerships to develop deployable autonomous intelligence solutions for smart cities and metaverse applications.
Giulio Dagnino is Associate Professor of Robotics and Mechatronics at the University of Twente and concurrently holds an appointment at the Digital Society Institute. His research integrates medical robotics, real-time perception and haptics to create MR-compatible platforms for endovascular surgery, earning an h-index of 17 and 971+ citations. Education & Career: PhD (details not specified in source) leading to faculty appointment at University of Twente. Promoted to Associate Professor with cross-appointments in Robotics & Mechatronics and Digital Society Institute. Research Interests: Prof. Dagnino’s core interest is medical robotic systems that can operate safely inside an MRI scanner. His work spans haptic guidance, real-time computer vision, soft robotic actuation, synthetic data generation and surgical simulation. By combining ferrofluid actuation, electromagnetic tracking and deep-learning-based scene understanding, he aims to reduce ionizing radiation exposure, enhance navigation accuracy and shorten procedure times for minimally invasive endovascular interventions. Publications Trend: Across 44 outputs (2010-2025) the portfolio reveals a clear evolution from early vision-based microsurgery and fracture-robot systems (2010-2016) toward holistic endovascular platforms integrating MR guidance, haptics and autonomy. Recent 2024-25 papers cluster around (i) synthetic data & scene understanding for surgical AI, (ii) MR-safe robot design and tracking, and (iii) translational studies bringing CathBot and related platforms closer to clinical use. Scientific Awards: Best Design Award – Hamlyn Symposium 2019 (with team) Best Innovation Award – ICRA 2018 Best Paper Award – CURAC 2019 IEEE ICRA Best Paper Award in Medical Robotics – 2016 Grants & Projects: Although explicit grant numbers are not listed, the continuous outputs, patents, multi-institutional collaborations (UK, Germany, Estonia, Canada) and press releases imply sustained funding from EU, Dutch and UK research councils as well as industrial partnerships. Labs & Teams: He leads activities within the Robotics and Mechatronics group at University of Twente, collaborates closely with the Digital Society Institute, and maintains international partnerships visible in co-authored papers with Imperial College London, University of Leeds, and several European hospitals.
Danijel Skočaj is Full Professor at the University of Ljubljana, Faculty of Computer and Information Science , and serves as Head of the Visual Cognitive Systems Laboratory . He is an internationally recognized researcher in computer vision, machine learning, and cognitive robotics , with a strong focus on deep-learning solutions for real-world visual perception tasks and their ethical implications. Education: While specific degrees are not listed in the text, Professor Skočaj’s 2002 “Best PhD paper award” confirms he holds a PhD in the relevant field. Research Interests: His work spans Computer Vision & Pattern Recognition Deep Learning & Neural Networks Cognitive Robotics & Autonomous Navigation Visual Anomaly & Surface-Defect Detection AI Ethics & Societal Impact of AI These interests manifest in both theoretical advances and practical systems deployed in industry and public infrastructure. Publication Trends: Recent papers (2020-2024) emphasize deep-learning architectures for defect detection, robotic grasping, autonomous navigation, traffic-sign recognition, and 3-D anomaly detection , demonstrating a clear trajectory toward robust, real-time, and data-efficient visual intelligence. Awards & Honors: Prometheus of Science Award 2021 (Slovenian Science Foundation) Golden Plaque, University of Ljubljana 2020 ARRS National Award for Exceptional Scientific Achievement 2011 & 2022 Multiple Best-Paper awards at ERK conferences (2013, 2017, 2019) Top-downloaded paper recognition, Journal of Intelligent Manufacturing 2020 Grants & Projects: He currently leads or co-leads five major 2025-2028 national and EU projects (RTFM, SMASH, COMET, RoDEO, MUXAD) totaling several million Euros, focusing on advanced computer vision, machine learning for science & humanities, autonomous systems, and explainable AI. Past leadership includes EU FP7 CogX, GOSTOP, ViLLarD, and many ARRS programmes. Laboratory & Team: The Visual Cognitive Systems Laboratory hosts a dynamic group of doctoral and master’s students working on cutting-edge perception systems. The lab’s open-source low-cost robotic platform and datasets are widely adopted for education and research.
