Narges Sharif Razavian is an Assistant Professor at NYU Grossman School of Medicine , holding appointments in both the Department of Population Health and Department of Radiology . She earned her PhD from Carnegie Mellon University and completed postdoctoral training at New York University's Courant Institute in Computer Science's Machine Learning group. Research focuses on applying Machine Learning and Artificial Intelligence to healthcare challenges, including Predictive Analytics for disease outcomes, Biomarker Discovery , and Medical Imaging analysis. Recent publications highlight her work on AI-driven diagnosis in oncology (lung and pancreatic cancer), hematoma expansion prediction in neurology, and real-time models for infectious disease outcomes (e.g., COVID-19). She utilizes Electronic Health Records (EHRs) and multimodal data to develop clinical decision support systems, with applications in public health surveillance and personalized medicine. Contact: Email | Phone: 212-263-2234 | Office: 227 East 30th Street, 6th Floor, Room 639, New York City
Dr. Hongye Zhang serves as a Lecturer in Superconducting and Cryogenic Electric Machines at the School of Engineering, University of Edinburgh, while maintaining a Visiting Research Fellow position at the University of Manchester. He actively contributes to the European Society for Applied Superconductivity (ESAS) as a Board Member and chairs the international HTS 2026 workshop. His educational foundation includes: BSc and MSc in Electrical Engineering from Xi’an Jiaotong University (2015, 2018) Diplôme d’ingénieur (MEng) from École Centrale de Lyon (2018) PhD in Applied Superconductivity from the University of Edinburgh (2021) Dr. Zhang’s research centers on decarbonizing transport through superconducting/cryogenic electric machines for hydrogen-powered aircraft, integrating artificial intelligence with superconductor technology and cryogenic techniques. His work targets net zero emissions by developing high-power-density propulsion systems that leverage hydrogen energy and advanced numerical modeling of superconductors. Analysis of his 2022-2025 publications reveals dominant themes in superconducting machine design for wind energy and electric aviation, with significant contributions to loss mitigation, flux pump technology, and trapped field magnet applications. His research bridges fundamental superconductor characterization with practical system integration for renewable energy. Recognized with the 2021 IEEE Council on Superconductivity Graduate Study Fellowship, his professional engagements include: Early Career Editorial Board Member for Elsevier’s Superconductivity journal Technical Editor for IEEE Transactions on Applied Superconductivity Program Committee Member for SMT 2023 He leads critical research within the £54-million H2GEAR project developing hydrogen-electric aircraft propulsion, while teaching Power Engineering 2 and Electrical Machines courses. His advisory roles span doctoral supervision and industry collaboration through Energy Systems research institute. Based at the University of Edinburgh’s Faraday Building, Dr. Zhang directs a research group focused on hydrogen energy applications and superconducting machine testing, with strong ties to the H2GEAR consortium and ESAS working groups.
Florian Lionnet is an Associate Professor of Linguistics in Princeton University's Council of the Humanities and Director of Undergraduate Studies. He holds a Ph.D. from UC Berkeley (2016) and joined Princeton in 2017. His research focuses on phonetics/phonology, typology, areal and historical linguistics, and language documentation, with a focus on African and Oceanic languages. Current projects include endangered languages in Chad and New Caledonia, involving annual fieldwork collaborations with anthropologists, ethnomusicologists, and archaeologists. Research interests emphasize tonal systems, phonological representation, and language contact, particularly in Laal (an isolate language), Fanya (Bua), and Khoisan languages. His work bridges theory and practice, contributing to both linguistic theory and endangered language preservation. Articles explore tone phonology, areal patterns, and subphonemic effects, with recent emphasis on Kalahari Basin and Oceanic languages. Teaching responsibilities include directing undergraduate studies in Linguistics. No awards or grants are explicitly listed, though his fieldwork suggests active research funding. His office is located at 1-S-18 Green Hall, Princeton University.
