Joel Andreas is a Professor of Sociology at Johns Hopkins University since 2003, serving as Program Director of the East Asian Studies Program. His research focuses on political contention, social inequality, and social change in contemporary China. He holds a PhD in Sociology from UCLA and teaches social theory, political sociology, and courses on Chinese society. He has held visiting positions at universities including the University of Sydney, Hong Kong University, and Ningxia University. His work examines labor movements, rural land policies, and class dynamics in China. Recent research includes analyses of staff-worker congresses, rural land dispossession, and the persistence of militarism in U.S. policy. He critiques market reforms and explores the legacy of the Cultural Revolution. Andreas has contributed to debates on China’s transition to capitalism and the decline of socialist systems. His publications span topics from urbanization to state coercion in rural development. He maintains an active role in academic exchanges and policy-relevant research on China’s socio-political landscape.
Professor David A Palmer is a distinguished academic jointly appointed by the Hong Kong Institute for the Humanities and Social Sciences and the Department of Sociology at the University of Hong Kong, where he has been teaching since 2008. His interdisciplinary work bridges anthropology, sociology, religious studies, and Sinology, establishing him as a leading scholar in Chinese religious studies and transnational religious movements. Palmer's educational background includes a PhD in Anthropology of Religion from Ecole Pratique des Hautes Etudes (Sorbonne, Paris), an MPhil in Clinical Psychology/Medical Anthropology from University of Paris-VIII, and a BA in Anthropology and East Asian Studies from McGill University. His scholarly contributions span multiple disciplines with particular emphasis on Chinese religious traditions and their global manifestations. His research interests focus on the anthropology and sociology of spirituality and religion, especially Daoism and Chinese religion; religion in modern and contemporary China; inter-Asian and transnational religious circulations; Baháʼí Faith; civil society, volunteering, philanthropy, community engagement; and cultural and religious interactions on the Belt and Road. His work examines how religious traditions navigate modernity, state power, and globalization, with particular attention to the Belt and Road Initiative's cultural dimensions. Palmer's publications reveal a consistent focus on understanding Chinese religious traditions within broader theoretical frameworks of social theory, civil society, and transnational religious networks. His articles demonstrate how Chinese religious practices and institutions both adapt to and shape contemporary social and political contexts, with particular attention to the intersections of spirituality, modernity, and state-society relations. Joseph Levenson Award of the Association for Asian Studies PROSE award of the American Publishers' Association Francis L.K. Hsu Award of the Society for East Asian Anthropology Edward Bruner Prize for the best book in Anthropology of Tourism HK Research Grants Council RGC Research Fellowship (2021-2025) HK Research Grants Council Humanities and Social Sciences Prestigious Fellowship (2019) Palmer currently leads multiple significant research projects including 'Global China, Local Cultures: Chinese Modernity and Soft Power on the Belt and Road' (RGC Research Fellowship, 2021-2025), 'Infrastructures of Faith: Religious Mobilities on the Belt and Road' (CRF, 2019-2023), 'Rituals and Manuscripts of the Lanten Yao (Laos)' (GRF, 2021-2024), and 'Religious Cosmopolitanism in China, 1895-1927' (GRF, 2021-2024). He leads the 'Asian Religious Connections' research cluster at HKIHSS, co-convenes the 'Culture, Community and Social Equity' research cluster in the HKU Faculty of Social Sciences, and serves as co-convenor of the HKU Anthropology Research Network. As President of the East Asian Society for the Scientific Study of Religion, he plays a key role in advancing scholarly understanding of religion in the region.