Yoann Altmann is Professor in the School of Engineering & Physical Sciences at Heriot-Watt University and a member of the Institute of Sensors, Signals & Systems. Since 2024 he holds the Chair in Electrical, Electronic & Computer Engineering (EECE), directing a research programme that bridges statistical signal processing, computational imaging and quantum & neuromorphic sensing. Education & career: 2010 – Eng. degree (Electrical Engineering), ENSEEIHT, Toulouse, France 2010 – M.Sc. (Signal Processing), National Polytechnic Institute of Toulouse 2013 – Ph.D. (Signal & Communications), IRIT Laboratory, Toulouse 2014-2017 – Post-doctoral Research Fellow, Heriot-Watt University 2017 – Royal Academy of Engineering Research Fellow & Assistant Professor, HWU 2024 – promoted to Professor, School of Engineering & Physical Sciences, HWU Research interests: Prof. Altmann develops mathematical and algorithmic tools for Bayesian inverse problems, with emphasis on single-photon LiDAR, low-illumination imaging, neuromorphic computational sensing, variational inference and sparse reconstruction. His work combines principled statistical modelling with efficient computational schemes to enable imaging in extreme scenarios such as underwater scattering, photon-starved environments, quantum metrology and real-time 3-D scene reconstruction. Publication trends: Across 160 outputs (2011-2025) his recent articles reveal a clear trajectory toward integrating modern machine-learning paradigms—variational autoencoders, diffusion generative models, spiking neural networks—with rigorous physics-based forward models. Applications span quantum parameter estimation, multimode-fiber endoscopy, hyperspectral & Compton imaging, nuclear safeguards and cultural-heritage spectroscopy, demonstrating both methodological breadth and high-impact interdisciplinary deployment. Honours & recognition: Royal Academy of Engineering Research Fellowship – competitively awarded (2017) Grants & datasets: He has generated four open datasets supporting reproducible research in quantum sensing, variational autoencoders, underwater single-photon LiDAR and multispectral fluorescence imaging, reflecting sustained funding and commitment to open science. Continuous peer-review service for IEEE and Elsevier journals since 2013 underlines his standing within the signal-processing community. Labs & teams: He leads the Bayesian Imaging & Sensing Computing (BISC) group ( https://bisc.site.hw.ac.uk ) which hosts post-docs, PhD researchers and international visitors working on statistical machine-learning for imaging, sensing and quantum technologies.
National and Kapodistrian University of AthensGreece
Dimitris Maroulis is a Professor at the Department of Informatics and Telecommunications, University of Athens, leading the Real-Time Systems and Image Analysis Lab (RTS-image). With over 20 years of experience in data acquisition and real-time systems, and 15 years in image/signal analysis, he collaborates extensively with Greek and European hospitals in biomedical informatics. He has led 5 R&D projects and authored 150+ papers with 1400+ citations. University of Athens: Professor (2000–present) Meudon Observatory: Research Fellow (3 years) & Long-term Collaborator (10+ years) Research Interests focus on real-time systems , image/signal processing , and biomedical applications . Key areas include automated segmentation of proteomic images, noise removal methods, and stereo image coding. His 15 most recent publications (2003–2012) span 3D imaging , medical image analysis , and biomedical informatics , with sub-fields like autostereoscopic displays, wavelet-based coding, and computer-aided diagnosis. Awards : Best Paper Award (2012: Integral Image Analysis) Projects include European and national R&D initiatives in image analysis and real-time systems. Labs : RTS-image Lab develops methodologies for biomedical and proteomic applications.
Professor John Shi Wen-zhong is Chair Professor of Geographical Information Science and Remote Sensing at The Hong Kong Polytechnic University, where he serves as Head of the Department of Land Surveying and Geo-Informatics. He also holds leadership positions as Director of the Otto Poon Charitable Foundation Smart Cities Research Institute and Director of the PolyU-Shenzhen Technology and Innovation Research Institute (Futian). Professor Shi is recognized as an international leader in uncertainty modeling and quality control for spatial data and spatial analyses, with contributions dating back to the 1990s. He currently serves as President of the International Society for Urban Informatics and Editor-in-Chief of the international journal Urban Informatics. Professor Shi's research focuses on urban informatics for smart cities, geographical information science and remote sensing, artificial intelligence-based object extraction and change detection from satellite imagery, intelligent analytics and quality control for spatial big data, and mobile mapping and 3-D modelling based on LiDAR and remote sensing imagery. His work has solved fundamental uncertainty issues in spatial data and spatial analyses, making significant contributions to geographical information science. He has authored over 300 research articles in Web of Science-indexed journals and 20 books, and has been granted 44 patents as of July 2023. Professor Shi has received numerous prestigious awards for his groundbreaking work: ESRI Award for Best Scientific Paper by the American Society for Photogrammetry and Remote Sensing (2006) State Natural Science Award (Second Award), China's highest award for fundamental research (2007) Wang Zhizhuo Award by the International Society for Photogrammetry and Remote Sensing (2012) Founder's Award by the International Spatial Accuracy Research Association (2020) CPGIS Distinguished Scholar Award (2021) Gold Medals at both the 2021 and 2023 Geneva Invention Expos Smart 50 Awards (2021) Gold Medal in Asia International Innovative Invention Exhibition (2023) He is also listed among the world's top 2% most cited researchers according to Elsevier BV's standardized citation indicators. Professor Shi has been elected as an Academician of the International Eurasian Academy of Sciences and is a Fellow of the Academy of Social Sciences (UK), the Royal Institution of Chartered Surveyors, and the Hong Kong Institute of Surveyors.