Richard MA Tianbai is an Associate Professor at the School of Computing, National University of Singapore (NUS), and serves as IT Coordinator for Computing Facilities. He holds a B.Sc. (First-Class Honors) and M.Phil. from the Chinese University of Hong Kong, followed by an M.Sc. and Ph.D. from Columbia University, USA. His research focuses on systems & networking, distributed computing, and internet economics, with projects addressing cloud-based big data systems and internet peering agreements. Richard has received multiple awards, including the Best Paper Award Runners-up at ACM Mobihoc 2020 and the Teaching Excellence Award from NUS School of Computing in 2019. His work bridges theoretical models with practical applications in network economics and distributed systems. Education: B.Sc. (First-Class Honors), Computer Science & Engineering, Chinese University of Hong Kong (2002) M.Phil., Computer Science & Engineering, Chinese University of Hong Kong (2004) M.Sc., Columbia University (2008) Ph.D., Columbia University (2010) Research Interests: Cloud-Based Big Data Systems: Innovating serverless paradigms for real-time analytics (e.g., Stream as a Service). Internet Economics: Modeling peering agreements, premium peering dynamics, and regulatory alternatives to net neutrality. Distributed Computing: Auto-scaling frameworks (e.g., Elasticutor, DRS) for real-time stream processing. His research emphasizes performance guarantees and resource optimization in dynamic networked systems. Awards: Best Paper Award Runners-up (ACM Mobihoc 2020) Teaching Excellence Award (School of Computing 2019) Bell Labs Best Paper Award (IEEE SDP 2015) Best Paper Awards at ICNP 2014 and IC2E 2013 Grants & Advising: Recipient of research grants supporting projects like “Testbed for Innovative Inter-Networking Research.” Advises on doctoral projects related to stream processing and network economics. His work is funded by industry collaborations and institutional grants. Labs & Teams: Part of the Distributed Network Analysis (DNA) Research Group and Advanced Networking & Systems Research Group, fostering interdisciplinary collaborations in networking and distributed systems.
Prof. Dr. Patrick Huber is a leading physicist and Institute Director at the Hamburg University of Technology (TUHH) , heading the Institute for Materials and X-Ray Physics (M-2) . He also leads the High-Resolution X-Ray Analytics of Materials group at DESY through a cooperative professorship. His research spans condensed matter physics , nanoporous materials , and X-ray analytics , with significant contributions to molecular water science and soft matter in confinement . Education: PhD in Physics (1999, Saarland University), Diploma in Physics (1995, Saarland University) Professional Career: Full Professor at TUHH (2020-present), Member of CRC 1615 (2023-present), Spokesperson for CMWS (2024-present), Cluster of Excellence BlueMat (2025) Research Interests focus on multi-scale material behavior under extreme confinement, particularly hierarchical porous silicon and silica systems . His work examines adsorption-induced deformation , elastocapillarity , fluid transport in nanopores, and metamaterial design principles using electrolytes , polymers , and liquid crystals . Fundamental studies include fluid interface thermodynamics and microscopic hydrodynamics . Scientific Awards include the Top Reviewer Award (2018) from Applied Physics Letters and the Dr.-Eduard-Martin Award (2000) for his dissertation. He contributes to 130+ publications with an h-index of 36 (2021). Advising and Grants involve supervising 18 doctoral and master's students , including Manuel Brinker , Marc Thelen , and Stella Gries . He participates in Collaborative Research Centre CRC 1615 , Cluster of Excellence EXC 3120 BlueMat , and the United Nations University Hub on Climate Engineering . Laboratory and Teams include the Institute for Materials and X-Ray Physics (M-2) at TUHH, the High-Resolution X-Ray Analytics group at DESY, and contributions to the Centre for Hybrid Nanostructures (CHyN) .
Varun Jog is Professor of Information Theory and Statistics in the Department of Pure Mathematics and Mathematical Statistics (DPMMS) at the University of Cambridge, Faculty of Mathematics. Previously, he served as Assistant Professor at the University of Wisconsin-Madison (2016-2020) and at the University of Cambridge (2021-2024). His academic background includes a B.Tech. in Electrical Engineering from IIT Bombay (2010) and a Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley (2015). Professor Jog's research centers on fundamental questions at the intersection of information theory, statistics, and machine learning. He develops theoretical frameworks for statistical inference under constraints such as limited communication and privacy requirements, with significant contributions to hypothesis testing, differential privacy, adversarial risk analysis, and information-theoretic inequalities. His work bridges abstract mathematical principles with practical applications in data science and robust machine learning. Recent publications demonstrate a concentrated focus on distributed inference systems, particularly examining sample complexity limits in hypothesis testing under information constraints and privacy-preserving mechanisms. His research consistently reveals deep connections between information theory and statistical learning, with increasing emphasis on adversarial robustness and foundational inequalities. His scientific contributions have earned recognition through prestigious awards: NSF-CAREER Award (2020) R. Narasimhan Memorial Lecture Award (2020) Eli Jury Award from UC Berkeley EECS Department (2015) Jack Keil Wolf student paper award at ISIT (2015) Professor Jog maintains an active research group, currently supervising one PhD student while having graduated four PhD students and four Master's students. His mentorship extends to postdoctoral researchers including Amir Asadi, Deepanshu Vasal, and Andre Wibisono. Research funding includes the competitive NSF-CAREER grant. He co-organizes the Cambridge Information Theory Seminar, fostering academic exchange and collaboration within the theoretical research community.