Ching Kwan Lee is a Professor of Sociology at the University of California, Los Angeles (UCLA). Her work focuses on global and comparative issues, including labor politics, political sociology, decolonization, and the dynamics of Chinese capitalism. She is renowned for her trilogy on Chinese labor and global expansion, including Gender and the South China Miracle , Against the Law , and The Specter of Global China . Lee has also authored groundbreaking studies on Hong Kong’s decolonization struggles and African-China relations. Education: PhD in Sociology from the University of California, Berkeley. Research Interests : Globalization and its social impacts Comparative labor movements and protest dynamics Decolonization and postcolonial state formations Sino-African economic and labor relations Hong Kong’s political trajectory and transnational activism Her recent work explores resistance to 'global China,' including mass mobilizations in Hong Kong documented in Forever Hong Kong: A Global City’s Decolonization Struggle (2025). She co-edits the Cambridge Elements in Global China and leads the Global Hong Kong Studies @UC initiative. Awards & Recognition : 2024 Distinguished Career in Political Sociology Award Multiple book prizes for The Specter of Global China Fellowships at Stanford’s Center for Advanced Study and Princeton’s Institute for Advanced Study Grants & Initiatives : Lead researcher on NSF grants studying platform capitalism and labor in China Initiated comparative projects on Brazil, India, China, and South Africa ( Southern Interregnum ) Co-editor of volumes on Hong Kong’s Umbrella Movement and global labor studies
Ricky Ini Liu is an Associate Professor in the Department of Mathematics at the University of Washington. Previously, he held positions at North Carolina State University, the University of Michigan, and the University of Minnesota. He earned his Ph.D. in Mathematics from MIT in 2010 under Alexander Postnikov. His research focuses on algebraic combinatorics, particularly its intersections with algebraic geometry, combinatorial geometry, and representation theory. Key interests include Schubert polynomials, polytopes, Hopf algebras, and Kronecker coefficients. He has contributed to foundational work on birational rowmotion, Gelfand-Tsetlin polytopes, and Fomin-Kirillov algebras. Liu has taught a wide range of courses at UW, including special topics in dynamical algebraic combinatorics, combinatorial theory, and problem-solving. He has also been a key instructor at the Mathematical Olympiad Summer Program since 2007 and mentored undergraduates in research programs at the University of Minnesota, Duluth. His publications span high-impact journals like Selecta Mathematica and Journal of Combinatorial Theory , with recent work addressing topics such as determinantal formulas for Schubert polynomials and applications of flow polytopes to diagonal harmonics. Though no specific awards are listed, his extensive publication record and academic roles reflect significant contributions to combinatorial mathematics.
Marsha E. Barrett is an Assistant Professor of History at the University of Illinois at Urbana-Champaign, specializing in US political history, policy history, and African American history. She holds a PhD from Rutgers University and a BA from Yale University. Her research focuses on 20th-century American politics, particularly the evolution of Republicanism, the political influence of figures like Nelson Rockefeller, and voter dynamics among Black communities in New York. Education PhD, History, Rutgers University BA, History, Yale University Research Interests United States political history Policy history African American history 20th-century political ideologies and governance Her work examines how political figures and institutions shaped modern US politics, with a focus on New York's political landscape and the Republican Party’s ideological shifts. Recent Scholarship Explores Nelson Rockefeller’s role in moderate Republicanism and its decline Analyzes voter loyalty among Black New Yorkers during the Cold War era Investigates the intersection of wealth and political rhetoric in mid-20th century New York Awards & Honors ACLS Fellowship (2019) Lincoln Excellence for Assistant Professors (LEAP) Scholar (2019) 2014 Dixon Ryan Fox Manuscript Prize Teaching & Grants Teaches courses on American political history, African-American women’s history, and presidential politics Recipient of grants from the Ford Foundation, Rockefeller Archive Center, and New York State Archives
