Dr. Iro Armeni is Assistant Professor of Civil and Environmental Engineering at Stanford University, leading the Gradient Spaces research group. Her interdisciplinary research bridges architecture, civil engineering, and computer vision to develop data-driven methods for sustainable and adaptive built environments. Professor Armeni's work focuses on creating gradient environments that blend physical and digital realities through mixed reality technologies. She develops computational methods for 3D scene understanding, generative design, and adaptive spaces that respond to human needs. Her research integrates AI with architectural design to improve sustainability, inclusivity, and reusability of built spaces. Current projects include 3D scene graph representations, automated BIM modeling from visual data, and neuro-symbolic approaches for design optimization. She has developed tools like HoloLabel (AR semantic labeling) and SemSpray (VR annotation) for construction information management. Professor Armeni holds a PhD from Stanford University, supported by a Google PhD Fellowship, and completed postdoctoral research at ETH Zurich with an ETH Fellowship. She teaches courses on Computer Vision for the Built Environment and Mixed Reality applications.
Georg Martius is a Full Professor in the Department of Computer Science at the University of Tübingen's Faculty of Science and a Max Planck Research Group Leader at the MPI for Intelligent Systems. Since April 2023, he has been a core member of the DFG-funded Cluster of Excellence 'Machine Learning: New Perspectives for Science,' which received extended funding through 2032 for its mission to integrate machine learning into fundamental scientific discovery processes. His academic foundation includes a PhD from the University of Göttingen and Bernstein Center for Computational Neuroscience (2005), a Diploma in Computer Science from the University of Leipzig (2003), and a visiting research period at the University of Edinburgh's Division of Informatics. Postdoctoral positions followed at the Max Planck Institutes for Dynamics and Self-Organization (Göttingen, 2009), Mathematics in the Sciences (Leipzig, 2010), and IST Austria (2015). Professor Martius's research pioneers the intersection of reinforcement learning, robotics, and tactile sensing, with emphasis on developing autonomous systems capable of natural locomotion, dexterous manipulation, and physical-world understanding. His work bridges theoretical machine learning with practical hardware applications, particularly in creating differentiable simulators, superresolution tactile sensors, and biologically plausible learning frameworks for robotic control. Analysis of his 2024-2025 publications reveals dominant trends in offline reinforcement learning (especially goal-conditioned and diversity-maximization techniques), object-centric representation learning for video understanding, and tactile sensing innovations. A strong thread connects foundation models to world model construction, while his work on differentiable physics engines enables precise collision handling and contact dynamics for real-world robotic control. His leadership roles include directing the Distributed Intelligence research team at Tübingen and contributing to major collaborative initiatives like the Real Robot Challenge and Myochallenge 2022. The Cluster of Excellence appointment represents recognition of his contributions to transforming scientific methodology through machine learning, particularly in automating hypothesis generation and experimental design. Current projects focus on integrating large-scale machine learning with embodied intelligence, advancing tactile perception systems like the Minsight vision-based sensor, and developing neuroplasticity-inspired approaches for robust out-of-distribution detection. His work directly impacts fields requiring physical interaction intelligence, from autonomous navigation to medical robotics, with emphasis on sample-efficient learning from limited real-world data.
Chuang Gan is an Assistant Professor at the University of Massachusetts Amherst, affiliated with the College of Information and Computer Sciences and the Department of Computer Science. His work focuses on advancing artificial intelligence, robotics, computer vision, and embodied agents through interdisciplinary research combining neural networks, physical simulations, and multimodal learning. Research interests include generative models, reinforcement learning, vision-language integration, and scalable autonomous systems. He explores topics like world modeling for robots, adaptive policy learning, and physics-driven AI. His projects often involve creating systems that learn from visual, auditory, and tactile inputs to perform complex tasks such as object manipulation, navigation, and decision-making in dynamic environments. Recent research trends emphasize embodied AI systems capable of long-horizon planning, compositional reasoning, and efficient learning from limited data. His work bridges theory and practice, with applications in robotics, simulation platforms, and multimodal generation. Key contributions include frameworks for 3D scene understanding, adaptive world models, and novel training paradigms for large language models. His research has been applied to robotics platforms like RoboDreamer and UBSoft, focusing on unbounded soft environments. Collaborations involve designing benchmarks for physical scene understanding (e.g., Physion++), and creating tools like DiffTactile for tactile simulation. His work often integrates principles from differential geometry, PDE dynamics, and game theory. Chuang Gan’s research group develops open-source tools and benchmarks, such as the SoftZoo robot co-design platform and the SOK-Bench situated reasoning benchmark. His team emphasizes scalable alignment methods beyond human supervision and explores ethical AI through principles like symmetry-enhanced training.
