Haibin Ling is the SUNY Empire Innovation Professor in the Department of Computer Science at Stony Brook University, part of the College of Engineering and Applied Sciences. His research focuses on computer vision, medical image analysis, augmented reality, and AI applications in science. He holds a Ph.D. from the University of Maryland (2006) and prior degrees from Peking University. Previously, he worked at Temple University (2008–2019) and held roles at Siemens Corporate Research, UCLA, and Microsoft Research Asia. Professor Ling's work spans biomedical imaging, AI for science, and human-computer interaction. He leads the CV Lab and collaborates with the AI Institute at Stony Brook. Awards include the NSF CAREER Award (2014), Best Student Paper (ACM UIST 2003), and IEEE Fellow (2020). He serves on editorial boards for IEEE Trans. PAMI, Pattern Recognition, and CVIU, and chairs major conferences like CVPR. His research group includes over 50 students and alumni, with active projects in tracking benchmarks (LaSOT), Leafsnap, and medical imaging tools. Notable publications address OCTA flow estimation, backdoor attacks on vision models, and topology-guided medical learning. Collaborations involve institutions like Temple University and Stony Brook's Department of Applied Mathematics and Statistics.
Nancy M. Wells is a Professor in the Department of Human Centered Design and serves as the Senior Associate Dean for Research and Graduate Education in the College of Human Ecology at Cornell University. Her work bridges environmental psychology and public health, examining how built and natural environments impact human health and behavior across the lifespan. Dr. Wells received a joint PhD in Psychology and Architecture from the University of Michigan and completed National Institute of Mental Health (NIMH) post-doctoral training at the University of California, Irvine. Her educational background uniquely positions her to investigate the intersection of physical environments and human wellbeing through both psychological and architectural lenses. Her research focuses on how environmental contexts—from homes to neighborhoods to schools—affect health outcomes, cognitive functioning, and resilience. A central theme examines nature's role in human resilience, particularly how natural environments buffer against adversity and challenge. She has conducted influential work on school gardens and their effects on children's physical activity, dietary habits, and learning. More recently, she has pioneered research on nature and autism, conducting a randomized controlled trial examining natural environments' effects on autistic youth wellbeing. Analysis of Dr. Wells' recent publications reveals consistent focus on nature's health benefits, with strong emphasis on experimental methodologies including randomized controlled trials and natural experiments. Her work spans environmental psychology, public health, and education, with particular attention to vulnerable populations including low-income communities and children of color. NSF Funding for environmental health research USDA Funding for school garden studies Robert Wood Johnson Foundation Funding MacArthur Foundation Funding Senior Associate Editor of Environment and Behavior (4 years) Research Advisory Board of Children and Nature Network Board of Directors for reDirect Dr. Wells actively mentors undergraduate and graduate students through the Wells Lab, involving them in all research stages from data collection to publication. Her research has been supported by multiple significant grants examining housing quality effects on mental health, neighborhood design impacts on physical activity, and school garden interventions. She leads interdisciplinary research teams that integrate perspectives from environmental psychology, public health, landscape architecture, and education. The Wells Lab operates as an integrated research-teaching-outreach unit that develops practical yet rigorous studies to assess causal relations between environment and health, creates valid measurement tools, and identifies causal pathways. Current projects include examining nature's effects on autistic youth wellbeing in partnership with The Center for Discovery in New York, and investigating multi-sensory design in urban public spaces.