Kristen Grauman is a Full Professor in the Department of Computer Science at the University of Texas at Austin, where she leads the UT Computer Vision Group. Her research focuses on computer vision and machine learning, with applications in visual recognition, video analysis, and multi-modal perception. She received her B.A. from Boston College and her Ph.D. from MIT. Her research interests span visual recognition, image and video search, video analysis, first-person vision, embodied and multi-modal perception, and interactive machine learning. She has made significant contributions to the field, particularly in developing algorithms for understanding visual content and human activities from video, including foundational work on the Pyramid Match Kernel and relative attributes. Her recent publications reveal a strong emphasis on egocentric (first-person) vision, audio-visual learning, and view-invariant representations. There is a clear trajectory toward multi-modal integration (vision, audio, language) and real-world applications in instructional videos, human activity understanding, and embodied AI systems. She has received numerous awards including: AAAI Fellow (2019) J. K. Aggarwal Prize, International Association for Pattern Recognition (2018) Helmholtz Prize (2017) UT Austin Academy of Distinguished Teachers (2017) Best Paper Award, Asian Conference on Computer Vision (2016) Presidential Early Career Award for Scientists and Engineers (2014) Computers and Thought Award, International Joint Conferences on Artificial Intelligence (2013) Pattern Analysis and Machine Intelligence Young Researcher Award (2013) Alfred P. Sloan Research Fellow (2012) Marr Prize (2011) Prof. Grauman serves as Associate Editor-in-Chief for the IEEE Transactions on Pattern Analysis and Machine Intelligence. She has secured substantial research funding including the Presidential Early Career Award, NSF grants, and industry partnerships. Her advising has produced numerous influential publications and students who are now leaders in computer vision. She leads the UT Computer Vision Group, which collaborates closely with the Electrical and Computer Engineering Department. The group is pioneering large-scale egocentric video research through projects like Ego4D and Ego-Exo4D, focusing on real-world applications in human activity understanding, audio-visual perception, and interactive systems.
Hao Su is an Associate Professor in the Department of Computer Science and Engineering at University of California, San Diego . He serves as Chairman & CTO of Hillbot Inc , and leads the SU Lab which focuses on building autonomous systems that learn actively in physical environments. His affiliations include the Institute for Learning-enabled Optimization at Scale , Artificial Intelligence Group , Contextual Robotics Institute , Halicioğlu Data Science Institute , and Center for Visual Computing . As a researcher in Computer Vision, Robotics, and Neural Geometry , he has made significant contributions to 3D foundation models, reward-free world models, diffusion policy frameworks, and GPU-accelerated simulation environments. His 2024-2025 publications include advancements in hand-eye calibration, dynamic mesh reconstruction, and multi-stage robotic manipulation. His scientific awards include: Frontiers of Science Award (2025) TPAMI Young Research Award (2025) NSF CAREER Award (2023) ACM SIGGRAPH Best Doctorate Thesis Honorable Mention (2019) He has served as Program Chair for CVPR 2025 and Area Chair for ICLR 2022 and NeurIPS 2023 , while previously serving as Publication Chair for 3DV 2016 and Program Committee for SIGGRAPH Asia Workshops .
Huan Zhang serves as an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign (UIUC), with affiliate appointments in the Department of Computer Science and the Coordinated Science Laboratory. His research focuses on building trustworthy AI systems with formal verification techniques to provide provable guarantees for safety-critical applications, particularly in machine learning and neural networks. Dr. Zhang received his Ph.D. in Computer Science from UCLA in 2020, advised by Professor Cho-Jui Hsieh. His academic journey includes an M.S. in Computer Engineering from UC Davis (2014) and a Bachelor of Engineering from Zhejiang University (2012). Prior to joining UIUC, he completed a postdoctoral fellowship at Carnegie Mellon University (2021-2023) with Professor Zico Kolter. Huan Zhang's research program centers on formal verification of machine learning systems, with particular emphasis on neural network verification, AI safety, robustness, and reliability. He pioneered the linear bound propagation-based verification framework that enables formal verification for networks with millions of neurons. His work spans five major research categories: formal verification of machine learning, training trustworthy ML models, machine learning safety and adversarial attacks, reinforcement learning safety, and optimization for scalable machine learning. His CROWN framework (NeurIPS 2018) established a foundational approach for neural network verification through efficient linear bound propagation. His recent publications demonstrate a strategic expansion from foundational verification techniques toward increasingly complex systems including large language models, vision-language models, and robotic control