Alexei A. Efros is the Howard Friesen Professor in the EECS Department at UC Berkeley, affiliated with the Berkeley Artificial Intelligence Research (BAIR) Lab. Previously, he spent a decade at CMU's Robotics Institute and held a postdoc at the University of Oxford under Andrew Zisserman. He collaborates with INRIA/École Normale Supérieure in Paris. His research focuses on self-supervised learning, generative models, and visual data mining, with applications to robotics, computational photography, and art. Education & Academic Roles: Postdoc at Oxford (with Andrew Zisserman), faculty at CMU (2005–2015), currently at UC Berkeley. Teaches courses like CS 180/280A (Computer Vision) and CS 280 (Graduate Computer Vision). Research Interests: Self-supervised learning, generative models (e.g., diffusion models, inpainting), visual commonsense, and cross-modal reasoning. His work bridges computer vision and graphics, emphasizing data-driven approaches. Recent projects include Visual Jenga, Diffusion Models as Data Mining Tools, and Prioritized Generative Replay. Grants & Labs: Leads the Efros Research Group, advised over 40 PhD students (e.g., Jun-Yan Zhu, Tinghui Zhou). Collaborates with institutions like INRIA and NVIDIA. Active in grants related to AI, vision, and robotics. Labs/Teams: BAIR Lab (UC Berkeley), former affiliations with CMU Robotics Institute and Willow Team (INRIA/ENS Paris). Current lab focuses on generative AI, 3D perception, and visual reasoning.
N. Rich Nguyen is an Assistant Professor in the Department of Computer Science at the University of Virginia (UVA), where he joined in August 2018. He's part of the School of Engineering and Applied Science and is on a teaching track , focusing on making machine learning accessible and engaging for all students. Research and Innovation: Rich Nguyen's research interests include biomedical image analysis , machine learning , and computer science education . He aims to reinvent instructional activities to make them adaptive and engaging by incorporating art and music elements to help everyone learn coding. Notable research contributions include: Floodwatch : A system for flood monitoring using crowdsourced images TuneScope : A digital music creation tool combining SoundScope and Snaps! technology CAD Library : Open-source design tools for educators AI for early sepsis detection : Highlighted in UVA Today Teaching Accomplishments: Before UVA, Rich taught computer science courses at UNC Charlotte for four years to a total of 1,458 students. At UVA, he teaches several courses including: CS 4774: Machine Learning (multiple semesters) CS 2501: Machine Learning for All (launched in Fall 2021) SYS 6016 / SDS 6050: Deep Learning CS 2150: Data and Program Representation (multiple semesters) CS 6316: Machine Learning (Graduate Level) CS 2910: CS Education Practicum (for Teaching Assistants) He previously taught at UNC Charlotte: ITCS 1600: Computing Professionals ITCS 2600: Computing Professionals for Transfer Students ITCS 4156: Introduction to Machine Learning ITCS 2215: Design and Analysis of Algorithms Academic Achievements: Rich Nguyen has received several notable awards and grants: Google Faculty Award for Machine Learning Education with TensorFlow (2019) Best Paper Award at IEEE BigDataSE (2022) Best Poster Award at SITE Conference (2022) CCI Faculty Innovation Award (2018) NSF grants for Smart and Connected Communities (2022) and Computational Thinking (2021) 3 Cavaliers Grant on Coding and Music (2021) Student Mentorship: Rich has mentored numerous students and teaching assistants who have achieved recognition. Notable students include: Joy Qiu - Published in Clinical Infectious Diseases Louisa Edwards and Zach Boner - Invited to Ken Ono Podcast Mike Ferguson - Winner of CS Louis T. Rader Undergraduate Teaching Award He has also served as faculty advisor for HooHacks (UVA's hackathon) and co-founded CharlotteHack at UNC Charlotte. Labs and Collaborations: Rich Nguyen leads the ML4VA (Machine Learning for Virginia) initiative, engaging students in project-based learning to apply machine learning to real-world problems affecting Virginia communities. He collaborates with institutions for symposiums on smart cities, particularly with ASEAN universities, and has partnered with Premier Healthcare for hackathons and with Glen Bull on educational technology projects.