David B. Lindell is an Assistant Professor in the Department of Computer Science at the University of Toronto, with affiliations to the Vector Institute and AXL. He is a founding member of the Toronto Computational Imaging Group. His research focuses on physically based intelligent sensing, integrating physical models, signal processing, and AI to advance sensing systems. Notable projects include imaging around corners, through scattering media, and developing machine learning algorithms for 3D scene reconstruction. Education: Ph.D. in Computational Imaging from Stanford University (advisor: Gordon Wetzstein). Awards include the 2024 Ontario Early Researcher Award and the Best Student Paper at CVPR 2025. His work combines computational imaging with applications in computer graphics and autonomous systems. Research interests span non-line-of-sight imaging, single-photon sensing, and neural representations. Key contributions include the Light-Cone Transform (Nature 2018), confocal diffuse tomography (Nature Communications 2020), and AutoInt (CVPR 2021). His lab develops systems for 3D reconstruction, transient imaging, and photon-efficient sensors. Selected grants and support: NSF CAREER Award, DARPA REVEAL program, and KAUST Visual Computing Center funding. Active collaborations with industry and academic institutions on autonomous driving and medical imaging applications.
Lars Ulander is a Professor at Chalmers University of Technology specializing in radar remote sensing. His research focuses on synthetic aperture radar (SAR) signal processing, particularly for applications in forest biomass mapping and ground imaging using VHF/UHF-band systems. He is a key proposer for ESA's BIOMASS satellite mission (launching 2025) and leads the BorealScat project, utilizing a 50-meter tower-based tomographic radar to study boreal forest dynamics. His work spans radar system development, SAR tomography techniques, and environmental monitoring of forests and sea surface currents. Current research areas include vegetation water content estimation, bistatic radar configurations, and optimization of SAR data processing algorithms for multi-temporal analysis. Recent publications demonstrate expertise in P-band/L-band SAR for biomass retrieval, passive radar systems, and interferometric techniques. His articles investigate radar backscatter sensitivity to forest structure, moisture parameters, and seasonal changes, while contributing to mission design frameworks like SLAINTE and SESAME.
Dr Dominic McCafferty is a Senior Lecturer in Physiology, Ageing & Welfare at the University of Glasgow, based at the Scottish Centre for Ecology and the Natural Environment. He holds academic roles including Editor-in-Chief of the Journal of Thermal Biology and previously Ibis . His research focuses on thermal adaptation, behavioral thermoregulation, and urban wildlife ecology, particularly using thermal imaging techniques. He has a Ph.D. and BSc in Ecological Science from the University of Edinburgh (1989–1993 and 1985–1989). His work spans physiological studies in mammals, birds, and marine species, emphasizing conservation applications. Notable projects include habitat suitability modeling for urban water voles and thermal physiology studies in elephant seals. His educational background includes advanced studies in ecological science, complemented by post-doctoral research at institutions like the British Antarctic Survey (1993–1998). He has contributed to over 99 publications, advancing methodologies in non-invasive wildlife monitoring and ecological field studies. His editorial roles reflect expertise in thermal biology and avian science. McCafferty has led projects on carrion dynamics, stress physiology, and behavioral adaptations in diverse species, with a focus on applying findings to conservation challenges. Labs/Teams: Scottish Centre for Ecology and the Natural Environment (SCENE), part of the broader University of Glasgow's ecological research network. Collaborations include the Muséum National d’Histoire Naturelle in France and the National Trust for Scotland.
