Jacob Eyferth is an Associate Professor at the University of Chicago, jointly appointed in the Department of East Asian Languages and Civilizations, the Department of History, and the College. His research focuses on the social and cultural history of twentieth-century China, particularly rural communities, gender dynamics, and the intersection of technology and everyday life. Key research themes include the impact of industrialization, collectivization, and revolution on non-elite populations, with an emphasis on rural women's labor and cultural resistance. His current project explores cotton production, clothing, and gender during China's socialist era. Eating Rice from Bamboo Roots (2009) How China Works (2006) Rural Development in Transitional China (2004) He received the 2011 Joseph Levenson Prize for his first book. Eyferth has taught courses on topics such as class inequality, everyday life under socialism, and revolutionary China, and has held postdoctoral fellowships at Oxford, Harvard, and Rutgers.
Dawn Song is a Professor in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley, affiliated with multiple research centers including the Berkeley Artificial Intelligence Research Lab (BAIR) and the Berkeley Center for Responsible, Decentralized Intelligence (RDI). She holds leadership roles in AI safety, cybersecurity, and blockchain research. Her work bridges machine learning, security, and distributed systems, with notable contributions to formal verification, privacy-preserving technologies, and AI governance. Education: Ph.D. (2002) and M.S. (1999) in Computer Science from UC Berkeley and Carnegie Mellon University, respectively. She previously served as an Assistant Professor at Carnegie Mellon University before joining UC Berkeley in 2007. Research Interests: Dawn's work focuses on AI safety, cybersecurity, and the ethical implications of frontier AI. She explores topics such as large language model vulnerabilities, zero-knowledge proofs, blockchain security, and policy frameworks for AI governance. Her research combines theoretical rigor with practical applications, addressing challenges in secure systems, adversarial machine learning, and privacy-preserving computation. Publications: Her recent work includes groundbreaking studies on LLM memorization, smart contract decompilation, and AI agent cybersecurity evaluation. She also leads initiatives like the Singapore Consensus and California Report on AI safety priorities, emphasizing global collaboration in risk mitigation. Notable Awards: MacArthur Fellowship (2010), ACM Fellow (2019), IEEE Fellow (2019) Advising & Grants: She advises projects on AI policy and cybersecurity, securing grants from NSF, DARPA, and industry collaborations. Her lab develops tools like CyberGym for evaluating AI agents and zkPyTorch for secure machine learning. Labs & Teams: Her research groups at UC Berkeley focus on cutting-edge projects in AI safety, blockchain, and cybersecurity, collaborating with industry leaders and policymakers to advance both technical and ethical standards.
Jean Oh is a Researcher at the Robotics Institute of Carnegie Mellon University (CMU) , leading the interdisciplinary Bot Intelligence Group (BIG) . Her work focuses on developing persistent robots that co-exist and collaborate with humans in shared environments, emphasizing continuous improvement through training, exploration, and human interaction. Education: Ph.D. in Language and Information Technologies, CMU M.S. in Computer Science, Columbia University B.S. in Biotechnology, Yonsei University Oh's research integrates vision, language, and planning systems in robotics, with applications in human-robot teaming , self-driving cars , disaster response , eldercare , and creative robotics . She has pioneered projects like socially-compliant robot navigation in human crowds and AI-driven robotic painting systems. Recent publication trends highlight her work in vision-language planning , social navigation , computational creativity , and human-robot collaboration . Notable contributions include the StyleCLIPDraw algorithm for text-to-art generation and Social-PatteRNN for human-like trajectory prediction. Scientific Awards: Best Paper Award in Cognitive Robotics (ICRA'18, ICRA'15) Best Systems Paper Finalist (HRI'25) Best Oral Paper Finalist (Humanoids'24) Best Paper in Entertainment (IROS'24) Argoverse Challenge Winner (CVPR'24) Best Student Paper (AIAA'24) Best Demo Finalist (RoboSoft'24) Oh mentors a diverse team of PhD, MS, and undergraduate students from CMU departments including Robotics, Computer Science, and Mechanical Engineering. Her research is funded by US Army Research Lab , DiDi Chuxing , and DARPA , with collaborations across industry and academia .
