Maciej Zięba is an academic researcher affiliated with the Faculty of Information and Communication Technology at Wrocław University of Science and Technology, specifically within the Department of Artificial Intelligence . His work spans machine learning, deep learning, and computer vision, with a focus on hyperspectral imaging, autonomous systems, and 3D modeling. Recent research includes uncertainty-aware sensor deployment for autonomous vehicles, low-light image enhancement algorithms, and probabilistic regression frameworks for tabular data. He has co-authored publications on flow-based models, hypernetworks, and neural radiance fields (NeRF) applied to 3D face rendering. Contact: maciej.zieba@pwr.edu.pl
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.
James S. Duncan is the Ebenezer K. Hunt Professor of Biomedical Engineering at Yale University, with additional appointments in Electrical & Computer Engineering and Radiology & Biomedical Imaging. His research focuses on biomedical image processing, quantitative image analysis using geometrical models, and applications in cardiac function and neuro-structure analysis. He has pioneered image-guided interventions and developed computational frameworks for medical imaging challenges. He holds a Ph.D. from the University of Southern California. His work integrates AI, deep learning, and statistical decision-making to advance medical imaging technologies. Notable contributions include advancements in 3D image segmentation, deformable models, and MRI-based tumor response assessment. Dr. Duncan has received prestigious awards, including IEEE Fellow (2001) and induction into the American Institute for Medical and Biological Engineering (2000). His recent research spans AI-driven hemodynamics modeling, trustworthy healthcare AI guidelines, and molecular MRI innovations in immunotherapy monitoring. He collaborates across disciplines to address challenges in cardiovascular, neuroimaging, and oncological applications.
Houman BOROUCHAKI is a Professor at the University of Technology of Troyes (UTT), France, with over 20 years of academic leadership. He has served as Head of the Automatic Mesh Generation and Advanced Methods (GAMMA3) project team since 2008 and previously led the Laboratory of Mechanical Systems and Concurrent Engineering (LASMIS) (2005-2007). His work bridges academic research and industrial applications through collaborations with INRIA , French Petroleum Institute (IFPEN) , Dassault Aviation , and others. Research Interests: A pioneer in adaptive meshing , he focuses on finite element methods , geometric modeling , and numerical simulations . His innovations underpin mesh generation algorithms , 3D triangulation software , and industrial applications in metal forming, composite simulation, and subterranean modeling. Scientific Trends: His recent work emphasizes metric-based meshing , high-order geometric validity , and parallel processing for mesh generation , with applications in petroleum reservoirs, aviation surfaces, and nanomaterials. His Google Scholar profile reflects 25+ years of contributions to meshing and simulation. Teaching: With 22 years of experience, he teaches courses on meshing , numerical analysis , geometric modeling , and computer graphics at UTT, covering undergraduate to PhD levels. Labs & Teams: He leads the interdisciplinary GAMMA3 team and has contributed to LASMIS (mechanical engineering), L2n (CNRS-UMR 7076) (nanomaterials), and LIST3N (computer science).
Tao Hou is an Assistant Professor in the Department of Computer Science at the University of Oregon, where he conducts research at the intersection of computational topology and machine learning. His academic journey includes a Ph.D. in Computer Science from Purdue University, a M.E. in Software Engineering from Tsinghua University, and a B.E. in Software Engineering from Beijing Institute of Technology. His research focuses on improving computational methods for topological data analysis, particularly through efficient algorithms for zigzag persistence and its applications across domains like neuroscience and materials science. Interdisciplinary applications in neuroscience (MICCAI 2024) and computational materials science (Comp. Mat. Sci. 2022) Developed open-source Python software packages for persistent cycle computation Contributed to advancements in zigzag persistence computational complexity Current research explores topological machine learning through projects like FastZigzag and LvlsetPersCyc . He teaches graduate courses on topological data analysis and algorithms theory, and actively seeks PhD students interested in combining mathematics with computer science.
Julian Adamek is a computational cosmologist and lead developer of gevolution , a general-relativistic N-body code for cosmological simulations. His work focuses on modeling relativistic effects in cosmic structure formation to better understand gravity’s role on large scales and dark energy. Research Interests: Computational Cosmology, Theoretical Cosmology, Large-scale structure of the Universe, Relativistic N-body simulations. Technical Leadership: Lead developer of gevolution , a public cosmological simulation code available via GitHub. Recent publications span diverse applications of deep learning in geospatial analytics, environmental monitoring, and computer vision, including phenology modeling, biomass mapping, conflict assessment, and 3D reconstruction from point clouds. Key Trends: Integration of AI/ML for environmental tasks, cross-domain applications (cosmology, ecology, forestry), and satellite data processing. Technical Focus: Transformer networks, diffusion models, super-resolution imaging, and ensemble learning for uncertainty quantification. Julian collaborates with researchers in cosmology and geospatial science, though specific students or awards are not mentioned in the provided texts.