Rozenn Dahyot is a Professor of Computer Science at Maynooth University within the Faculty of Science & Engineering. She previously held roles as Assistant and Associate Professor in Statistics at Trinity College Dublin (2008-2021) and Lecturer in Computer Science (2005-2008). Her research interests bridge Digital Signal Processing, Computer Vision, Machine Learning, and Statistical Analysis. She organized the European Signal Processing Conference (EUSIPCO2021) in Dublin and served as President of the Irish Pattern Recognition and Classification Society (IPRCS) from 2014-2020. Her work spans topics like semantic scene understanding, CNN compression, and medical image segmentation. Key contributions include advancements in graph-based image analysis, reinforcement learning optimization, and AI-driven systems for disaster management. Dahyot is a member of IEEE, ACM, and EURASIP, contributing to both academic and industrial collaborations.
Hanbyul Joo is an Assistant Professor in the Department of Computer Science and Engineering at Seoul National University (SNU). Prior to joining SNU, he was a Research Scientist at Facebook AI Research (FAIR) in Menlo Park. He completed his Ph.D. in the Robotics Institute at Carnegie Mellon University, working with Yaser Sheikh, and received his M.S. in Electrical Engineering and B.S. in Computer Science from KAIST, Korea. Dr. Joo's educational journey began at KAIST, where he earned both his Bachelor's and Master's degrees. He then pursued his Ph.D. at Carnegie Mellon University's Robotics Institute, completing his dissertation titled "Sensing, Measuring, and Modeling Social Signals in Nonverbal Communication." His doctoral work focused on developing the Panoptic Studio, a unique sensing system with over 500 synchronized cameras for capturing social interactions. Dr. Joo's research primarily focuses on endowing machines and robots with the ability to perceive and understand human behaviors in 3D . His goal is to build "social Artificial Intelligence" that can interact with humans using social signals (body languages). He pursues this direction using data-driven methods where data is collected by measuring the wide spectrum of social signals transmitted during interpersonal social interaction. His research spans computer vision, machine learning, computer graphics, and robotics , with particular emphasis on 3D human pose estimation, human-object interaction, and social signal processing. His recent publications demonstrate a clear trend toward leveraging diffusion models for 3D reconstruction and generation tasks, with a focus on human-centric applications. His work bridges the gap between 2D image understanding and 3D scene reconstruction, often utilizing pre-trained models to overcome data limitations. The research consistently addresses fundamental challenges in understanding human behavior, interaction with objects, and social dynamics in 3D space. Dr. Joo is a recipient of several prestigious awards including the Samsung Scholarship and the CVPR Best Student Paper Award in 2018 . His paper "Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies" received this honor at CVPR 2018. His research has been widely recognized in top computer vision and AI conferences, with multiple oral presentations at venues like CVPR, ICCV, and ECCV. Dr. Joo actively mentors a large group of students, with approximately 15 current students working toward MS/PhD degrees under his supervision. His lab, the SNU VCLab, focuses on cutting-edge research in computer vision and graphics. He has secured significant research funding through his work, though specific grant details aren't provided on his website. Dr. Joo frequently serves as an area chair for major conferences including CVPR, ICCV, and NeurIPS, demonstrating his standing in the academic community. Dr. Joo leads the SNU VCLab, which has developed several notable datasets and tools including SNU ParaHome, FrankMocap, and the CMU Panoptic Studio Dataset. His lab maintains strong industry connections, with students interning at leading companies like Meta. The lab's research focuses on building the infrastructure and algorithms needed for social AI, with an emphasis on practical applications that can be deployed in real-world settings.