Perla Maiolino serves as an Associate Professor in Engineering Science at the University of Oxford and Principal Investigator of the Soft Robotics Lab (SRL) within the Oxford Robotics Institute. Her academic foundation includes BEng, MEng, and PhD degrees in Robotics and Automation from the University of Genoa, where she pioneered CySkin technology for distributed tactile sensing in robots—later exhibited at the Science Museum in London. She expanded her expertise during a 2017-2018 postdoctoral fellowship at Cambridge University's Biologically Inspired Robotics Lab, focusing on soft robotics and tactile perception. Dr. Maiolino's research centers on developing artificial skin systems, soft robotic actuators, and distributed sensing architectures. Her work bridges biological inspiration with engineering innovation to create robots capable of safe human interaction and dexterous manipulation in unstructured environments. Key contributions include compliant beaded-string jamming mechanisms for anthropomorphic fingers, monolithic 3D-printed soft pneumatic arms (JAMMit!), and distributed time-of-flight sensor networks for robotic self-awareness. Recent publications (2024-2025) reveal a strong convergence of tactile sensing with machine learning, featuring optical flow for gesture recognition, diffusion models for artificial skin simulation, and zero-shot sim-to-real transfer techniques. Her team has made significant advances in multi-modal sensing integration, variable stiffness actuation, and scene flow estimation for robots operating in dynamic surroundings. Scientific Awards No specific awards were documented in the provided institutional materials. Advising and Grants While her leadership of the Soft Robotics Lab implies active student supervision and grant management, detailed information about advisees or funded projects was not included in the source documentation. Labs and Teams As Principal Investigator of the Soft Robotics Lab at Oxford Robotics Institute, Dr. Maiolino directs research on tactile perception systems, soft actuation mechanisms, and sensor-integrated robotic structures. The lab's work focuses on applications requiring safe physical interaction, including healthcare robotics and human-robot collaboration scenarios, with emphasis on multi-material 3D printing and embedded sensing technologies.
Jonathan T. Barron is a Researcher at Google DeepMind in San Francisco, specializing in Computer Vision , Neural Rendering , and 3D Scene Reconstruction . He earned his PhD at UC Berkeley under Jitendra Malik and has pioneered advancements in NeRF (Neural Radiance Fields) and diffusion-based 3D generation. Research Interests : Computer Vision, Deep Learning, Generative AI, Image Processing, and 3D Reconstruction via Radiance Fields. His work includes Bolt3D for rapid 3D scene generation, CAT3D/CAT4D for text-to-3D/4D, and Zip-NeRF for anti-aliased radiance fields. He has also developed real-time rendering frameworks like SMERF and NeRF-Casting for reflections. Scientific awards: PAMI Young Researcher Award He has served as Area Chair for CVPR, ICCV, and NeurIPS, and his research is widely adopted in applications like Google's Lens Blur , Portrait Mode , and Jump VR .
Andreas Geiger is a Professor and Head of the Department of Computer Science at the University of Tübingen, Germany. He leads the Autonomous Vision Group (AVG) within CyberValley and is a core faculty member of the Tübingen AI Center. His roles also include PI in the ML in Science Excellence Cluster and the CRC Robust Vision, as well as ELLIS Fellow and coordinator of the ELLIS PhD program. He specializes in machine learning models for computer vision, robotics, and autonomous systems, with applications in self-driving cars, VR/AR, and scientific document analysis. Educational background: While not explicitly detailed, his positions imply a Ph.D. in Computer Science or related field. His work spans interdisciplinary collaborations with institutions like ETH Zürich, Microsoft, and the University of Bonn. Research focuses on 3D scene understanding, Gaussian splatting, generative models, and reliable autonomous systems. Notable contributions include the KITTI dataset and foundational work in neural radiance fields. Awards include the Sage 10-Year Impact Award (2024), ERC Starting Grant (2019), and IEEE PAMI Young Researcher Award (2018). Key projects include the Scholar Inbox paper recommender platform, ReSim (reliable world simulation), and advancements in 3D scene generation (e.g., UrbanCAD, PrITTI). His lab maintains a strong focus on open-source tools and datasets, such as the CARLA Route Generator. Grants and funding include support from Vector Stiftung (MINT innovation program) and EU initiatives like the ML in Science Cluster. His team collaborates internationally, with recent work presented at CVPR, SIGGRAPH, and NeurIPS.