systems. The research trajectory shows a clear progression from theoretical frameworks to practical implementations with real-world applications, particularly in safety-critical domains. His work increasingly bridges formal methods with practical AI deployment requirements. Winner of International Verification of Neural Networks Competition (VNN-COMP) as team leader (2021-2024) Schmidt Futures AI2050 Early Career Fellowship ($300,000 research grant) Adversarial Machine Learning (AdvML) Rising Star Award (2021) IBM PhD Fellowship (2018) Dr. Zhang leads the development of α,β-CROWN, a neural network verifier that has won VNN-COMP 2021-2023, and auto_LiRPA, a PyTorch-based library for perturbation analysis on general computational graphs. He has mentored numerous graduate students from CMU, UCLA, UIUC, and Columbia University. His research is supported by significant funding including the Schmidt Futures fellowship and industry collaborations. He teaches courses including ECE 120, ECE 484, ECE 584, and ECE 598 HZ on topics ranging from computing fundamentals to safe autonomy and machine learning. Dr. Zhang maintains active research collaborations across multiple institutions and is affiliated with UIUC's Coordinated Science Laboratory. His work has significant implications for safety-critical AI applications in autonomous systems, healthcare, and other mission-critical domains where reliability guarantees are essential. He regularly gives guest lectures at institutions including Yale, Stony Brook, and the University of Nebraska Lincoln on formal verification techniques.
Lerrel Pinto is an Assistant Professor of Computer Science at the Courant Institute of Mathematical Sciences at New York University (NYU), where he leads the General-purpose Robotics and AI Lab (GRAIL) as part of the CILVR research group. His work bridges the gap between theoretical machine learning and practical robotics applications, with a focus on enabling robots to generalize and adapt in real-world environments. Dr. Pinto received his undergraduate degree from IIT Guwahati, followed by a PhD from the Robotics Institute at Carnegie Mellon University (CMU). He then completed a postdoctoral fellowship at the University of California, Berkeley before joining NYU as faculty. His research program centers on robot learning and decision making, with several key thrusts that demonstrate his innovative approach to robotics. Pinto's work emphasizes large-scale learning techniques that leverage both extensive data and sophisticated model architectures. A significant portion of his research focuses on representation learning for sensory data, particularly developing methods that enable robots to make sense of visual, tactile, and auditory inputs. His lab has made notable contributions to reinforcement learning algorithms that allow robots to adapt to new scenarios with minimal retraining. Pinto also champions open-source robotics , developing affordable robot platforms that democratize access to robotics research. Analysis of Pinto's recent publications reveals a strong trend toward multimodal perception in robotics, integrating visual, tactile, and auditory information to create more robust robot systems. His work increasingly focuses on zero-shot and few-shot learning capabilities, enabling robots to handle novel situations without extensive retraining. There's also a clear progression toward general-purpose robotics , moving away from task-specific solutions toward more flexible systems that can handle diverse real-world challenges. Dr. Pinto's scientific contributions have been recognized with several prestigious awards: Sloan Research Fellowship (2025) NSF CAREER Award (2024) RAL Early Career Award (2024) Best Student Paper Award at ICRA (2016) Outstanding Paper Award at MFM-EAI workshop at ICML (2024) Best Paper Award at NGSM workshop at ICML (2024) Best Student Paper Award at RSS (2023) As an advisor, Pinto has mentored numerous students who have gone on to impactful careers in both academia and industry. His former PhD student Denis Yarats co-founded Perplexity.AI, while Mahi Shafiullah became a postdoc at UC Berkeley and Meta AI. Many of his Masters students have pursued PhDs at top institutions like CMU, MIT, and Stanford, or joined leading robotics companies including 1X, Fauna Robotics, and NVIDIA. Pinto's lab has secured significant research funding, including the NSF CAREER award and likely other grants supporting his robotics research program. The General-purpose Robotics and AI Lab (GRAIL) that Pinto leads brings together a diverse team of researchers working on cutting-edge robotics challenges. The lab maintains strong collaborations with industry partners and other academic institutions, facilitating technology transfer and real-world impact. GRAIL's research spans multiple robotics platforms and focuses on developing algorithms that enable robots to learn from diverse experiences and generalize across environments.