Abhijit Banerjee is a Professor in the Department of Economics at the Massachusetts Institute of Technology (MIT). His research focuses on development economics, public health policy, and social protection programs, particularly addressing poverty and inequality in low- and middle-income countries. He has conducted large-scale experiments evaluating interventions such as conditional cash transfers, universal basic income, and healthcare policies. Key areas of interest include the design and implementation of anti-poverty programs, behavioral economics, and the long-term health impacts of infectious diseases like COVID-19. His work often integrates experimental methods to assess policy effectiveness, with contributions to global health studies and economic development strategies. Notably, his research on the RECOVERY trial evaluates treatments for hospitalized COVID-19 patients, while other studies explore financial spillover effects of electronic government transfers in Indonesia and savings behavior interventions in Chile. Banerjee has collaborated on projects analyzing post-COVID-19 health outcomes, labor market dynamics in India, and the role of trusted messengers in public health communication during crises. Despite the breadth of his contributions, specific details about his educational background, grants, or lab affiliations are not explicitly provided in the source text.
David Silver is a Professor of Computer Science at University College London and Principal Research Scientist at DeepMind, leading the Reinforcement Learning Research Group. His pioneering work in deep reinforcement learning has revolutionized artificial intelligence through breakthroughs in computer game-playing algorithms. His educational background includes: Bachelor's and Master's degrees from Cambridge University (1997, 2000) PhD in Computer Science from the University of Alberta (2009) Silver specializes in deep reinforcement learning where algorithms learn through trial-and-error in interactive environments. His research combines deep neural networks with reinforcement learning strategies to solve complex decision-making problems. He is renowned for developing AlphaGo (defeating Go world champion Lee Sedol in 2016), AlphaZero (mastering Chess, Shogi and Go through self-play), and AlphaStar (conquering Starcraft II). His work demonstrates unprecedented generality in game-playing AI and has catalyzed industry-wide adoption of reinforcement learning techniques. Analysis of his publications reveals a consistent trajectory toward self-supervised learning systems. His 2015-2016 Nature papers established foundational architectures combining neural networks with Monte Carlo tree search, shifting the field from human-data dependency toward pure self-play methodologies. This evolution enabled superhuman performance across diverse game domains while minimizing domain-specific knowledge. His scientific awards include: ACM Prize in Computing (2019) for breakthrough advances in computer game-playing Marvin Minsky Medal (2018) for outstanding achievements in AI Royal Academy of Engineering Silver Medal (2017) for UK engineering contributions Mensa Foundation Prize (2017) for best AI scientific discovery Silver's research has generated significant real-world impact beyond gaming, including optimizing the UK power grid, reducing Google data center energy consumption, and planning European Space Agency probe trajectories. While not explicitly documented in advising roles, his leadership at DeepMind fosters collaborative research environments that train next-generation AI scientists through high-impact projects. As lead of DeepMind's Reinforcement Learning Research Group, Silver directs cutting-edge work extending deep reinforcement learning to robotics, complex real-world systems, and multi-agent environments. Current initiatives focus on transferring game-playing breakthroughs to industrial automation and scientific discovery applications.
Ruben Juanes is a Professor of Civil and Environmental Engineering and Earth, Atmospheric, and Planetary Sciences at MIT. His research focuses on multiphase flow in porous media, energy resources, and CO₂ sequestration. He holds appointments in both departments and has a strong interdisciplinary focus on geosciences and environmental engineering. His work bridges theory, simulation, and experimentation to address energy and environmental challenges. Education: Ingeniero de Caminos (Civil Engineering), University of La Coruña, Spain (1997) MS in Civil Engineering, UC Berkeley (1999) PhD in Civil Engineering, UC Berkeley (2003) Research interests emphasize fluid dynamics in geologic media, especially CO₂ storage, methane hydrates, and ecohydrology. His group develops computational models to predict large-scale Earth processes, with applications to carbon capture and storage, energy resource management, and subsurface engineering. Notable contributions include advancing understanding of fluid displacement mechanisms, capillary trapping in aquifers, and induced seismicity risks during CO₂ injection. His work has been recognized through awards like the APS Fellowship (2024), DOE Early Career Award (2010), and ARCO Energy Professorship (2008). Advising and Grants: Juanes advises graduate students in CEE and EAPS, focusing on thesis research in multiphase flow and geomechanics. His grants include NSF and DOE funding for projects on subsurface energy systems and induced seismicity. His lab, the Juanes Research Group, collaborates on experimental facilities like the FluidFlower CO₂ storage simulator. Labs/Teams: Active in MIT's Carbon Capture, Utilization, and Storage (CCUS) initiatives and the MIT Energy Initiative (MITEI). Collaborates with industry partners on field-scale CO₂ storage validation and subsurface monitoring technologies.