Pierre Alliez is a Senior Researcher and Team Leader at Inria Sophia Antipolis – Méditerranée, leading the TITANE project-team. He holds roles such as President of the Inria Evaluation Commission and Scientific Coordinator of the Inria-DFKI partnership. His research focuses on Geometry Processing, including mesh compression, surface reconstruction, and optimal transportation. Alliez has authored numerous scientific publications and book chapters, receiving accolades like the Eurographics Young Researcher Award (2005) and ERC grants (IRON, TITANIUM). His academic activities include supervising over 50 PhD students and postdoctoral researchers, and leading projects like GRAPES (Learning and Processing Shapes) and BIM2TWIN (digital twin construction). He has served on editorial boards for Computer Graphics Forum and ACM Transactions on Graphics , and organized major conferences like Pacific Graphics and Eurographics. His work bridges computational geometry, computer graphics, and applied mathematics, with practical applications in 3D printing, cultural heritage, and urban modeling. Education: No specific educational details provided, but has authored a textbook on Polygon Mesh Processing (AK Peters, 2010). Research Interests: Geometry Processing, Mesh Generation, Surface Reconstruction, Optimal Transport, and 3D Data Analysis. Grants & Projects: ANR Pisco, ERC IRON, BIM2TWIN, GRAPES, and collaborations with industries like Dassault Systèmes and Dorea Technology. Labs/Teams: Leads the TITANE team at Inria, contributing to software like CGAL and advancing open-source tools for geometric processing.
Soteris Demetriou is a Senior Lecturer of Computer Systems Security at Imperial College London's Department of Computing, within the Faculty of Engineering. He leads the Applications, Platforms, and Systems Security (APSS) Research Lab and directs the Academic Centre of Excellence in Cyber Security Research (ACE-CSR). His research focuses on securing mobile, IoT, and cyber-physical systems through techniques like explainable AI, reverse engineering, and trusted computing. Notable contributions include tools for privacy preservation in machine learning models, detection of LiDAR spoofing attacks, and securing Android's middleware. Education: PhD and MSc in Computer Science (University of Illinois at Urbana-Champaign), Diploma in Electrical and Computer Engineering (University of Patras). Research Interests: Mobile/IoT security, AI security, trusted computing, and vulnerability analysis. Key areas include privacy in generative models, adversarial attacks on autonomous systems, and large-scale distributed systems. Publications: Over 50 peer-reviewed papers in top venues like NDSS, CCS, and SOSP. Recent work addresses privacy in speech generation, LiDAR security for autonomous vehicles, and hyperscale serverless architectures at Meta. Awards: Distinguished Paper Award at NDSS 2018, Best Paper at SafeThings 2024, and multiple travel grants. Served on technical committees for PETS, CCS, and AutoSec. Grants & Collaborations: SPRITE+ grant for Bio-IoT security, collaboration with Meta on distributed systems, and leadership in ACE-CSR. Labs: APSS Lab focuses on systems and AI security, with interdisciplinary projects in healthcare and autonomous systems.
Tamal K. Dey is a Professor of Computer Science at Purdue University, specializing in Computational Geometry and Topology with applications to topological data analysis, geometric modeling, and computer graphics. He holds ACM and IEEE Fellowships and has authored/co-authored over 200 publications, including influential books like Curve and Surface Reconstruction and Computational Topology for Data Analysis . His research group, CGTDA, focuses on theoretical and applied aspects of geometry and topology in data science. Education: B.E. from Jadavpur University (1985), M.E. from Indian Institute of Science (1987), Ph.D. from Purdue University (1991). Postdoctoral work at University of Illinois (1992). Previously led the Jyamiti group at Ohio State University (1999–2020) and served as interim department chair (2019–2020). Major contributions include foundational work on 3D reconstruction, mesh generation, and topological algorithms. His awards include ACM Fellow (2018), IEEE Fellow, and Solid Modeling Association Fellow. Advised numerous PhD students and postdocs, with ongoing projects in persistent homology and TDA applications.