Samuel J. Gershman is a Professor of Psychology at Harvard University, affiliated with both the Department of Psychology and the Center for Brain Science. He directs the Computational Cognitive Neuroscience Lab (CCNLab), where he investigates how the brain acquires richly structured knowledge about the environment and uses this knowledge to guide adaptive behavior. Gershman received his B.A. in Neuroscience and Behavior from Columbia University in 2007 and his Ph.D. in Psychology and Neuroscience from Princeton University in 2013, followed by postdoctoral training in the Department of Brain and Cognitive Sciences at MIT (2013-2015). His research spans computational neuroscience, cognitive psychology, and machine learning. His primary research interests include learning, memory, decision making, and computational neuroscience. Gershman's work integrates behavioral, neuroimaging, and computational techniques to understand cognitive processes. He has made significant contributions to understanding memory systems, reinforcement learning, and the computational principles underlying human cognition. Analysis of Gershman's recent publications reveals a strong focus on computational approaches to understanding cognitive processes, with particular emphasis on memory systems, decision-making mechanisms, and the intersection of artificial intelligence with cognitive neuroscience. His work often bridges theoretical computational models with empirical neuroscience data, exploring how the brain implements efficient cognitive algorithms. Gershman actively mentors graduate students and postdoctoral researchers, with current advisees working on diverse projects spanning computational modeling, neuroimaging, and behavioral experiments. His lab investigates topics ranging from dopamine signaling to social cognition using a combination of theoretical and experimental approaches. The CCNLab, which Gershman directs, brings together researchers from psychology, neuroscience, computer science, and related fields to explore the computational principles of cognition. The lab utilizes a range of methodologies including behavioral experiments, neuroimaging, computational modeling, and theoretical analysis to address fundamental questions about how the mind works.
Professor Jamie Peck is a University Killam Professor and Professor of Geography at the University of British Columbia, Faculty of Arts, Department of Geography. He holds the prestigious title of Distinguished University Scholar at UBC and maintains significant international academic affiliations including Visiting Professor at Oxford University's School of Geography and the Environment, Honorary Professorial Fellow at the University of Manchester, and Honorary Professor at the University of Nottingham. Peck received his PhD in Geography from the University of Manchester in 1988 following his BA (Hons) in Geography from the same institution. His distinguished academic career spans positions at the University of Manchester (1989-2000), University of Wisconsin-Madison (2000-2008), and his current position at UBC where he has been a central figure in economic geography and interdisciplinary social science. As an institutional political economist, Professor Peck's research critically examines the spatial dimensions of economic processes, with particular attention to how global phenomena like neoliberalization are constituted through local practices. His work bridges economic geography, urban studies, political economy, and social theory, consistently challenging conventional understandings of market processes and statecraft. Recent research focuses on Chinese state capitalism, especially the Greater Bay Area and Hong Kong's evolving political status, alongside methodological innovations in conjunctural analysis as demonstrated in his 2023 book Variegated Economies . His scholarly output reveals a sustained engagement with the variegated nature of capitalist development across different spatial contexts. Recent publications demonstrate a sophisticated methodological approach combining institutional analysis with extended case methods, emphasizing historical context and spatial specificity while challenging universalizing theoretical claims. His work on Chinese political economy represents a significant departure from conventional Western-centric analyses. University Killam Professor (2025) Lauréat Prix International de Géographie Vautrin Lud ("Nobel de Géographie") (2023) Corresponding Fellow of the British Academy Fellow of the Royal Society of Canada Clarivate Highly Cited, Social Sciences (2014-2024) Distinguished University Scholar, UBC (2017-present) Canada Research Chair in Urban & Regional Political Economy (2008-2022) Professor Peck has supervised numerous doctoral students whose dissertations examine diverse topics including global urban solutions industries, US-Mexico border economies, Rio de Janeiro's 'PACification' processes, and China's experimental economic zones. His supervision approach emphasizes theoretically sophisticated empirical work that connects local case studies to broader political economic processes. His Canada Research Chair position supported extensive research on urban and regional political economy, while his current Killam Professorship funds ongoing work on Chinese political economy and conjunctural analysis methodologies. While not formally affiliated with a specific laboratory, Professor Peck is deeply engaged with multiple scholarly networks through his editorial leadership as Managing Editor of EPA: Economy & Space and co-editor of the Economic Transformations book series. His role as Convenor of the Summer Institute in Economic Geography demonstrates his commitment to fostering scholarly exchange and theoretical development in economic geography as a discipline.