Yuguo Chen is a Professor in the Department of Statistics at the University of Illinois at Urbana-Champaign (UIUC), serving as Interim Department Chair and Director of the Illinois Statistics Office. He holds affiliations with the Department of Computer Science, Information Trust Institute, Coordinated Science Lab, and Illinois Informatics Institute. Chen earned his PhD in Statistics from Stanford University (2001) and a B.S. in Mathematics from the University of Science and Technology of China (1997). His research focuses on Monte Carlo methods, network data analysis, state space models, bioinformatics, and Bayesian inference. Key interests include scalable network estimation, community detection, and applications in public health, education, and computational biology. Recent work highlights include advancements in dynamic network modeling, Bayesian latent class models for cognitive diagnosis, and statistical methods for analyzing multi-layer networks. His contributions have been recognized through awards such as the American Statistical Association Fellowship (2018) and the Charles Edison Lectureship (2018). Editorial Roles: Associate Editor of Journal of the American Statistical Association , Journal of Computational and Graphical Statistics , and Journal of Algebraic Statistics . Grants & Consulting: Directs the Illinois Statistics Office, providing interdisciplinary research support. Active in collaborative projects involving healthcare, education, and computational infrastructure. Labs & Teams: Leads initiatives at the Coordinated Science Lab and Information Trust Institute, integrating statistical methods with cybersecurity and data-driven decision-making.
Jo Wood is Professor of Visual Analytics in the Department of Computer Science at City, University of London, where she has been employed since January 14, 2000. Her work bridges computer science, geographic information science, and human-computer interaction, focusing on innovative methods for visualizing complex spatial and behavioral data. Her research interests center on visual analytics , information visualization , and geovisualization , with applications in transportation, public health, crisis response, and citizen science. She investigates how interactive visual interfaces can support exploratory data analysis, decision-making, and storytelling, particularly through small multiples, faceted views, and sketch-based rendering techniques. The trends in her recent publications reflect a consistent focus on user-centered design , spatial data abstraction , and interactive exploration of multivariate datasets. Her work often integrates real-world behavioral data such as GPS tracks, cycling patterns, and crowd-sourced information to build meaningful visual narratives and support analytical reasoning. Throughout her career, Jo Wood has contributed significantly to the advancement of visual analytics through high-impact publications in top-tier venues such as IEEE Transactions on Visualization and Computer Graphics and Computer Graphics Forum. Her collaborations with researchers like Jason Dykes and Aidan Slingsby highlight her role in a vibrant research community. She has supervised numerous research projects and mentored students in visualization and geospatial analytics, though specific names are not listed in the provided text. Her work has been supported by various research grants, particularly in domains involving urban mobility, energy modeling, and crisis informatics, though grant details are not specified here. Jo Wood has also contributed to the design of visual analytics systems for applications including disease spread modeling, bicycle-hire scheme monitoring, and persuasive technology for health and leisure, demonstrating a strong commitment to impactful, interdisciplinary research.
Hank Childs is a Professor in the School of Computer and Data Sciences at the University of Oregon, specializing in scientific visualization and high-performance computing. He leads the Research Group on Computing and Data Understanding at eXtreme Scale (CDUX) and has held leadership roles including Interim Executive Director of the School of Computer and Data Sciences. His educational background includes a Ph.D. (2006) and B.S. (1999) in Computer Science from the University of California at Davis. Prior to academia, he worked for 14 years at Lawrence Livermore and Lawrence Berkeley National Laboratories, where he served as architect of the VisIt open-source visualization tool. Research interests center on visualizing extreme-scale scientific datasets from supercomputers, with a focus on in situ visualization for cosmology, seismology, and fluid dynamics. He has pioneered projects like VTK-m and Ascent, and his work explores power-performance tradeoffs and data-parallel algorithms for GPUs. Recent publications emphasize scalable visualization techniques for exascale computing, with 15 notable works from 2021-2020 covering particle advection, in situ triggering, and power-aware frameworks. His research has been honored with multiple best paper awards at IEEE LDAV, EGPGV, and SC conferences. DOE Early Career Award (2012) University of Oregon Faculty Excellence Award (2018) 4+ million dollars in research funding since 2013 5 Best Paper awards in 2021 alone As an educator, he received four consecutive CIS Best Teacher Awards (2014-2019). He has served as Associate Editor for IEEE Transactions journals and organized numerous visualization workshops including Dagstuhl seminars and Shonan workshops.