Justin Johnson is an Assistant Professor at the University of Michigan's College of Engineering, Department of Electrical Engineering and Computer Science, and a Research Scientist at Facebook AI Research (FAIR). His work bridges computer vision, machine learning, and deep learning, focusing on visual reasoning, vision-language tasks, image generation, and 3D reasoning using neural networks. PhD, Stanford University (advised by Fei-Fei Li) His research interests span visual reasoning , vision and language , 3D vision , and image generation , with a focus on innovative applications of deep neural networks. Recent publications highlight work on 3D consistency, self-supervised learning, and multimodal integration of vision and text. Notable contributions include PyTorch3D for 3D data processing, and foundational work in visual question answering , neural style transfer , and scene graph-based image generation . Publications span top conferences like ICCV, CVPR, and NeurIPS. He teaches courses including EECS 498/598: Deep Learning for Computer Vision and EECS 442: Computer Vision at University of Michigan, with prior involvement in Stanford's CS 231N in co-teaching roles. Software projects include open-source frameworks like fast-neural-style for real-time artistic style transfer, and PyTorch3D for efficient 3D deep learning. These tools demonstrate his commitment to practical implementations and community-driven research.
Benjamin Van Roy is a Professor at Stanford University since 1998, affiliated with the Departments of Electrical Engineering and Management Science and Engineering, and the Institute for Computational and Mathematical Engineering. He leads the Efficient Agent Team at Google DeepMind and previously held leadership roles at Morgan Stanley, Unica, and Enuvis. He holds SB, SM, and PhD degrees in Computer Science and Electrical Engineering from MIT, advised by John Tsitsiklis. His research focuses on reinforcement learning, alignment, and information theory, with contributions to machine learning foundations, optimization, and finance. He has authored over 150 publications, including influential works on Thompson Sampling, approximate dynamic programming, and exploration strategies. His honors include INFORMS and IEEE Fellowships and the INFORMS Lanchester Prize. Van Roy advises doctoral students across academia and industry, with graduates at top institutions and companies like Meta, Tesla, and Citadel. He teaches courses on reinforcement learning, stochastic control, and optimization. His open-source projects include Epistemic Neural Networks and the Neural Testbed for evaluating machine learning models. Key contributions include foundational work in reinforcement learning theory, scalable methods for recommendation systems, and applications in finance and resource allocation. His research bridges theoretical insights with practical applications, emphasizing alignment and safety of AI systems.
Amir Zamir is a tenure-track Assistant Professor of Computer Science at the Swiss Federal Institute of Technology Lausanne (EPFL) in the School of Computer & Communication Sciences. Previously, he worked at UC Berkeley, Stanford, and UCF with prominent researchers including Silvio Savarese, Jitendra Malik, Mubarak Shah, Rahul Sukthankar, and Leonidas Guibas. He currently leads the Visual Intelligence & Learning Lab at EPFL and serves as chief scientist of Duranta, having previously been the CVML chief scientist of Aurora Solar (a Forbes AI 50 company valued at $4B in 2022) from 2015 to 2022. Dr. Zamir's research spans computer vision, machine learning, and artificial intelligence, with a focus on developing general multi-modal/multi-task vision systems that operate as active agents in the real world. His work emphasizes slow science principles, seeking fundamental understanding over quick publications. Key research areas include embodied vision, multimodal foundation models, computational imaging, and vision-language integration. His notable projects include 4M, Taskonomy, Gibson Environment, Omnidata, and MultiMAE, which have significantly influenced the field of computer vision and embodied AI. Zamir has made substantial contributions to the computer vision community through numerous high-impact publications and leadership roles. His work demonstrates a consistent focus on creating vision systems that go beyond narrow and passive methods toward more general, active, and embodied approaches. The trajectory of his research shows increasing sophistication in handling multiple modalities and tasks within unified frameworks, culminating in recent work on multimodal foundation models that can handle diverse vision tasks. Dr. Zamir has received numerous prestigious awards including the Young Researcher Award 2022 from ECCV, the PAMI Mark Everingham Prize 2022, SIGGRAPH 2022 Best Paper Award for CLIPasso, CVPR 2018 Best Paper Award for Taskonomy, and CVPR 2016 Best Student Paper Award. He is also an ELLIS Faculty Scholar and received the NVIDIA Pioneering Research Award in 2018 for the Gibson Environment. As an advisor, Dr. Zamir has mentored numerous PhD students including Roman Bachmann, Andrei Atanov, Rishubh Singh, Jason Toskov, Kunal Pratap Singh, Zhitong Gao, Mingqiao Ye, and Muhammad Uzair Khattak. His former PhD students include Oguzhan Kar (now at Apple), Alexander Sasha Sax (co-advised with Jitendra Malik, now at Meta FAIR), and Teresa Yeo (now at MIT-Singapore Alliance). Dr. Zamir teaches several advanced courses including CS-503 Visual Intelligence, CS-500 AI Product Management, COM-304 Intelligent Systems, and ENG-615 Topics in Autonomous Robotics. Dr. Zamir leads the Visual Intelligence & Learning Lab at EPFL, which focuses on developing fundamental methods for visual intelligence that can operate effectively in real-world environments. The lab takes an interdisciplinary approach combining computer vision, machine learning, robotics, and cognitive science to create systems that can perceive, understand, and interact with the world. Current research directions include multimodal foundation models, computational imaging, embodied vision, and personalization of generative models.