Alexander Schwing is an Associate Professor in the Department of Electrical and Computer Engineering and Computer Science at the University of Illinois at Urbana-Champaign, affiliated with the Coordinated Science Laboratory. His research focuses on machine learning and computer vision with applications in 3D scene understanding, generative modeling, and multi-agent systems. Education: Diploma in Electrical Engineering and Information Technology, Technical University of Munich (TUM) PhD in Computer Science, ETH Zurich Postdoctoral Fellow, University of Toronto Research Interests: Structured prediction in deep learning Generative adversarial networks and stability Multi-modal vision-language models 3D scene reconstruction from single images Embodied agent collaboration Semantic segmentation with temporal coherence Recent Publications: Highlight trends in neural rendering, video object segmentation, and reinforcement learning with applications to 3D modeling and multi-agent systems. Notable innovations include SAIL-VOS dataset for amodal segmentation and NeRFDeformer for single-view scene transformation. Scientific Awards: NSF CAREER Award, 3M and Amazon research awards, multiple student recognition awards, ETH Zurich PhD medal, and best paper at Intelligent Tutoring Systems 2014. Teaching: Offers graduate courses in Pattern Recognition (ECE 544) and Machine Learning (CS 446/ECE 449). Previously taught at University of Toronto and ETH Zurich. Labs & Collaborations: Leads research at Coordinated Science Laboratory (UIUC) with collaborations across University of Toronto, ETH Zurich, and industry partners like Samsung SAIT and Amazon.
Jackie Chit Kit Cheung is an Associate Professor in the School of Computer Science at McGill University, where he co-directs the Reasoning and Learning Lab. He holds the Canada CIFAR AI Chair and serves as an Associate Scientific Co-Director at the Mila Quebec AI Institute. He is also a consulting researcher at Microsoft Research Montreal. Academic Background: Ph.D. in Computer Science, University of Toronto (2010–2014) M.Sc. in Computer Science, University of Toronto (2008–2010) B.Sc. (Honours) in Computer Science, minors in Linguistics and German, University of British Columbia (2004–2008) Research Interests: Jackie Cheung's research lies at the intersection of natural language processing, machine learning, and cognitive science. He focuses on natural language generation , automatic summarization , commonsense and pragmatic reasoning , and the evaluation of NLP systems . His work aims to build language models that reflect real-world structure and reasoning, with applications in health, education, and language revitalization. He is particularly interested in how implicit meaning is processed in context, a core concern in linguistic pragmatics. Publication Trends: His recent publications (2024–2025) show a strong emphasis on evaluation methodologies (e.g., COSMIC, ECBD), hallucination and factuality in language models, coreference and reasoning , and long-context modeling . He frequently collaborates across institutions and integrates insights from linguistics and psychology into NLP system design and analysis. Scientific Awards: Best Paper Award, ACL 2018 Outstanding Paper Award, NAACL 2018 Student Research Workshop SAC Award, ACL 2024 (for COSMIC) Best Poster Award, CMDO AI-Health Symposium 2024 Advising and Grants: He advises a large and diverse group of graduate students, including PhD and Master’s candidates, often in co-supervision with other faculty. His group has received support from major AI and health research initiatives, including CIFAR and Mila. He has trained alumni who have gone on to faculty and industry research positions. He has served in leadership roles in top NLP conferences, including as Senior Area Chair, Workshop Chair, and Program Chair of Canadian AI 2018. Labs and Teams: He co-directs the Reasoning and Learning Lab at McGill and is deeply involved with Mila – Quebec AI Institute . He founded the NLP Reading Group at McGill, which brings together researchers from computer science, linguistics, and information studies to discuss theoretical and applied NLP topics.