Dr. Jia Zhang is the Inaugural Robert H. Dedman Jr. Endowed Department Chair and Professor of Computer Science at Southern Methodist University (SMU Lyle School of Engineering). She holds the Cruse C. and Marjorie F. Calahan Centennial Chair in Engineering and has a courtesy appointment in the Department of Operations Research and Engineering Management. Her research focuses on applying machine learning, natural language processing, and information retrieval to data science infrastructure, particularly scientific workflows, provenance mining, software discovery, knowledge graphs, cloud computing, immune AI, and applications in earth science and healthcare. Education: Ph.D. in Computer Science, University of Illinois at Chicago M.S. in Computer Science, Nanjing University B.S. in Computer Science, Nanjing University Dr. Zhang's work emphasizes data science infrastructure and machine learning for scientific workflows and knowledge graphs. Her recent publications highlight deep learning , graph neural networks , and optimization algorithms in cloud computing, cybersecurity, and environmental applications. Key trends include spatiotemporal modeling , hybrid neural architectures , and AI-driven service ecosystems . Scientific Awards: Best Paper Awards IEEE SCC (2011, 2017) Best Student Paper Awards IEEE ICWS (2014, 2018), IEEE ICCC (2018) Distinguished Paper Award ICSOC (2023) First Outstanding Service Award IEEE Technical Committee on Services Computing (2016) She has secured over $5 million in federal grants (as PI) and $11 million as PI/Co-PI from NSF, NASA, NIH, UTSW, Ericsson, SAP, and Google. Her lab (Caruth Hall 308) actively recruits research assistants. She previously served as a faculty member at Carnegie Mellon University, Northern Illinois University, and Nanjing University, and worked in industry as a software architect.
Dr. Benjamin Busam is a Senior Research Scientist at the Technical University of Munich , affiliated with the Chair for Computer Science Applications in Medicine under Prof. Nassir Navab. Starting September 2025, he will hold the Professorship for Photogrammetry and Remote Sensing at TUM. His career includes leadership roles at FRAMOS Imaging Systems and Huawei Research in London. Education: Mathematics (TUM), Mathematics and Physics (ParisTech, University of Melbourne), PhD in Mathematics (TUM, 2014) His research focuses on 3D computer vision , multi-modal sensor fusion , and their applications in collaborative robotics and augmented reality . He specializes in projective geometry , 6D pose estimation , and neural radiance fields , with a particular emphasis on photometrically challenging environments. Recent publications highlight advancements in 3D scene understanding , neural rendering , and medical imaging , often leveraging machine learning and vision-language models . His work has been recognized through awards like the EMVA Young Professional Award (2015) and Innovation Pioneer of the Year (2019) , along with multiple Outstanding Reviewer distinctions at leading conferences. Dr. Busam has supervised numerous PhD and MSc students on topics including 6D pose estimation , medical augmented reality , and robotic ultrasound , collaborating with institutions like MIT , École Polytechnique , and University of Padova .
