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.
Deva Ramanan is a Professor at the Robotics Institute of Carnegie Melllon University, where he leads research in computer vision and machine learning. His work focuses on modeling human visual perception, leveraging large-scale visual data, and developing systems for 3D understanding, neural rendering, and autonomous systems. He advises a large group of PhD students and has mentored numerous postdoctoral researchers now in leading roles across industry and academia. His research interests include computer vision, machine learning, human perception modeling, 3D scene understanding, neural rendering, autonomous driving, video understanding, and multimodal foundation models. These areas reflect his focus on both foundational models and their application to real-world problems in robotics and AI. The recent publications highlight a strong trend toward multimodal and 3D-aware models, with increasing use of diffusion models, neural fields, and large vision-language systems. Key themes include scene flow, 3D reconstruction from monocular video, autonomous driving perception, and robust evaluation of vision-language models. There is a clear emphasis on both methodological innovation and practical deployment in dynamic environments. Marr Prize, Honorable Mention (ICCV 2021) Best Paper, Honorable Mention (ECCV 2020) Best Paper Finalist (WACV 2024) Best Paper Award (WACV 2016) Best Industrial Paper, Honorable Mention (BMVC 2017) Marr Prize winner (ICCV 2009) Deva Ramanan has advised numerous PhD and master’s students, many of whom are now at top institutions and companies including Apple, Meta, Google, Nvidia, OpenAI, and Princeton. He has received substantial funding from IARPA, DARPA, NSF, Intel, Google, and Facebook for projects in video analytics, dispersed computing, visual cloud systems, and multi-task recognition. His group has developed influential datasets and benchmarks used widely in the community. He leads a vibrant research lab focused on advancing computer vision through deep learning and multimodal integration. His team works on core challenges in perception, including 3D reconstruction, motion modeling, object detection, and scene understanding, with applications in robotics and autonomous systems.
Prof. Dr. Dennis Säring is a faculty member at the University of Applied Sciences Wedel , specifically affiliated with the School of Engineering. His academic and research activities focus on Deep Learning , Medical Image Analysis , and applications of Artificial Intelligence in healthcare and biomedical imaging. He has led seminars on Deep Learning topics and supervised student projects in Autonomous Driving at Audi's AADC 2018 competition. Research Highlights : Cardiovascular imaging, forensic age estimation via MRI, neural network-based bone segmentation, and cerebrovascular aneurysm analysis. Technical Expertise : Cardiac MRI, 3D/4D image processing, parametric mapping, and spatiotemporal data fusion. His recent publications (2018-2023) emphasize 3D MR segmentation for age assessment, CMR strain analysis in athletes, and T1/T2 mapping for myocarditis. Key collaborations include institutions like the University Medical Center Hamburg-Eppendorf and Wedler Hochschulbund, with funding for autonomous vehicle research. While no explicit scientific awards are listed, his work spans clinical cardiology, forensic radiology, and AI-driven medical diagnostics.
Max Planck Institute for Intelligent SystemsGermany
Michael J. Black is a Professor and Honorarprofessor at the University of Tübingen's Faculty of Science, Department of Computer Science, and a founding Director of the Max Planck Institute for Intelligent Systems, leading the Perceiving Systems department. He holds a B.Sc. from the University of British Columbia (1985), M.S. from Stanford (1989), and Ph.D. in Computer Science from Yale (1992). His research focuses on computer vision, 3D human modeling, motion capture, and AI-driven digital humans. Key contributions include the SMPL body model, optical flow algorithms, and datasets like Middlebury Flow and Sintel. He has received major awards such as the PAMI Distinguished Researcher Award, multiple Koenderink and Longuet-Higgins Prizes, and is a member of the German National Academy of Sciences Leopoldina and Royal Swedish Academy of Sciences. His commercial ventures include co-founding Body Labs (acquired by Amazon) and Meshcapade, advancing 3D human generation and interaction technologies. Recent work includes markerless motion capture systems (e.g., MAMMA, PICO), 3D hair and garment synthesis, and AI tools like ChatHuman for 3D human interaction analysis. His research bridges vision, graphics, and robotics, with applications in animation, healthcare, and robotics.
Dylan Campbell is a Lecturer in Computing at the Australian National University (ANU), affiliated with the ANU College of Systems & Society. His research focuses on computer vision, optimization, and robotics, particularly in 3D vision and deep learning applications. He has held prior roles as a Research Fellow at the University of Oxford’s Visual Geometry Group and ANU’s Australian Centre for Robotic Vision. Campbell holds a PhD from ANU (2018) and a BE in Mechatronic Engineering from UNSW (2012). Research interests include geometric sensor alignment, neural radiance fields, and differentiable optimization layers. He actively supervises students (7 PhD/DPhil, 3 MEng, 9 honours) and teaches advanced courses in computer vision and robotics. Notable awards include the Marr Prize Honourable Mention (2017) and the IEEE Australia Council Postgraduate Student Paper Competition (2018). He has organized workshops at ECCV and CVPR, served as a reviewer for top conferences like CVPR/ICCV/ECCV, and contributed to datasets like SEED4D and RefRef. His work emphasizes efficient training of neural networks and leveraging symmetries in data for long-range connections.