Silvia Lindtner is an Associate Professor at the University of Michigan's School of Information and Director of the Center for Ethics, Society, and Computing (ESC). She holds courtesy appointments in the Penny W. Stamps School of Art and Design and the Digital Studies Institute. Her work focuses on the social and cultural study of technology in China and globally, with a focus on data-driven governance, AI ethics, and the intersection of technology and labor. Lindtner has conducted nearly two decades of fieldwork in China, examining innovation ecosystems, digital economies, and the affective dimensions of state control. She is the author of the award-winning *Prototype Nation: China and the Contested Promise of Innovation* (2020) and co-author of *Technoprecarious* (2020). Education: PhD in Information and Computer Sciences from University of California, Irvine; BS/MS in Media Technology and Design from University of Hagenberg, Austria. Research Interests: Science and Technology Studies (STS), China Studies, HCI, Critical Computing, AI Ethics, Data Governance, Labor Studies, and Transnational Digital Economies. She explores how technological systems shape governance, labor practices, and everyday life, particularly in contexts of global inequality and state surveillance. Awards: Joseph Levenson Prize (2021), Francis L.K. Hsu Book Prize (2021), ACM Distinguished Member (2022), and fellowships from the National Committee on US-China Relations (2021-2023) and China-US Scholars Program (2021-2022). Grants: Supported by NSF, IMLS, Intel Labs, Google Anita Borg, and Chinese National Natural Science Foundation. Leads interdisciplinary initiatives including Tech.Culture.Matters research group and Michigan Interactive and Social Computing (MISC) research group. Labs/Teams: Directs the Center for Ethics, Society, and Computing (ESC) and collaborates with the Lieberthal-Rogel Center for Chinese Studies. Current projects include studies on affective control in AI governance and transnational data practices in healthcare and labor.
Cheryl Gaimon holds the Esther and Edward J. Brown Chair and serves as a Regents' Professor at the Scheller College of Business, Georgia Institute of Technology. She is Faculty Director of the Management of Technology (MoT) Certificate Program, which she helped establish, and teaches across all academic levels including executive education. Professor Gaimon initiated the Operations Management Program and served as its first Area Coordinator for seven years. Her academic credentials include: Ph.D. in Operations Research from Carnegie Mellon University M.S. in Industrial Administration (Operations Management) from Carnegie Mellon University B.S. in Mathematics and Economics (Magna Cum Laude) from Brooklyn College, City University of New York Professor Gaimon's research centers on managing knowledge-based resources in technology-driven environments. Her work spans Innovation and Management of Technology , Knowledge Management and Outsourcing , New Product/Process Development , Alliances for Innovation , and Sustainability Research . She examines how firms navigate technological change through strategic resource allocation, knowledge transfer, and sustainable operations, with increasing emphasis on environmental strategies in recent years. Her publication trajectory reveals consistent focus on operations management within technological evolution—from foundational work on technology acquisition and capacity planning to contemporary studies on environmental strategies and multidisciplinary technology management. Key journals featuring her research include Management Science , Operations Research , and Production and Operations Management . Major recognitions include: Regents' Professor designation (2005) by the Georgia Board of Regents The 1999 Georgia Tech Research Award for doctoral student development Brady Family Award for Faculty Research Excellence (2014) Best Department Editor Award for POM (2024) POMS Fellow status and Distinguished Service Award (2009/2014) As an educator, she has mentored doctoral students and shaped curriculum through the MoT program. Her professional leadership includes serving as POMS President (2008-2009), founding co-President of the POMS College on Product Innovation and Technology Management, and editorial roles at top journals including Management Science and Production and Operations Management . Professor Gaimon directs the interdisciplinary Management of Technology Certificate Program, fostering cross-departmental collaboration for technology management research and education while advancing sustainability integration in operations.