Adriana Schulz is an Assistant Professor in the Department of Computer Science & Engineering at the University of Washington's College of Engineering. She leads a research group focused on computational design, computer-aided design (CAD), and digital fabrication. Her work bridges computer science with practical applications in manufacturing, robotics, and sustainable design. Dr. Schulz received her Ph.D. in Computer Science from MIT in 2018 under the supervision of Professor Wojciech Matusik. Prior to her doctoral studies, she earned a Master's degree in Mathematics from IMPA (Instituto Nacional de Matemática Pura e Aplicada) in Rio de Janeiro, where she worked with Professor Luiz Velho, and a Bachelor's degree in Electronics Engineering from UFRJ (Federal University of Rio de Janeiro). Her research interests center around computational tools that enhance design and manufacturing processes. She develops novel algorithms for CAD systems, computational fabrication techniques, and sustainable design approaches. Her work spans multiple domains including robotics, textiles, electronics, and architecture, with a strong emphasis on creating practical tools that designers and engineers can use in real-world applications. She explores how machine learning, particularly neurosymbolic approaches, can improve design workflows and enable new capabilities in computational design systems. Analysis of her recent publications reveals a strong trend toward more intelligent and user-centered design tools. Her research increasingly integrates machine learning with traditional CAD systems to create more intuitive interfaces, supports sustainable design practices with computational tools, and develops novel fabrication techniques that push the boundaries of what's possible with digital manufacturing. She has made significant contributions to zero-waste fashion design, immersion cooling for high-performance computing, and CAD program understanding through novel representation learning techniques. Innovators Under 35 - MIT Technology Review Bolsa Aluno Nota 10 from FAPERJ Engineer 20000 award Dr. Schulz actively mentors several PhD students and postdoctoral researchers, including Haisen Zhao, Ben Jones, Yuxuan Mei, and others, often in collaboration with colleagues across different departments. Her research has attracted significant media attention, with coverage in major outlets including MIT News, BBC, IEEE Spectrum, Wired, and TechCrunch. Her work on Interactive Robogami was noted as the most read article in the International Journal of Robotics Research in its publication year. She leads a vibrant research group at the University of Washington that focuses on computational design systems, with particular emphasis on creating tools that bridge the gap between digital design and physical fabrication. Her team develops novel algorithms for CAD systems, computational fabrication techniques, and sustainable design approaches that have practical applications across multiple industries.
Freda Shi is an Assistant Professor at the David R. Cheriton School of Computer Science at the University of Waterloo and a Faculty Member at the Vector Institute, where she holds a Canada CIFAR AI Chair. She joined the University of Waterloo in July 2024 after completing her Ph.D. at the Toyota Technological Institute at Chicago. Educational Background: Ph.D. in Computer Science, Toyota Technological Institute at Chicago (2024), advised by Professors Karen Livescu and Kevin Gimpel Bachelor's degree in Intelligence Science and Technology (Computer Science Track) with a minor in Sociology, Peking University (2018) Dr. Shi's research focuses on computational linguistics and natural language processing, particularly on deeper understandings of natural language and the human language processing mechanism. She is especially interested in learning language through grounding, computational multilingualism, and related machine learning aspects. Her work aims to inform the design of more efficient, effective, safe, and trustworthy NLP systems. She leads the CompLING Lab at the University of Waterloo, which investigates how language models process spatial relationships and acquire linguistic structures through grounded experiences. Her publication record shows a consistent trajectory of high-impact research, with recent work focusing on spatial reasoning in vision-language models, multilingual chain-of-thought capabilities, and grounded language acquisition. She has published in top-tier conferences including ACL, EMNLP, ICLR, and NAACL, with several papers receiving notable recognition including Best Paper Nominee status at multiple venues. Her research bridges theoretical linguistics with practical NLP applications, demonstrating how linguistic insights can improve AI systems. Scientific Recognition: Canada CIFAR AI Chair (2024) Google Ph.D. Fellowship Thesis of Distinction for her doctoral work Multiple Best Paper Nominee awards at major NLP conferences Dr. Shi teaches CS 784: Computational Linguistics and CS 486/686: Introduction to Artificial Intelligence at the University of Waterloo. She actively contributes to the NLP research community through conference participation, program committee service, and collaborative projects. Her research has significant implications for creating more robust, human-like language understanding systems and advancing the field of grounded language learning in artificial intelligence.