Dominique Bechmann is a **Professor of Computer Science** at the University of Strasbourg, affiliated with the **Department of Computer Science** within the **Sciences Collegium**. He is also a researcher at the **ICube Lab** (UMR 7357 CNRS-University of Strasbourg). His academic career spans over three decades, including roles as Head of the IGG Computer Graphics and Geometry research group (1997–2022) and Head of the National Research Group GdR IG-RV (2014–2021). He holds a Habilitation (1995) and PhD (1989) from the University of Strasbourg, with postdoctoral research at IBM's Thomas Watson Research Center (1989–1990). His research focuses on **Computer Graphics**, **Geometric Modeling**, and **Virtual Reality**, with key contributions in free-form deformation, 3D modeling of anatomical structures, and interaction techniques in immersive environments. He has led projects like the ICT-Asian initiative on Virtual Reality (2004–2007) and organized conferences such as AFRV 2012 and AFIG-EG France 2005. His teaching spans undergraduate and graduate levels in algorithms, computer graphics, and computational geometry. Bechmann has held numerous leadership roles, including Head of the Computer Science Department (1997–2000), Vice-Head of LSIIT Lab (2009–2012), and member of the National Commission of Universities (CNU) section 27 (2007–2011). His work bridges theoretical computer graphics with practical applications in medicine, architecture, and collaborative systems.
Isabelle Bloch is a full Professor at Télécom Paris (Institut Polytechnique de Paris) and Emeritus Professor at the University of Bordeaux . She heads the Image, Modeling, Analysis, Geometry, Synthesis (IMAGES) research team within the Information Processing and Communication Laboratory (LTCI) in the Image, Data, Signal (IDS) Department . Education & Affiliations Professor – Télécom Paris, Institut Polytechnique de Paris (current) Professeure Émérite – University of Bordeaux (honorary) Research Interests Her work lies at the intersection of computer science, applied mathematics and medicine . Core themes include mathematical morphology , fuzzy and bipolar logics , 3-D image interpretation , discrete 3-D geometry & topology , information fusion , structural pattern recognition , spatial reasoning and medical imaging . Recent projects extend these concepts to explainable AI , argumentation theory , computational musicology and robotic surgery guidance . Scientific Awards Blondel Medal (2008) – awarded by the French Electrical & Electronics Engineering Society to outstanding young scientists. Grants & Collaborative Projects While specific grant numbers are not listed in the text, Prof. Bloch coordinates and participates in large-scale interdisciplinary projects funded by national (ANR) and European programs, spanning medical AI, pediatric oncology imaging, neuro-informatics and ethical AI. Labs & Teams She leads the IMAGES team (≈ 25 researchers) inside the LTCI – a joint research unit of Télécom Paris and CNRS. The group develops theory and open-source software in mathematical morphology, deep learning and symbolic AI, and collaborates closely with clinical partners at Necker–Enfants Malades Hospital, AP-HP and Gustave Roussy.
Mahanth Gowda is an Associate Professor in the Department of Computer Science and Engineering, leading a prolific research program at the intersection of mobile systems, wireless sensing, and human-computer interaction. His work has been continuously funded by the U.S. National Science Foundation since 2019, serving as Principal Investigator on four awards and Co-PI on two others, collectively spanning edge computing for XR, sign-language recognition, next-generation wireless networking, and healthcare-oriented wearables. Research Interests Millimetre-wave and ultra-wideband sensing for 3-D finger motion tracking and speech eavesdropping Edge-IoT platforms for real-time sign-language recognition and translation Neural-augmented game streaming and super-resolution on commodity mobile devices Open-source wearable systems for sports analytics and rehabilitative healthcare Security and privacy implications of motion sensors in smartphones and IoT devices Over the past five years his publication trajectory has concentrated on leveraging emerging radio modalities—especially millimetre-wave radar, Wi-Fi, and UWB—to extract fine-grained human-centric information such as finger gestures, facial micro-motions, and spoken content. A complementary thread develops edge-native machine-learning frameworks that push intelligence to resource-constrained devices, enabling immersive AR/VR experiences and assistive technologies for the Deaf and hard-of-hearing communities. Grants & Projects CAREER: Sign-to-Speech – NSF, $500k, 2021-2026; Edge-IoT platform for real-time ASL recognition. SHF: Medium: Next-Gen XR Edge Platform – NSF, $1.2M, 2022-2025; Co-PI with Das, Sivasubramaniam, Kandemir. CNS Core: IoTScope – NSF, $450k, 2020-2025; Sensing physical materials via low-cost IoT radios. CNS Core: Medium: ML-driven Next-G Wireless – NSF, $800k, 2020-2024; Co-PI with Yang and Mahdavi. I-Corps: Smart Ring for Healthcare Analytics – NSF, $50k, 2023-2025; Commercialization of finger-motion wearables. Labs & Teams Gowda directs a research group that operates at the confluence of wireless networking, embedded systems, and applied machine learning. The lab maintains active collaborations with faculty in computer architecture, augmented reality, and accessibility studies, and routinely mentors graduate researchers whose work appears in top-tier venues such as ACM MobiCom, IEEE INFOCOM, ISCA, and ACM IoTDI.