Alexei A. Efros is a Professor in the Department of Electrical Engineering and Computer Sciences (EECS) at UC Berkeley, where he holds the Howard Friesen Professorship and is affiliated with the Berkeley Artificial Intelligence Research (BAIR) Lab. He previously served on the faculty at the Robotics Institute of Carnegie Mellon University (CMU) and completed a postdoctoral fellowship at the University of Oxford. His research spans data-driven computer vision, self-supervised learning, computational photography, and applications to computer graphics and robotics. His research interests include: Data-Driven Computer Vision Self-Supervised and Unsupervised Learning Generative Models and Image Synthesis Visual Representation Learning Applications in Robotics and Human-Computer Interaction Intersections with Human Vision and the Humanities The recent publications highlight a strong trend toward self-supervised learning, visual reasoning, and generative modeling, particularly diffusion models and 3D scene understanding. His work increasingly bridges computer vision with language, robotics, and cognitive science, emphasizing interpretability and real-world applicability. There is a clear focus on leveraging unlabeled data and developing methods for robust, generalizable AI systems. His scientific awards and recognitions include: Berkeley Fellowship Google Fellowship Soros Fellowship NSF Fellowship SIGGRAPH Outstanding Doctoral Dissertation Award Facebook Fellowship Adobe Fellowship CMU School of Computer Science Distinguished Dissertation Award ACM Doctoral Dissertation Honorable Mention Alexei Efros has advised numerous PhD students and postdocs, many of whom have gone on to faculty positions at top institutions including CMU, Stanford, MIT, Columbia, NYU, and Georgia Tech. His lab has received research funding from major tech companies and federal agencies, though specific grants are not detailed in the text. He teaches core computer vision and machine learning courses at both undergraduate and graduate levels at UC Berkeley. His research group is highly active, with ongoing projects in 3D perception, generative modeling, and vision-language systems. He leads a vibrant research lab at UC Berkeley, part of the BAIR consortium, collaborating with leading researchers such as Jitendra Malik, Trevor Darrell, Pieter Abbeel, and Angjoo Kanazawa. His lab fosters strong interdisciplinary connections with institutions worldwide, including Oxford, INRIA, and École Normale Supérieure.
Wei-Lun (Harry) Chao is an Associate Professor in the Department of Computer Science and Engineering at the Ohio State University (OSU), College of Engineering. Promoted to this role in May 2025, he is also an Innovation Scholar and Distinguished Assistant Professor of Engineering Inclusive Excellence. His work spans machine learning, computer vision, and their applications in autonomous driving, healthcare, biology, and natural language processing. Research Focus: Machine learning with imperfect data, interpretable and personalized learning, robust perception for autonomous systems, and visual recognition in real-world scenarios. Awards: 2025 OSU Early Career Distinguished Scholar Award, CVPR Best Student Paper Award (2024), CSE Faculty Teaching Award (2024), Lumley Research Award (2023). Grants: Funded by NSF, NIH, ONR, Cisco, AWS, and Google. Notable Research Trends: The 15 most recent articles highlight his work on vision foundation models, federated learning, diffusion models for biological species generation, interpretable vision transformers, and robust perception systems for autonomous driving. Key subfields include sparse autoencoders, 3D object detection, semi-supervised learning, and anomaly detection in scientific domains. Scientific Awards: 2025 Early Career Distinguished Scholar Award (OSU) CVPR Best Student Paper Award (2024) CSE Faculty Teaching Award (2024) Lumley Research Award (2023) Mentoring & Grants: As an advisor for the OSU Buckeye AutoDrive Team and AI Club, he mentors graduate and undergraduate students. His research is supported by major grants from NSF, NIH, ONR, and industry partners like Cisco and Google.
