جست‌وجوی استاد و پژوهشگر

Yunxiang Yanمشاهده پروفایل

استاد

• Economic Anthropology
• Social Change and Development
• Family and Kinship
• +۵ مورد دیگر

Yunxiang Yan is a Professor in the Department of Anthropology at the University of California, Los Angeles (UCLA). His work focuses on China and Cultural Anthropology , with specific expertise in Economic Anthropology , Family and Kinship , and Individualization . Education: Ph.D. in Social Anthropology from Harvard University (1993). Yan’s research explores the intersection of social change , moral theory , and family dynamics in modern China. His ethnographic studies in rural villages provide foundational insights into exchange theory and cultural globalization . Recent publications analyze food safety , medical ethics , and moral crises in China, reflecting his interdisciplinary approach combining anthropology, sociology, and philosophy. Articles span themes like intergenerational intimacy , political oppression , and networked social structures . Scientific Awards: Guggenheim Fellowship Levenson Prize-winning Research Yan’s work bridges local Chinese studies with global anthropological discourse. He has held positions at the Chinese University of Hong Kong, Johns Hopkins University, and UCLA since 1996. His career reflects a commitment to documenting the lives of ordinary people, rooted in his early experiences as an expelled villager during Maoist China. Contact: yan@anthro.ucla.edu

Yarin Galمشاهده پروفایل

دانشیار

• Bayesian Deep Learning
• Machine Learning
• AI Safety
• +۴ مورد دیگر

Yarin Gal is an Associate Professor of Machine Learning at the University of Oxford's Department of Computer Science and a Tutorial Fellow at Christ Church College. He is also a Turing AI Fellow at the Alan Turing Institute and Director of Research at the UK Government’s AI Safety Institute. He leads the Oxford Applied and Theoretical Machine Learning (OATML) Research Group, focusing on Bayesian deep learning, AI safety, and uncertainty quantification. Education PhD in Uncertainty in Deep Learning (2016) Research Interests His research integrates Bayesian methods with deep learning to address challenges in AI safety , uncertainty quantification , and robustness . Key areas include: Bayesian neural networks and approximate inference Uncertainty estimation in deep learning AI safety and interpretability Applications in autonomous driving, medical imaging, and NLP Publications & Trends His recent work spans Bayesian optimization , adversarial robustness , and continual learning . Notable contributions include Targeted Dropout for model pruning, Uncertainty in Autonomous Driving , and theoretical studies on adversarial examples in Bayesian networks. Awards & Honors Turing AI Fellow Teaching & Supervision He has taught Advanced Machine Learning , Uncertainty in Deep Learning , and contributed to NASA's Frontier Development Lab. His current students include Kelsey Doerksen, Gunshi Gupta, and Shreshth Malik. Labs & Teams He leads the OATML Group , a multidisciplinary team advancing theoretical and applied machine learning, with a focus on safety and interpretability in AI systems.

Jimmy Baمشاهده پروفایل

استادیار

• Deep Learning
• Reinforcement Learning
• Neural Networks
• +۲ مورد دیگر

Jimmy Ba is an Assistant Professor in the Department of Computer Science at the University of Toronto and a CIFAR AI Chair. His research develops efficient learning algorithms for deep neural networks, with applications in reinforcement learning and AI. He completed his PhD under Geoffrey Hinton and holds multiple fellowships including the Facebook Graduate Fellowship. Research Focus: Neural network efficiency, reinforcement learning architectures, and optimization methods for deep learning systems. Teaching: Courses on Neural Networks, Deep Learning, and Inference Algorithms at University of Toronto. Awards: Facebook Graduate Fellowship (2016-2018) Massey College Junior Fellowship

Joseph A. Novemberمشاهده پروفایل

دانشیار

• History of Science and Technology
• Biomedical Computing
• History of Medicine
• +۳ مورد دیگر

Joseph A. November is an Associate Professor in the Department of History at the University of South Carolina, affiliated with the McCausland College of Arts and Sciences. His research focuses on the history of biomedical computing, distributed computing, and the intersection of technology and medicine. He holds a Ph.D. from Princeton University (2006), an M.A. from the University of Chicago (2002), and a B.A. from Hamilton College (1997). His work includes the award-winning book Biomedical Computing: Digitizing Life in the United States (2012), which explores the co-development of biomedicine and computing technologies. Current projects include Revolutions@home , examining distributed computing in protein folding research, and a biography of computing pioneer Robert S. Ledley. He has received grants from the NSF, NIH, and the Charles Babbage Institute. Teaching interests span the history of science and technology, including courses on the history of medicine, digital humanities, and the role of games in historical education. He actively contributes to professional organizations like SHOT and the History of Science Society. Awards include the Computer History Museum Prize (2013) and the National Institutes of Health DeWitt Stetten Fellowship (2007-2008). His research bridges historical analysis with contemporary issues in technology and biomedical ethics.