Ismail Ben Ayed is an Associate Professor at École de technologie supérieure (ETS) in Montreal, Canada, holding the ETS Research Chair on Artificial Intelligence in Medical Imaging. His research bridges computer vision, optimization, and medical image analysis to develop advanced algorithms for clinical applications, with particular focus on cardiac and neurological imaging. His research program centers on medical image segmentation using novel optimization techniques, graph-based methods, and deep learning models. He pioneers approaches for handling volumetric bias, shape compactness, and distribution matching in MRI and cardiac imaging, directly addressing clinical challenges in spine labeling, ventricle segmentation, and tumor detection. His work emphasizes mathematical rigor combined with practical medical relevance. Analysis of his 15 most recent publications (2014-2017) reveals dominant themes in medical image segmentation (80% of works), particularly for cardiac MRI (35%) and neurological applications (25%). Key methodological contributions include distributed optimization frameworks (20%), advanced graph cut techniques (30%), and deep learning architectures (25%), published consistently in top-tier venues including CVPR, MICCAI, and TPAMI. His scientific recognition includes: MICCAI travel award (2017) Outstanding Reviewer Award at CVPR (2015) GE innovation award (2010) He actively mentors researchers as evidenced by his recruitment of PhD students and postdocs, with research supported by the ETS Research Chair and multiple patents. His service includes chairing MICCAI 2017/2015 and IPTA 2017, plus continuous program committee roles at CVPR, ICCV, and MICCAI since 2011. Leading the ETS Research Chair on AI in Medical Imaging, he directs a collaborative team working on clinical translation of computer vision techniques. Current projects focus on cardiac motion analysis, brain tumor segmentation, and spine labeling systems with direct applications in radiology workflows.
Christopher Rycroft is a Professor and Associate Chair in the Department of Mathematics at the University of Wisconsin–Madison. He leads the Rycroft Group, which focuses on mathematical modeling and scientific computation for interdisciplinary applications in science and engineering. Prior to joining UW-Madison in summer 2022, he was a professor at Harvard University's School of Engineering and Applied Sciences from 2014-2022, and before that a Morrey Assistant Professor at UC Berkeley from 2010-2013. Professor Rycroft's research spans three main areas: numerical methods for material mechanics, data-driven discovery, and computational geometry. His group develops new computational methods while working directly with domain scientists. Key achievements include the development of the reference map technique for fluid-structure interaction, Voro++ software library for Voronoi tessellation, and novel approaches to understanding crumpling physics. His work combines traditional analysis and modeling with machine learning methods to extract scientific insights from complex data. The Rycroft Group's publication record demonstrates a strong trajectory of interdisciplinary research bridging mathematics, physics, materials science, and biology. Recent work has focused on fluid-structure interaction, computational geometry applications, mechanical metamaterials, and biological fluid dynamics. The group develops both theoretical frameworks and practical software tools that have found applications across diverse scientific domains from materials science to virology. Everett Mendelsohn Award for Excellence in Mentorship (2021) Professor Rycroft has advised numerous PhD and master's students who have gone on to postdoctoral positions at institutions including MIT, EPFL, and Cornell. His teaching includes advanced scientific computing courses that have quadrupled in enrollment during his tenure. He has secured research funding supporting his group's work on computational methods and interdisciplinary applications. The Rycroft Group consists of graduate students, postdocs, and collaborators with diverse backgrounds in applied mathematics, physics, engineering, and computer science. The group maintains active collaborations with researchers across multiple institutions and participates in centers such as the Harvard Quantitative Biology Initiative.
Professor Hossein Rahmani serves at the School of Computing and Communications , Lancaster University , with a focus on Computer Vision and Machine Learning . His career spans institutions like the University of Western Australia (PhD), Shahid Beheshti University (MSc), and Isfahan University of Technology (BSc). Research Interests : Computer Vision, Machine Learning, Video Analysis, Action Recognition/Detection, Object/Human Pose Estimation, 3D Reconstruction, Diffusion Models, Human-Object Interaction Editorial Roles : Associate Editor for IEEE Transactions on Neural Networks and Learning Systems , Pattern Recognition , ACM Computing Surveys ; Area Chair for CVPR 2025, ICLR 2025, ECCV 2024, IJCAI 2024 His recent work leverages diffusion models for domain-generalized object pose estimation, 3D scene editing, and human mesh recovery, published in top venues like TPAMI , CVPR , ICCV , and ECCV . He received the Best Scientific Paper Award from the International Conference on Pattern Recognition and actively supervises 5 PhD students with interdisciplinary projects in digital health and data science.