Max Planck Institute for Intelligent SystemsGermany
Michael J. Black is a Professor and Director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, where he leads the Perceiving Systems department and serves as Managing Director . He is also an Honorarprofessor at the University of Tübingen 's Faculty of Science . His career spans roles at Brown University (2000-2010), Xerox PARC, and academic-industry collaborations with Amazon and Meshcapade.
Associate Professor Mohsen Kalantari is a Geospatial Engineering academic at the University of New South Wales (UNSW) School of Civil and Environmental Engineering , with concurrent roles as co-founder of the startup Faramoon . His career spans roles at the University of Melbourne's Department of Infrastructure Engineering and Victorian government's land administration initiatives through DELWP. Education : PhD in Geomatics Engineering (2008, University of Melbourne), Master of GIS Engineering (2004), Bachelor of Surveying Engineering (2001) His research bridges geospatial engineering with construction automation , focusing on 3D cadastre , BIM-GIS integration , and smart cities . Recent publications show trends in underground land administration , digital twins , and LADM standard implementations . Scientific Awards : National educational recognition (2019), Victorian educational grants (2018), and prestigious fellowships (2012) As a supervisor , he guides PhD candidates in topics ranging from BIM for waste management to underground cadastral systems . His industry engagement includes partnerships with the United Nations , Open Geospatial Consortium , and Singapore Land Authority .
Jennifer C. McIntosh is a Professor and University Distinguished Scholar in the Department of Hydrology and Atmospheric Sciences at the University of Arizona (UA), with a joint appointment in the Department of Geosciences. She is also an Adjunct Professor at the University of Saskatchewan. Her academic roles include teaching advanced hydrogeology courses and advising graduate students. She holds a BA in Geology-Chemistry from Whitman College, MS and PhD in Geology from the University of Michigan, and completed a postdoctoral fellowship at Johns Hopkins University. Her research focuses on the interplay between hydrology, geochemistry, and microbiology in the Earth’s crust, spanning micro-to-macro scales. Key projects include studying subsurface fluid dynamics, microbial methane production, and the impacts of climate and human activities on groundwater systems. She has applied these methods to critical zones, sedimentary basins, and oil/gas reservoirs, with fieldwork in the Colorado Plateau, Paradox Basin, and Western Canada. McIntosh has received numerous awards for research and teaching, including the Blitzer Award for Excellence in Teaching Physics-Related Sciences (2019) and the UA Distinguished Scholar Award (2017). She actively serves on national committees for the US EPA, National Academies, and Nuclear Waste Technical Review Board. Education: BA (Whitman College), MS/PhD (University of Michigan), Postdoc (Johns Hopkins University) Notable Awards: GSA Fellow (2019), CIFAR Earth 4D Program (2019), Morton K. Blaustein Fellowship (2004) Labs/Teams: Jemez River Basin Critical Zone Observatory, Siljan Impact Structure Research Group Future Works: Investigating anthropogenic impacts on deep groundwater systems and astrobiology of subsurface microbes
Prof. Dr. Stefan Luther is a Max Planck Research Group leader (W2, tenured since 2013) at the Max Planck Institute for Dynamics and Self-Organization, Göttingen, and an Honorarprofessor at the Faculty of Physics, University of Göttingen. He holds adjunct roles as Adjunct Associate Professor at Cornell University (2009–2012) and Northeastern University (2016–2018), and serves as DZHK-Professor at the Institute of Pharmacology and Toxicology, University Medical Center Göttingen. His research focuses on nonlinear spatiotemporal dynamics in excitable biological media, particularly cardiac arrhythmias. He pioneered 4D imaging of heart function and developed algorithms for optogenetic and electrical control of arrhythmias. Translational efforts span basic research to preclinical and clinical studies. Education includes a Diplom in Physics (1997) and PhD (2000) from Georg-August-University, Göttingen. Postdoctoral training followed at the University of Twente (2001–2004) and Cornell University’s LASSP (2004–2006). His lab, the Biomedical Physics group, explores electromechanical coupling in cardiac systems and develops novel therapeutic approaches. Collaborations include work on computational modeling, uncertainty quantification in dynamical systems, and fluid dynamics of multiphase flows.