Marcia C. Linn is the Evelyn Lois Corey Professor of Instructional Science in the Berkeley School of Education at the University of California, Berkeley. She serves as Chair of the Graduate Group in Science and Mathematics Education (SESAME) and has made significant contributions to the field of science education for over five decades. Dr. Linn is a member of the National Academy of Education and a Fellow of multiple prestigious organizations including the American Association for the Advancement of Science (AAAS), the American Psychological Association (APA), the Association for Psychological Science (APS), the American Educational Research Association (AERA), and the International Society of the Learning Sciences (ISLS). Dr. Linn earned her B.A. in Psychology and Statistics (1965), M.A. in Educational Psychology (1967), and Ph.D. in Educational Psychology (1970) from Stanford University, where she worked under Lee Cronbach. Her early career included working with Jean Piaget at the Institute Jean Jacques Rousseau in Geneva, Switzerland (1967-68), serving as a Fulbright Professor at the Weizmann Institute of Science in Israel (1983), and conducting research at University College in London. She has been a fellow at the Center for Advanced Study in Behavioral Sciences three times and a Writing Resident at the Rockefeller Foundation Bellagio Center twice. Dr. Linn's research focuses on how students learn science and how technology can be used to improve science education. She developed the Knowledge Integration framework, which has become widely used in science education. Her work explores the intersection of cognitive science and educational practice, with particular attention to how students develop understanding of complex scientific concepts. She has pioneered the use of technology in science education, developing the Web-based Inquiry Science Environment (WISE) and directing the NSF-funded Technology-Enhanced Learning in Science (TELS) center. Dr. Linn's recent publications demonstrate a clear trajectory toward integrating artificial intelligence with science education. Her work increasingly focuses on how AI can support knowledge integration, facilitate science learning opportunities, and promote equitable educational experiences. She examines how technology can help students develop deeper understandings of scientific concepts through inquiry-based learning while addressing issues of social justice in science education. Scientific Awards and Honors National Association for Research in Science Teaching Award for Lifelong Distinguished Contributions to Science Education American Educational Research Association Willystine Goodsell Award Council of Scientific Society Presidents first award for Excellence in Educational Research Fulbright Professor (1983) Apple Wheels for the Mind grant (1985) National Institute of Education grant (1983) Throughout her career, Dr. Linn has secured significant funding for educational research, including multiple National Science Foundation grants. She directed the NSF-funded Technology-Enhanced Learning in Science (TELS) center and has led numerous projects investigating the cognitive consequences of computer environments for learning. She has advised countless students and researchers in the field of science education, shaping the next generation of educational researchers and practitioners. Dr. Linn directs the Web-based Inquiry Science Environment (WISE) project and has been instrumental in developing technology-enhanced learning environments for science education. Her laboratory has been at the forefront of creating and testing innovative learning technologies that support students in developing deep understanding of scientific concepts through inquiry-based approaches.
Yu Sun is an assistant professor in the Department of Electrical and Computer Engineering at Johns Hopkins University with a joint appointment at the Data Science and Artificial Intelligence (DSAI) Institute. His research integrates machine learning, computer vision, optimization, and physics to advance computational imaging frameworks for reliable AI-driven imaging systems. He earned a BEng in electronics and information from Sichuan University (2015) and a PhD in computer science from Washington University in St. Louis (2022), where his dissertation received the Turner Dissertation Award. His academic journey includes a postdoctoral fellowship at Caltech's Department of Computing and Mathematical Sciences. Dr. Sun's research spans biomedical imaging, computational imaging, inverse problems, and machine learning, focusing on interpretable AI integration for next-generation imaging. His work bridges theoretical foundations with practical applications in medical and scientific imaging domains. Recent publications reveal a dominant trend in diffusion models for scientific imaging problems, including plug-and-play priors for reconstruction (NeurIPS 2024) and benchmarks for diffusion-based scientific problem-solving (ICLR 2025 Spotlight), demonstrating cross-disciplinary impact from biomedical engineering to cell biology. Key honors include: Turner Dissertation Award for doctoral contributions Rising Star Award from the Conference on Parsimony and Learning (CPAL, 2025) He serves as a consultant associate editor for the IEEE Open Journal of Signal Processing and actively participates in the IEEE Signal Processing Society’s Computational Imaging Technical Committee. His research is supported by institutional funding through the Hopkins Computational Imaging Group. The Hopkins Computational Imaging Group, which he leads, unites AI, mathematics, and data science to develop principled algorithms for imaging systems, with emphasis on biomedical applications and novel computational frameworks.