Dr. Karim El-Basyouny is a Killam Laureate Professor and City of Edmonton Urban Traffic Safety Research Chair at the University of Alberta's Faculty of Engineering, where he serves as Associate Dean (Research Infrastructure and Innovation) in the Civil and Environmental Engineering Department. A licensed Professional Engineer in Alberta, he holds advanced degrees in Transportation Engineering from the University of British Columbia and has dedicated his career to advancing road safety through data-driven management frameworks. His academic credentials include: Doctor of Philosophy, Civil Engineering, University of British Columbia, 2011 Engineering Management Sub-specialization, Civil Engineering, University of British Columbia, 2010 Master of Applied Science, Civil Engineering, University of British Columbia, 2006 Bachelor's degree (ABET Equivalent), Civil & Environmental Engineering, United Arab Emirates University, 2003 El-Basyouny's research pioneers the integration of remote sensing, machine learning, and statistical modeling to enhance transportation safety. His work develops automated tools for infrastructure digitization, collision prediction, and speed management, treating safety as a systemic product requiring management frameworks. Key contributions include LiDAR-based road feature extraction, network-level safety evaluations, and frameworks for vision-zero outcomes that address both human-driven and autonomous vehicle contexts. His recent publications demonstrate a cohesive research trajectory centered on leveraging point cloud data and computational intelligence for safety management. Over 15 major publications since 2021 focus on automated infrastructure assessment (light pole detection, clear zone mapping, vertical clearance evaluation), weather-impact modeling, and enforcement resource optimization. This body of work bridges transportation engineering with computer vision and operations research to create scalable safety solutions. His scientific contributions have been recognized with prestigious honors including: 2024 Killam Annual Professorship Award 2024 Road Safety Achievement Award from TAC 2023 Donald Stanley Award for environmental engineering 2022 Faculty of Engineering Graduate Teaching Award 2021 Daniel B. Fambro Student Paper Award As an academic leader, El-Basyouny actively mentors graduate students and secures significant research funding through his endowed chair position. He currently recruits fully-funded PhD and postdoctoral candidates specializing in remote sensing applications, machine learning, and geomatics for road digitization projects. His research group collaborates with national safety committees and municipal agencies to translate findings into policy, while he serves on editorial boards for Transportation Research Record and Analytic Methods in Accident Research. The research group operates at the intersection of transportation engineering and computational science, developing automated frameworks that merge sensor technologies with data processing tools. Current projects focus on semantic segmentation of 3D point clouds, safety implications of infrastructure digitization, and machine learning applications for road feature extraction in both urban and rural environments.
Professor Irem Dikmen is a leading academic in Construction Engineering and Management at the University of Reading, where she serves as School Director of Internationalisation in the Chancellor's Building. Her research integrates engineering, management, and information sciences to advance construction project risk management, particularly focusing on climate resilience, digital technologies, and social value in infrastructure systems. PhD, MSc, and BSc in Civil Engineering from Middle East Technical University Her work leverages systems thinking, artificial intelligence, and digital tools to develop decision-support frameworks for megaprojects and climate adaptation. Recent publications highlight innovations in NLP contract analysis, energy performance ontologies, and risk visualization techniques. She supervises students on topics spanning IoT lifecycle management, ESG risks, and NLP defect detection. Key collaborations include the Climate and Finance Research Cluster and Walker Institute , with contributions to digital construction technologies and sustainability risk assessment. Teaching modules include Construction Risk Management, Economics, and Business Organisation.
Lili Zheng is an Assistant Professor in the Department of Statistics at the University of Illinois. Her research focuses on statistical methodology, machine learning, and high-dimensional data analysis with applications in neuroscience and network science. Key areas of expertise include graphical models, stochastic processes, and algorithmic optimization. She collaborates extensively on projects involving functional connectivity analysis, neuronal data imputation, and interpretable machine learning frameworks. Her work bridges statistical theory and computational practice, addressing challenges in model inference, feature importance assessment, and low-rank tensor completion. Notable contributions include techniques for distribution-free inference, spectral clustering in patchwork learning, and Gaussian process parameter estimation using mini-batch stochastic gradient descent. Dr. Zheng's research emphasizes interdisciplinary applications, particularly in neuroimaging (calcium imaging, functional connectivity) and multi-modal data integration. She actively explores statistical challenges in big data contexts, emphasizing robust methodologies for real-world datasets.