Suzanne M. Carbotte is a Bruce Heezen Lamont Research Professor at the Lamont-Doherty Earth Observatory (LDEO) and affiliated with the Columbia Climate School . She holds a PhD in Marine Geophysics from the University of California, Santa Barbara (1992), an M.Sc. in Geophysics from Queen's University (1986), and an H.B.Sc. in Geology and Physics from the University of Toronto (1982). Marine and Polar Geophysics Department, LDEO Key focus areas: Mid-ocean ridges, subduction zones, seismic imaging, geoinformatics Research Interests : Carbotte specializes in marine geophysical studies of oceanic crust formation, subduction zone dynamics, and cyberinfrastructure development for geoscience data. Her work integrates seismic reflection, sonar mapping, and geoinformatics tools to analyze crustal evolution, magma chamber processes, and sediment dynamics in regions like the East Pacific Rise, Juan de Fuca Ridge, and Hudson Estuary. Scientific Trends : Recent publications highlight 3D/4D seismic imaging of magmatic-hydrothermal systems, sediment consolidation in subduction zones, and data infrastructure innovations. Key subfields include melt sill dynamics, fault distribution in oceanic plates, and mantle temperature gradients. Scientific Awards 2015 AGU Fellow 2010 Ridge2000 Distinguished Lecturer 2008 Birch Lectureship & UCSB Distinguished Alumni Award 2007 Bruce C. Heezen Research Chair 1982 Governor General's Silver Medal Grants & Projects : Her career spans funding for initiatives like the Rolling Deck to Repository (R2R) program, EarthCube Building Blocks , and Integrated Earth Data Applications (IEDA) . She has led 16 marine expeditions, emphasizing seismic studies from ridge to trench.
Changjian Li is an Assistant Professor in the School of Informatics at the University of Edinburgh. He leads the GraphViX Group (Graphics, Vision and X) and is a member of the Institute of Perception, Action and Behaviour (IPAB). His research spans computer graphics, computer vision, and human-computer interaction with a focus on 3D generation and analysis. Education: Bachelor's Degree from Shandong University (2014) Ph.D. from the University of Hong Kong (2019) under Prof. Wenping Wang Postdoc at University College London (UCL) with Prof. Niloy Mitra Starting Researcher position at Inria with Dr. Adrien Bousseau Research Interests: Changjian's research focuses on sketch-based 3D modeling, CAD modeling, point cloud processing, and medical imaging applications. He develops systems that bridge intuitive sketching with precise CAD workflows, enhances 3D animation pipelines, and applies neural methods to sparse medical data reconstruction. Scientific Recognition: Best Paper Honorable Mention Award (MICCAI 2021) CADTalk selected as Highlight (CVPR 2024 top 10%) ACM SIGGRAPH Asia 2018 cover image selection ACM SIGGRAPH Asia 2015 technical paper highlight CVPR 2019 poster highlighted in 'Computer Vision News' Advising & Collaborations: He mentors postdocs and PhD students including Duolikun Danier, Haocheng Yuan, Ankan Bhunia, and Lei Zhong. Former advisees include Salvatore Esposito (now at Edinburgh), Guangshun Wei (Shandong University), and Mingjun Yang (University of Melbourne). Collaborates with Oisin Mac Aodha, Hakan Bilen, and Niloy Mitra. Professional Service: Currently serves as Associate Editor for IEEE TVCG and participates in program committees for SIGGRAPH Asia, SIGGRAPH, EuroGraphics, and Geometry Design and Computing (GDC) conferences.
Francesco Maisano, MD , is Full Professor of Cardiac Surgery at Vita-Salute San Raffaele University (Milan) since 2021, where he also serves as Director of the Cardiac Surgery Clinic and of the Valve Center at IRCCS San Raffaele Hospital. From 2014 to 2020 he held the Chair of Cardiac Surgery and directed the Department at University Hospital Zurich. Education & Training 1990 – MD, Catholic University of Rome 1994 – Clinical Fellowship, University of Alabama at Birmingham 1995 – Specialization in Cardiac Surgery, La Sapienza University of Rome Research Interests Professor Maisano’s work centres on innovative therapies for heart-valve disease, spanning surgical reconstruction, catheter-based interventions (TAVI, MitraClip, transcatheter tricuspid devices), and hybrid approaches. He leads translational programmes in biomedical engineering, multimodality cardiac imaging, and artificial-intelligence-guided interventions, with emphasis on the multidisciplinary “Heart Team” model for complex cardiovascular disease. His recent publications (2024-2025) demonstrate intense activity in transcatheter mitral and tricuspid repair, long-term durability of surgical mitral repair, AI-driven procedural guidance, and renal protection strategies during mechanical circulatory support. A dominant theme is translating imaging innovations and device concepts into first-in-human studies and large-scale registries. Scientific Awards & Recognitions European Society of Cardiology Silver Medal (2018) ICI Lifetime Achievement in Research & Teaching (2018) ICI Best Technology Parade Presentation (2010) C. Walton Lillehei Young Investigator Award (1999) Leadership & Grants He directs multiple postgraduate programmes, including Certificate of Advanced Studies (CAS) tracks at the University of Zurich in multimodality imaging, aortic valve, and mitral–tricuspid interventions. He is principal investigator on investigator-initiated grants, coordinates industry-partnered device trials, and mentors numerous doctoral and post-doctoral researchers. His team has filed >24 patents and spun off several cardiovascular start-ups. Labs & Teams At IRCCS San Raffaele he leads the Valve Science Center , a multidisciplinary hub integrating cardiac surgeons, interventional cardiologists, imaging specialists, biomedical engineers, and data scientists focused on next-generation valve repair/replacement technologies and personalised cardiovascular medicine.