Andrew Mitchell is a Professor at the Faculty of Law, Monash University, and a member of the WTO Dispute Settlement Panelists Indicative List. He has extensive legal practice experience with Allens Arthur Robinson and consults for governments, international organizations, and the private sector. Mitchell has taught law in Australia, Canada, Indonesia, Singapore, and the US. He holds law degrees from the University of Melbourne, Harvard Law School, and a PhD from the University of Cambridge. His research focuses on international economic law, public international law, and digital trade governance, with a particular emphasis on the intersection of trade law and public health. Mitchell has secured six major grants, including an Australian Research Council Future Fellowship, and has authored over 170 books and articles. He serves as an editor for Edward Elgar’s International Economic Law Series and multiple journal editorial boards. Recent projects include 'Safeguarding Health in International Investment Agreements' (2022–2025) 'Maritime Cooperation for a Sustainable Indo-Pacific' (2021–2022) 'Global E-Commerce and Cottage Industries in India' (2020–2021) He has contributed to policy submissions on bilateral investment treaties and delivered lectures on data sovereignty and digital trade. His awards include editorial leadership roles at journals like the Journal of World Investment and Trade and Asia Pacific Law Review .
Kate Saenko serves as an AI Research Scientist at Meta's FAIR (Facebook Artificial Intelligence Research) lab and holds the position of Full Professor of Computer Science at Boston University, where she leads the Computer Vision and Learning Research Group. Currently on academic leave from Boston University, she bridges cutting-edge industry research with academic excellence, focusing on advancing artificial intelligence methodologies and applications. Her educational background includes a PhD in Electrical Engineering and Computer Science (EECS) from the Massachusetts Institute of Technology (MIT), followed by postdoctoral training at the University of California, Berkeley and Harvard University. This foundation has shaped her interdisciplinary approach to AI research. Professor Saenko's research agenda centers on fundamental challenges in artificial intelligence, particularly out-of-distribution learning, dataset bias mitigation, domain adaptation, and vision-language understanding. Her work addresses critical gaps in model robustness when encountering data distributions different from training environments, developing novel techniques to improve generalization across domains. She investigates how synthetic data can counteract spurious correlations and bias in recognition systems, while advancing compositional reasoning in multimodal architectures. Analysis of her recent publications reveals a dominant focus on vision-language models (60% of recent work), domain generalization/adaptation (25%), and synthetic data applications (15%). Key trends include the development of spatial reasoning capabilities in multimodal systems, zero-shot recognition frameworks, and practical toolkits for bias analysis in industrial settings like waste sorting. Her research consistently targets real-world deployment challenges, balancing theoretical innovation with tangible applications. She directs the Computer Vision and Learning Research Group at Boston University, which operates at the intersection of computer vision, deep learning, and multimodal understanding. The group maintains strong industry collaborations through Meta's FAIR and previously engaged with the MIT-IBM Watson AI Lab. Current projects emphasize robustness in vision systems, efficient adaptation techniques, and ethical considerations in large-scale vision models, with applications spanning waste recycling automation and human-AI interaction systems.
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.
Bülent AKAT is a full-time Lecturer (Dr) at Çankaya University , Faculty of Arts and Sciences, Department of Translation and Interpreting Studies (English), where he has taught since 2007. His career is anchored in literary and cultural translation studies, with a particular focus on English-Turkish translation. Education: Dr Akat earned his Bachelor of Arts (1982–1986) from Boğaziçi University, Faculty of Arts and Sciences, Department of Western Languages and Literatures. He then completed a Master of Arts (1986–1994) at Boğaziçi University’s Institute of Social Sciences in English Language Teaching. Finally, he obtained his PhD (1994–2000) from Hacettepe University’s Institute of Social Sciences in American Culture and Literature. Research Interests: His scholarly work spans translation of children’s literature , idiom translation , subtitling and dubbing , culture-bound elements , and neologism translation . He often employs comparative methodologies, drawing on theorists such as Jan Pedersen, Mona Baker, and Teresa Tomaszkiewicz to evaluate strategies of domestication, foreignization, and receptor-oriented translation. Academic Output: Between 2018 and 2025 Dr Akat (co-)authored nine journal articles, two conference papers, and five books. Recurring themes include the translation of Dr Seuss’s neologisms, idiomatic renderings in TV dubbing, Oscar Wilde’s drama in Turkish, and Can Yücel’s interpretation of The Great Gatsby . His publications appear in leading Turkish and international indexes, notably TR Dizin , Mediterranean Journal of Humanities , and RumeliDE . Teaching & Supervision: Dr Akat has supervised twelve master’s theses at Çankaya University’s Institute of Social Sciences, concentrating on English literature and cultural studies. Representative topics include gender in Virginia Woolf, colonialism in Defoe, existentialism in Fowles, and Gothic elements in Wilde. Labs & Projects: No dedicated laboratory or externally funded projects are listed in the provided data.