Yi Fang is an Associate Professor of Computer Engineering and an affiliated Associate Professor of Computer Science at New York University Abu Dhabi (NYUAD), and a Global Network Associate Professor at NYU Tandon. He is a core faculty member in the Division of Engineering, specializing in Electrical and Computer Engineering. His research is centered at the intersection of Embodied AI, Robotics, and AI-driven assistive technologies, with strong support from agencies such as the US NSF, UAE ADEK, and ASPIRE. PhD, Purdue University Yi Fang's research interests span 3D Computer Vision, Multimedia Processing, Machine Learning, Deep Learning, and Embodied AI . He focuses on AI-driven perception, learning, and real-world applications, particularly in engineering, medicine, and accessibility. His lab, the Embodied AI and Robotics (AIR) Lab, develops intelligent robotic systems that integrate perception, learning, and decision-making to solve complex societal challenges. His work emphasizes large-scale visual computing, deep visual learning, and cross-domain/multimodal foundation models , with recent innovations in assistive AI for the Deaf and Hard-of-Hearing community. The 15 most recent publications reflect a consistent focus on 3D vision, sketch-based 3D retrieval, point cloud learning, and assistive computer vision . His work leverages deep learning, adversarial training, metric learning, and generative models to bridge modalities such as sketches, depth images, and 3D models. There is a clear trend toward cross-modal understanding, unsupervised representation learning, and real-world assistive applications , especially for visually impaired individuals. Yi Fang actively contributes to the academic community as an Area Chair for top-tier conferences including CVPR, ECCV, ICCV, IJCAI, and IROS. He also serves in peer review and mentoring roles, shaping the future of AI and robotics research. As a dedicated educator, he teaches foundational and advanced courses such as Computer Vision, Applied Machine Learning, Data Structures, and Capstone Design . He mentors students through research seminars and honors projects, fostering innovation and technical excellence. His research is supported by major grants from US NSF, UAE ADEK, and ASPIRE, enabling high-impact interdisciplinary collaborations. He founded and directs the Embodied AI and Robotics (AIR) Lab at NYU Abu Dhabi, a dedicated research space for developing intelligent systems that seamlessly integrate perception, learning, and decision-making. The lab promotes interdisciplinary collaboration across engineering, medicine, and social sciences, advancing the frontiers of Embodied AI.
Georgia Gkioxari is an Assistant Professor in the Division of Computing and Mathematical Sciences at Caltech , with a part-time affiliation at Meta AI . Her work focuses on extending visual perception models through advanced 2D and 3D representation learning, spatial reasoning, and generative models. Education: Not explicitly mentioned in the text Research interests span 3D perception , spatial reasoning , and vision-language integration , with projects like Visual Agentic AI for Spatial Reasoning and Token-by-Token Multimodal Alignment . Her publications emphasize 3D object detection , reconstruction , and generative modeling techniques including diffusion models and transformers . Scientific recognition includes the Meta LLM Evaluation Research Grant , Okawa Research Grant , Google Faculty Scholar Award 2024 , and Amazon Research Award . She teaches courses like Large Language & Vision Models (EE/CS 148) and Learning & 3D (CS 101) at Caltech. Labs & Teams: Leads Glab with members including Ilona Demler, Ziqi Ma, and Damiano Marsili
Prof. Dr. Otmar Hilliges is a Full Professor at the Department of Computer Science at ETH Zurich. He leads the AIT lab and serves as the head of the Institute of Intelligent Interactive Systems. His research focuses on spatio-temporal understanding of human movement and interaction, leveraging algorithms and representations from videos, images, and sensor data for applications in Augmented Reality (AR), Virtual Reality (VR), and Human-Robot Interaction. Education: Diplom (MSc) in Computer Science, Technical University of Munich (TUM), Germany PhD in Computer Science, Ludwig Maximilian University of Munich (LMU), Germany (2009) Research Interests: Hilliges' work spans computer vision, robotics, and human-computer interaction. He develops methods for 3D human pose estimation, generative models for realistic avatar creation, and physically plausible simulation of human-object interactions. His research emphasizes practical applications in AR/VR and assistive robotics, aiming to bridge the gap between perception and action. Grants & Contributions: ERC Consolidator Grant (2022-2027): 'AI-Perceive: Robust Human-Centric Computer Vision for Advanced AI-Agents' Google Research Agreement (2020-2025): 'Generative Modelling of Humans' Microsoft Research Grants: Focus on human-centric robotics and interactive technologies Labs & Teams: Leads the AIT Lab at ETH Zurich, which pioneers research in intelligent interactive systems, emphasizing human-centric AI and robotics. The lab collaborates on projects ranging from drone cinematography to haptic feedback systems.