Schloss Dagstuhl - Leibniz Center for InformaticsGermany
Dr. Chenyang Zhao is an Assistant Professor in the Department of Computer Science and Engineering at Shanghai Jiao Tong University's School of Electronic Information and Electrical Engineering. His research spans multiple domains at the intersection of computer vision, machine learning, and engineering applications. Dr. Zhao's primary research interests include: Computer Vision and Deep Learning Robotics and Autonomous Systems Medical Imaging and Healthcare Applications Hardware Acceleration for AI Precision Measurement and Instrumentation His recent work demonstrates a strong focus on practical applications of AI across diverse domains, from medical imaging to infrastructure inspection. Dr. Zhao has developed innovative approaches in areas such as sewer inspection systems using evidential deep learning, RGB-D semantic SLAM for robotics, and lightweight computing-in-memory architectures for edge AI applications. His research shows a consistent pattern of addressing real-world engineering challenges with cutting-edge machine learning techniques, particularly emphasizing trustworthiness, uncertainty quantification, and practical implementation constraints. Dr. Zhao's laboratory focuses on developing trustworthy AI systems with applications in: Infrastructure monitoring and maintenance Medical diagnostics and imaging Autonomous robotic systems Energy-efficient AI hardware
Andrej Novak is an Associate Professor at the Theoretical Physics Department , Faculty of Science, University of Zagreb. He holds a PhD in Mathematical Models of Flow in Porous and Mixed Media (2017) and a Master's in Mathematical Model of Piano String (2011), both from University of Zagreb. Research Interests span: (1) Partial Differential Equations & Mathematical Modeling, (2) Numerical Solutions of PDEs, (3) Data Analysis Algorithms (bioinformatics/medical applications). Current focus includes shock filter equations, medical image processing, and cardiovascular pharmacotherapy modeling. Key Projects include leading the 2024 HRZZ-funded 'From PDEs to Deep Learning: Advancing Medical Image Processing' and contributing to projects like 'Analysis of partial differential equations and shape optimization' (HRZZ, 2023). He has participated in international collaborations including Austrian-funded 'Vanishing Capillarity on Smooth Manifolds' (2019). Teaching responsibilities include courses like Computational Neuroscience, Introduction to Computer Science, and Numerical Mathematics. He advises on C++ programming, AI fundamentals, and mathematical modeling across undergraduate and graduate levels. Publications highlight contributions in journals like Archive for Rational Mechanics and Analysis (2024), Applied Soft Computing (2025), and Canadian Journal of Cardiology (2025), focusing on interdisciplinary applications of PDEs, machine learning in healthcare, and image processing techniques.
Francesca Odone serves as a Full Professor in the Department of Computer Science, Bioengineering, Robotics, and Systems Engineering (DIBRIS) at the University of Genoa, Italy. She holds a position on the Department Board and teaches core courses including Computational Vision , Algorithms and Data Structures , and Fundamentals of Signal and Image Processing across undergraduate and graduate programs in Computer Science and Biomedical Engineering. Her research centers on Computer Vision and Machine Learning with critical applications in Biomedical Engineering . Key focus areas include markerless motion analysis for neurological disorders (particularly multiple sclerosis), video-based assessment of motor functions in spinal cord injury and preterm infants, and deep learning for medical image interpretation . She integrates robotics and signal processing to develop non-invasive clinical diagnostic tools, emphasizing practical healthcare solutions through interdisciplinary collaboration. Analysis of her 15 most recent publications (2024-2025) reveals three dominant trends: (1) Proliferation of markerless video-based clinical assessment tools for gait/motion analysis, (2) Advanced deep learning architectures (diffusion models, disentangled representations) applied to medical imaging challenges, and (3) Cross-cutting work on AI fairness/debiasing techniques. Her research consistently bridges computer vision with neurology, rehabilitation medicine, and neonatology. Professor Odone actively contributes to DIBRIS's research ecosystem through ongoing projects in biomedical computer vision, with strong connections to clinical partners. Her work demonstrates sustained focus on translating computer vision innovations into practical healthcare applications, particularly for neurological and developmental conditions.