Dr. Yongjie Jessica Zhang is a Professor at Carnegie Mellon University, holding appointments in both the Department of Mechanical Engineering and the Department of Biomedical Engineering . She received her B.S. and M.S. in Engineering Mechanics from Tsinghua University, followed by an M.S. in Aerospace Engineering and a Ph.D. in Computational Engineering and Sciences from the University of Texas at Austin. After a postdoctoral fellowship at ICES, she joined CMU in 2007, advancing from assistant to full professor by 2016. Research Interests : Image-based geometric modeling, mesh generation, finite element analysis (FEA), isogeometric analysis, and applications in computational biomedicine, materials science, and computer-assisted surgery. Leadership Roles : Chair of Solid Modeling Association (2019-2020), USACM Executive Committee Member-at-Large (2017-2021), and ELATE Fellow (2017-2018). Her work addresses the critical challenge of automating high-fidelity geometric modeling and mesh generation for complex domains (e.g., human anatomy), which traditionally consumes ~80% of FEA time. Her group develops AI-driven methods for multiscale modeling (molecular to organ), with applications in neuroscience , biomechanics , and 4D printing . Notable awards include the Presidential Early Career Award (PECASE) , NSF CAREER Award , and ASME Van C. Mow Medal (2025) . Dr. Zhang’s publications span over 170 peer-reviewed articles, focusing on truncated hierarchical B-splines , polycube meshing , and neurite transport modeling . She has advised more than 40 students, including PhD candidates and postdoctoral fellows. Her editorial roles include Associate Editor of Computer Aided Geometric Design and editorial board memberships in Computer-Aided Design and Engineering with Computers .
Johannes Brandstetter is an Associate Professor at the Institute for Machine Learning at Johannes Kepler University Linz (JKU) where he leads the "AI for data-driven simulations" research group. He is also Co-founder and Chief Scientist at Emmi AI, bridging academic research with industrial applications in AI-driven physics simulation. Brandstetter earned his PhD after working at CERN's CMS experiment on Higgs boson physics. In 2018, he transitioned to machine learning, joining Sepp Hochreiter's research group in Linz. From 2021-2023, he worked at the Amsterdam Machine Learning Lab under Max Welling and Microsoft Research, developing expertise in Geometric Deep Learning and neural surrogates for partial differential equations. He returned to JKU in October 2023 to establish his own research group. His research spans Machine Learning, Deep Learning, and Physics-Informed Machine Learning with focus areas including Neural PDE solvers, Computational Fluid Dynamics, and Climate Modeling. Brandstetter believes AI is poised to revolutionize industrial-scale simulations, potentially saving thousands of compute hours across engineering domains. His work integrates computer vision, numerical simulation, and engineering components to advance data-driven approaches. Recent publications reveal a strong trend toward foundation models for scientific applications, particularly in atmospheric modeling (Aurora), geometric deep learning, and neural surrogates for complex physical systems. His interdisciplinary work spans computer vision, climate science, computational physics, and engineering, demonstrating the versatility of his research approach. Principal Investigator for "AlKa-DL: Alpine karst spring discharge prediction" (FWF-funded, 2024-2027) Principal Investigator for Cluster of Excellence "Bilateral Artificial Intelligence" (FWF-funded, 2024-2029) Co-PI for "Fast, efficient and flexible CFD simulation through generative AI" (FFG-funded, 2025-2026) As an educator and researcher, Brandstetter actively engages with the scientific community through invited talks at major conferences including presentations on "Closing the Gap Between Scientific Foundation Models and Real-World Applications" (March 2025) and "Scientific Machine Learning for Science and Engineering" (February 2025).
Dr. Frederic Bosche is a Reader in Construction Informatics at the University of Edinburgh's School of Engineering, leading the CyberBuild Lab. His research focuses on advancing digital construction technologies, including BIM, sensing systems, and digital twinning to enhance infrastructure management and workforce safety. Education: PhD in Civil Engineering (University of Waterloo), M.Sc. from University of Texas at Austin, and M.Eng. from Ecole Centrale de Lille. Research interests include automated construction processes, data-driven infrastructure lifecycle management, and integrating emerging technologies like AI and IoT into construction workflows. His CyberBuild Lab has pioneered projects in defect detection, roof monitoring, and smart construction inspection. Notable contributions include over 100 publications, 12 research projects (e.g., 'Digital Facility' and 'Monitoring Roofs of Traditional Buildings'), and awards such as the Charles M. Eastman Top PhD Paper Award. He actively engages in public outreach through science festivals and collaborates internationally with institutions like ETH Zurich and Heriot-Watt University.