University of California , Santa Barbara (UCSB)United States
Dr. Andrea S. Carlini is an Assistant Professor of Materials in the Department of Chemistry & Biochemistry at the University of California, Santa Barbara (UCSB). Her research focuses on structurally dynamic biomaterials and devices, aiming to bridge biochemical signals with soft materials for smart biomedical applications. She holds a PhD from UC San Diego and completed a postdoc at Northwestern University’s Querrey Simpson Institute for Bioelectronics. B.S. in Chemistry & Biological Sciences (Virginia Tech, 2012) M.S. in Chemistry & Biochemistry (UC San Diego, 2014) Ph.D. in Chemistry & Biochemistry (UC San Diego, 2018) Her research is organized into three core areas: (1) stimuli-responsive materials for disease monitoring, (2) 4D shape-changing peptides/polymers, and (3) soft wearable devices for quantitative health feedback. Recent work includes thermal sensors for vascular access and enzyme-responsive biomaterials for tissue engineering. Published articles span bio-electrochemical systems, wearable sensors, and smart hydrogels. Her NSF GRFP Fellowship supported early work on myocardial tissue engineering. The Carlini Group collaborates broadly across UCSB’s interdisciplinary environment. Labs/Teams: Carlini Group (UCSB) Focus: Bioelectronics, biomedical devices, and dynamic materials
Stefan Vandewalle is a full professor at the Department of Computer Science, Faculty of Engineering Sciences, KU Leuven. His research focuses on numerical analysis, applied mathematics, and computational methods for stochastic differential equations, wind energy modeling, and uncertainty quantification. Department Chair, KU Leuven Member, Subdivision Numerical Analysis and Applied Mathematics Member, iSi Health Institute Observer, Faculty Council of Sciences Chair, Department Council for Computer Science His recent work explores multiscale modeling, Monte Carlo methods, and data assimilation techniques. Projects include micro-macro Parareal algorithms, wind turbine aeroelasticity, and turbulent flow reconstruction for wind farms. He supervises PhD candidates and collaborates on interdisciplinary studies involving structural mechanics and renewable energy systems. Publications highlight advancements in parallel-in-time methods, stochastic optimization for tokamak reactors, and DNS-based control of turbulent flows. Key keywords: Multiscale numerical methods Uncertainty quantification Wind energy simulation Monte Carlo algorithms PDE-constrained optimization Stochastic differential equations He contributes to academic governance as a member of extended faculty boards and evaluation committees.
Dimitris N. Metaxas is a Professor in the Department of Computer Science within the School of Arts and Sciences at Rutgers University. His research spans computer vision, medical image analysis, and artificial intelligence, with a particular focus on medical applications including cardiac MRI analysis and foundation models for healthcare. Dr. Metaxas's research interests encompass medical image analysis, computer vision, deep learning, and artificial intelligence. His work demonstrates a strong emphasis on applying advanced machine learning techniques to medical imaging problems, particularly in cardiac analysis. He has made significant contributions to diffusion models, multimodal learning, and efficient AI techniques for medical applications. His research bridges the gap between theoretical computer vision and practical healthcare solutions, with numerous publications in top-tier conferences and journals. His recent publications show a clear trend toward foundation models for medical image analysis, with significant contributions to cardiac MRI segmentation, diffusion models, and multimodal learning. The research spans both theoretical advancements in AI techniques and practical applications in healthcare, particularly focused on improving medical diagnostics through computer vision. His work demonstrates expertise in adapting cutting-edge AI techniques like diffusion models and large language models for specialized medical applications. Dr. Metaxas has mentored numerous students and researchers, as evidenced by his extensive publication record with multiple co-authors across various institutions. His work has received significant attention in the research community, with numerous publications in top venues including CVPR, ICCV, MICCAI, and Medical Image Analysis. His research group focuses on medical image computing, computer vision, and machine learning applications in healthcare. The team works extensively with cardiac MRI data, developing advanced techniques for segmentation, reconstruction, and analysis of 4D cardiac imaging. They are particularly known for their contributions to foundation models in medical imaging and efficient adaptation techniques for specialized medical tasks.