Prof. Dr. Mareike Kühne is a full-time Professor of Business Administration at the Department of Business and Management, Brandenburg University of Technology. With a unique interdisciplinary background spanning economics, business administration, philosophy, and neuroscience, she bridges affective dimensions with economic rationality to develop holistic decision-making frameworks. Doctorate in Economics, Otto von Guericke University Magdeburg (1992-1997) Fulbright Scholar, San Diego State University (1997-1998) Studies in Philosophy, Humboldt University (2007-2011) Doctoral and Master’s in Neuroscience, Berlin School of Mind and Brain (2011-2013) Her research integrates insights from psychology and neuroscience into capital market-oriented corporate reporting, corporate governance, and normative rationality frameworks. She critiques current economic models for neglecting the embodied nature of cognition, advocating for a more nuanced understanding of rationality that incorporates lived experience. Her 15 most recent works span sustainability reporting (2025), neuroeconomics (2021), M&A disputes (2017), and IFRS standard evolution (2005-2012). These publications highlight her focus on reconciling traditional accounting principles with interdisciplinary approaches to decision-making. Fulbright Scholar (1997-1998) Prof. Kühne has taught at Humboldt University, HHL Leipzig, and the Berlin School of Mind and Brain. She actively participates in professional organizations including the German Society for Philosophy, European Accounting Association, and European Financial Reporting Advisory Group. Her work emphasizes the practical impact of neuroscientific findings on economic policy and standard-setting.
Hakwan Lau is a tenured Professor of Psychology at the University of California, Los Angeles (UCLA), where he is also a member of the Brain Research Institute. Additionally, he serves as Co-Director of the IBS Center for Neuroscience Imaging Research in South Korea, leading the Systems and Intervention Neuroscience (SIN) group. His work spans consciousness, metacognition, and perceptual reality monitoring, employing advanced neuroimaging and computational techniques. Education: Born and raised in Hong Kong, Hakwan Lau pursued graduate studies in England. He was previously an Associate Professor at Columbia University in New York before moving to UCLA, where he is now tenured. From 2017-2020, he was primarily based at the University of Hong Kong. Research Interests: Lau's lab investigates why we have subjective experiences , focusing on visual awareness, spatial attention, and metacognition. They use fMRI, decoded neurofeedback (DecNef), psychophysics, and computational modeling to explore how conscious perception arises and its clinical applications, such as treating phobias and PTSD. Research Trends: His recent work emphasizes decoded neurofeedback interventions for clinical conditions like phobias and PTSD, metacognitive mechanisms underlying confidence judgments, and neural correlates of subjective experience . The research bridges theoretical neuroscience with practical therapeutic applications, often using innovative techniques like closed-loop fMRI. Scientific Awards: While specific awards are not listed in the provided text, Lau has mentored 15 former trainees who are now independent PIs , reflecting his significant impact on the field. Advising and Grants: Lau actively advises numerous PhD students and postdocs across UCLA, the University of Hong Kong, and collaborative institutions. His lab supports projects ranging from AI-driven fMRI analysis to clinical neurofeedback trials . Labs and Teams: Lau leads the Metacognition and Consciousness Lab at UCLA, now relocated to the RIKEN Center for Brain Science near Tokyo. His team includes researchers from diverse backgrounds, focusing on interdisciplinary approaches to consciousness and metacognition.
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.
Cecilia O. Alm is a Professor in the Department of Psychology within the College of Liberal Arts at Rochester Institute of Technology (RIT), where she serves as the Artificial Intelligence Program Director. She holds multiple leadership roles including Director of the Center for Human-aware AI and Director of the Computational Linguistics and Speech Processing Lab (CLaSP). Her institutional affiliations span the School of Information, Ph.D. Programs in Cognitive Science and Computing and Information Sciences, Department of Computer Science, and MS in Data Science program. Dr. Alm earned her Ph.D. from the University of Illinois at Urbana-Champaign. Her research focuses on human-centered artificial intelligence with particular emphasis on linguistic and multimodal sensing, affective computing, and natural language processing. She investigates how AI systems can better understand and respond to human communication through multimodal dialogue processing, with applications in accessibility, education, and healthcare. Her recent publications demonstrate a strong trend toward developing inclusive AI systems, particularly through projects addressing Deaf community needs (MULTICOLLAB-ASL), subtle emotion recognition (FUSE corpus), and bias mitigation in NLP. The work consistently integrates multimodal data streams (speech, gaze, gesture) to create more responsive human-AI interaction frameworks. Current research directions emphasize diversity in AI education, visual prosody in sign languages, and human-in-the-loop AI development. Dr. Alm leads several significant NSF-funded initiatives including the AWARE-AI program, IRES AI-PROWIL international research experience, and collaborative projects with Gallaudet University focused on Deaf scientist-centered AI research. She has secured over $2.5 million in external funding for her work on human-aware AI systems. She directs the CLaSP lab which provides research opportunities for PhD, MS, and undergraduate students, with graduates employed at major technology companies including Amazon, Apple, Microsoft, and Facebook. The lab focuses on real-world AI applications in accessibility, human-robot interaction, and multimodal communication systems.