Mark Yatskar is an Assistant Professor in the Department of Computer and Information Science at the University of Pennsylvania. His research focuses on the intersection of natural language processing, computer vision, and fairness in machine learning. He earned his PhD from the University of Washington under advisors Luke Zettlemoyer and Ali Farhadi, and previously worked as a Young Investigator at the Allen Institute for Artificial Intelligence. Education: PhD in Computer Science, University of Washington (Advisor: Luke Zettlemoyer & Ali Farhadi) Research Interests: Yatskar's work explores how language can structure visual perception and mitigate human biases in machine learning systems. Key themes include: Natural language as a scaffold for visual intelligence Bias characterization and control in machine learning systems His lab currently investigates projects like language-guided bottlenecks, annotator cognitive heuristics, and gender bias amplification. Teaching: CIS 5300: Computational Linguistics (2021-2024) CIS 7000: Language and Vision (2020) CIS 6300: Efficient NLP (2023, 2025) Awards: Best Paper Award at EMNLP (Gender Bias Amplification Research) Advising & Grants: Yatskar advises a team of PhD/Master's students and actively seeks motivated researchers. His group has explored funding in areas like interpretable AI, multimodal reasoning, and dataset bias mitigation. Labs/Teams: Leads the Penn NLP & Vision Lab, focusing on projects like MolMo/PixMo open models, ViUniT visual unit tests, and bias mitigation frameworks.
Lara Ferry is the Vice President of Research at Arizona State University (ASU), holding the title of President’s Professor. She is affiliated with the New College of Interdisciplinary Arts and Sciences, specifically the School of Mathematical and Natural Sciences, and serves as graduate faculty in the School of Life Sciences. Her roles include advancing research strategies, fostering interdisciplinary collaborations, and enhancing partnerships with institutions, communities, donors, and industry. Ferry is also a PLuS Alliance Fellow, a Senior Global Futures Scientist at the Global Institute of Sustainability and Innovation, and affiliated with the Biodesign Institute and Global Security Initiative. She holds honors faculty status in ASU’s Barrett Honors College. Her academic background includes a B.S. in Biology from Cal Poly State University, M.S. in Marine Science from San Francisco State University, Ph.D. in Ecology and Evolutionary Biology from UC Irvine, and postdoctoral work at UC Davis. Her research focuses on functional morphology, particularly the evolution of jaws and skeletal materials in aquatic organisms. She has contributed to understanding prey capture mechanisms in fish and the biomechanics of cartilage in elasmobranchs. Ferry’s awards include the 2016 Outstanding Faculty Mentor Award, 2014 Outstanding Research/Creative Activity Award, and the 2018 President’s Professor title. She has led initiatives in equity, diversity, and inclusion (JEDI) and served on boards of professional societies like the American Elasmobranch Society and Society for Integrative and Comparative Biology. Her editorial roles include Senior Executive Editor of Functional Ecology and editorial board memberships in journals like Integrative & Comparative Biology . Her research activity spans functional morphology, biomechanics, and marine physiology, with grants focused on interdisciplinary education and diversity in STEM. Notable projects include studies on cartilage strength in sharks and collaborations on cultural change in professional societies.