Dr. Kaitlyn Zhou is an incoming Assistant Professor in the Department of Information Science at Cornell University's Bowers College of Computing and Information Science, commencing August 2026. Her research focuses on human-language model interaction dynamics, with recognition at premier NLP and HCI conferences. Education: PhD in Computer Science, Stanford University (Advised by Dan Jurafsky) B.Sc., B.Se., M.S. in Computer Science and Human Centered Design and Engineering, University of Washington Research Focus: Her work investigates how language models shape human decision-making through three pillars: (1) identifying model overconfidence risks, (2) developing context-aware evaluation frameworks, and (3) reimagining interactions for marginalized user groups. This spans NLP, HCI, and AI ethics with emphasis on trust calibration and inclusive design. Publication Trends: Recent work examines human reliance on unreliable language models (NAACL 2025 Best Paper Runner-Up), uncertainty expression failures (ACL 2024), and evaluation metric limitations. Collectively, these advance responsible AI through human-centered methodologies across 12 major publications from 2017-2025. Scientific Awards: NAACL Best Paper Runner-Up (2025) MIT EECS Rising Star (2024) Stanford Graduate Fellowship College of Engineering Dean's Medal School of Engineering Dean's Medal of Excellence (UW) Advising & Grants: Supported by Stanford Graduate Fellowship and research internships at Microsoft Research FATE (hosted by Olteanu/Blodgett) and Allen Institute for AI (hosted by Sap/Hwang/Ren). Will recruit NLP/HCI students at Cornell starting 2026. Appointed by Washington Governor to UW Board of Regents, advocating for educational equity. Research Ecosystem: Collaborates with Stanford's NLP group, Microsoft's FATE team, and Allen Institute researchers. Features in NYT/WSJ for methods impacting real-world AI deployment.
Yangruibo Ding is an incoming Assistant Professor in the Department of Computer Science at the University of California, Los Angeles (UCLA), and currently serves as a Postdoctoral Scientist at AWS Agentic AI. He has held significant research positions at Google DeepMind, Amazon AWS AI Labs, and IBM Research, establishing himself as a leading researcher in software engineering with a focus on large language models for code. His research focuses on developing large language models (LLMs) and agentic systems for software engineering. He specializes in training LLMs with advanced symbolic reasoning capabilities for debugging, testing, program analysis, and verification. His work aims to build efficient, collaborative agentic systems for complex software development and maintenance tasks, with particular emphasis on code generation, vulnerability detection, and execution-aware pre-training techniques. Dr. Ding's publication record reveals a strong trajectory toward enhancing code intelligence through comprehensive semantics reasoning and self-refinement approaches. His research spans multiple dimensions of software engineering including code completion, vulnerability detection, model evaluation, and cross-file context understanding, with applications across various programming languages and development environments. Dr. Ding has received numerous prestigious awards recognizing his contributions to the field: IBM Ph.D. Fellowship Award (2022-2024) ACM SIGSOFT Distinguished Paper Award (2023) IEEE TSE Best Paper Award Runner-up (2022) Ph.D. Service Award, Columbia CS (2025) NSF Student Travel Award for ESEC/FSE'23 (2023) ACM SIGSOFT CAPS Travel Grant (2023) NSF Travel Award for ICSE'22 (2022) As he establishes his research group at UCLA, Dr. Ding is actively seeking students with strong coding skills and experience in large language models, program analysis, verification, or security. He serves on program committees for major conferences including ICSE (2026), ASE (2024, 2025), and ESEC/FSE Artifacts Track (2023), and regularly reviews for top-tier conferences and journals in AI and software engineering. His research is conducted through collaborations with leading industry teams including AWS Agentic AI, Google DeepMind's Learning4Code team, and IBM Research's AI for Code team, creating a robust network of industry-academia partnerships that drive innovation in software engineering research.
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.