• Machine Learning
• Probability
• Mathematical Physics
• +۶ مورد دیگر

Boris Hanin is an Associate Professor at Princeton University's Department of Operations Research and Financial Engineering (ORFE) and Associated Faculty at the Program in Applied and Computational Mathematics (PACM). Prior to Princeton, he held academic positions at Texas A&M University (Assistant Professor of Mathematics), MIT (NSF Postdoctoral Fellow), and Northwestern University (PhD in Mathematics under Steve Zelditch). He works part-time at Foundry, an AI/computing startup, leading the Foundry Institute. PhD in Mathematics, Northwestern University NSF Postdoctoral Fellowship, MIT Mathematics Associate Professor, Princeton ORFE Part-Time Leader, Foundry Institute His research spans machine learning, probability theory, and mathematical physics, focusing on neural network theory (approximation power, optimization guarantees), random matrix theory, and spectral asymptotics. He has made foundational contributions to understanding gradient behavior, initialization, and infinite-width limits in deep learning. Boris has supervised numerous PhD students and postdocs, with former members securing prestigious positions at Harvard, MIT, and Huawei. He serves as Associate Editor for journals like Pure and Applied Analysis and Mathematics of Operations Research , and has taught short courses at Oxford, Luxembourg, and Tor Vergata on statistical physics of neural networks and deep learning theory. 2024 Sloan Fellowship in Mathematics NSF CAREER grant DMS-2143754 NSF grant DMS-2133806 His research group collaborates on topics including hyperparameter transfer, architecture-aware scaling, and spectral analysis of random waves and neural networks. He actively contributes to theoretical machine learning through foundational publications in venues like Probability Theory and Related Fields , Journal of Machine Learning Research , and Communications in Mathematical Physics .

Marlyse Baptistaمشاهده پروفایل

استاد

• Language Contact
• Cognitive Linguistics
• Pidgins and Creoles
• +۴ مورد دیگر

Marlyse Baptista is the President's Distinguished Professor of Linguistics at the University of Pennsylvania, Department of Linguistics. She is affiliated with the School of Arts & Sciences and MindCore initiative. Her research focuses on language contact, creolization processes, bilingualism, and experimental methods in creole studies. Baptista leads the Language Contact and Cognition Lab, previously known as the Cognition, Convergence and Language Emergence (CCLE) group at the University of Michigan. Education includes a PhD in Linguistics (Harvard, 1997), MA degrees from Harvard and the Université de Bordeaux III, and extensive training in multilingual education. Her work bridges generative syntax with experimental approaches, including artificial language learning to study language convergence. Key themes include the cognitive underpinnings of creole formation, bidirectional influences in Cape Verdean Creole, and the role of congruence in language acquisition. Research highlights include studies on Cape Verdean Creole's grammatical properties, genetic-linguistic admixture correlations, and pedagogical resources for creole language education. Recent projects explore experimental validation of creole genesis theories through multilingual acquisition studies. Scientific Awards: President's Distinguished Professorship (University of Pennsylvania) Labs/Teams: Language Contact and Cognition Lab, MindCore Grants/Projects: MULTI Project (creole language educational resources), NSF-funded studies on creole genesis mechanisms Baptista's work emphasizes interdisciplinary approaches, integrating syntax theory, psycholinguistics, and sociolinguistics to address foundational questions in contact linguistics and creolistics.

• Deep Reinforcement Learning
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

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.

• Robotics
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Prof. Ingmar Posner is a leading figure in applied artificial intelligence at the University of Oxford, where he serves as Principal Investigator for the Applied Artificial Intelligence Lab (A2I) and founding Director of the Oxford Robotics Institute. His work focuses on enabling robots to operate effectively in complex real-world environments through experience-driven learning. Key research areas: robot learning, scene interpretation, data-efficient learning, and transfer learning Applications in manipulation, autonomous driving, logistics, and space exploration His team has produced groundbreaking work in world models, sim-to-real transfer, and constraint-based manipulation systems (e.g., COMBO-Grasp). Notable contributions include the TWIST distillation framework and foundational research in tactile data generation (TactGen). He has received multiple best paper awards at top robotics venues. Publications reveal evolving research themes: 2025 work emphasizes language-conditioned learning (Lumos) and multi-agent decision-making, while 2024 focused on diffusion models for locomotion and differentiable simulators. Earlier work spans from urban scene analysis to physically plausible scene synthesis (RELATE).

Stephen Lichtمشاهده پروفایل

دانشیار

• Marine Robotics
• Biologically Inspired Propulsion
• Nonlinear Attitude Control
• +۵ مورد دیگر

Stephen Licht is an Associate Professor of Ocean Engineering and Graduate Director at the University of Rhode Island's College of Engineering, where he directs the Robotics Laboratory for Complex Underwater Environments (R-CUE). His research focuses on developing maritime robots capable of operating in dynamic and unpredictable environments through biologically inspired propulsion, distributed pressure sensing, model-based optimal control, and compliant underwater manipulation technologies. Ph.D. in Oceanographic and Mechanical Engineering from MIT/WHOI Joint Program (2008) B.S. in Mechanical Engineering from Yale University (1998) Former Senior Research Scientist at iRobot and Senior Robotics Engineer at Vecna Robotics Current Research Affiliate with MIT Department of Mechanical Engineering Former Visiting Faculty at Libera Università di Bolzano (2019-2020) Dr. Licht's research spans marine robotics with emphasis on biologically inspired propulsion systems that provide high authority and bandwidth thrust, nonlinear attitude control for maneuvering in dynamic conditions, compliant underwater manipulation technologies, and unmanned aerial monitoring of coastal structures. His work bridges mechanical engineering principles with oceanographic applications to create more capable underwater robotic systems that can operate in complex marine environments. His recent publications demonstrate a strong trend toward soft robotics applications for deep-sea exploration, with particular focus on jamming grippers and neutrally buoyant manipulation systems. The research also shows increasing integration of additive manufacturing techniques for field-deployable solutions and computational methods for autonomous systems operating in challenging marine environments. His work spans fundamental control theory, mechanical design, and practical field applications. Dr. Licht has secured significant research funding as both Principal Investigator and Co-Principal Investigator from major organizations including the Office of Naval Research, NOAA, NSF, and various university collaborations. His grants focus on advancing unmanned underwater vehicle technology, soft robotics for deep-sea applications, and coastal monitoring systems. Active mentor to numerous graduate and undergraduate students in Ocean Engineering Successful track record of student placements at organizations including Jaia Robotics, Scripps Institution of Oceanography, FORSSEA Robotics, and government research labs Collaborates with researchers at MIT, WHOI, University of Connecticut, University of Maine, and international institutions Licht leads the R-CUE lab which develops innovative solutions for underwater robotics challenges, with particular expertise in biomimetic propulsion, soft robotics for deep-sea applications, and autonomous systems for environmental monitoring. The lab maintains strong industry connections with OceanGate Inc. and FabNewport, and engages with local educational institutions through outreach programs with Roger Williams Middle School.

Thomas Hofmannمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Natural Language Understanding
• +۳ مورد دیگر

Prof. Dr. Thomas Hofmann is a Full Professor and Head of the Department of Computer Science at ETH Zurich since 2014. He also leads the Institute for Machine Learning. His research focuses on machine learning, deep learning, natural language understanding, and text understanding. Hofmann holds a Ph.D. from the University of Bonn (1997) and has held academic positions at Brown University (1999–2004) and TU Darmstadt. He transitioned to industry as Director of Engineering at Google (2006–2014), leading the Zurich R&D center, before returning to academia. He co-founded Recommind (2000) and 1plusX (Swiss marketing tech company), currently serving as Chief Scientist and board member at 1plusX. Education: Ph.D. in Computer Science, University of Bonn (1997) Postdoctoral Work: MIT (CBCL/AI Lab), UC Berkeley (EECS/ICSI) His research explores advanced machine learning techniques, including diffusion models, generative adversarial networks, and optimization dynamics. Hofmann’s entrepreneurial ventures reflect his focus on applying AI to real-world challenges, such as e-discovery and marketing technology. His work spans theoretical contributions (e.g., neural network training dynamics, continual learning) and applied innovations (e.g., image editing, portrait generation). Hofmann actively bridges academia and industry, influencing both research and commercial AI applications.

Kevin Jamiesonمشاهده پروفایل

دانشیار

• Machine Learning
• Active Learning
• Multi-Armed Bandits
• +۴ مورد دیگر

Kevin Jamieson is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering and an Adjunct Professor in the Department of Statistics at the University of Washington . His academic journey includes a B.S. (2009) , M.S. (2010) , and Ph.D. (2015) in electrical engineering from the University of Washington, Columbia University, and University of Wisconsin–Madison respectively. He completed a postdoc at UC Berkeley's AMP Lab before joining UW in 2017. Ph.D., Electrical Engineering, University of Wisconsin–Madison (2015) M.S., Electrical Engineering, Columbia University (2010) B.S., Electrical Engineering, University of Washington (2009) Jamieson's research lies at the intersection of interactive machine learning , active learning , and sequential decision making . His work focuses on: Adaptive sampling strategies in multi-armed bandits and reinforcement learning (RL) Developing instance-dependent optimal algorithms that adapt to problem difficulty Applications in robotics , human perception studies , and hyperparameter optimization Representation learning for large models and experimental design frameworks His 15 most recent publications (2025-2022) demonstrate expertise in bandit theory , contextual RL , and game-theoretic learning . Notable trends include sample-efficient optimization , adaptive A/B testing , and sim-to-real transfer in robotics. Jamieson has received: NSF CAREER award for foundational contributions Amazon Faculty Research award for innovation in learning systems He actively recruits graduate students and postdocs , emphasizing collaboration in areas like: Multi-agent RL and strategic actor learning Empirical process suprema and adaptive sampling theory Applications in robotics , large language model finetuning , and biomedical data analysis Jamieson leads the Washington AI Lab (WAIL) and develops open-source learning systems like the NEXT framework for real-world adaptive data collection. He serves as co-PI for the Institute for the Foundations of Data Science (IFDS) and co-organizes the Distinguished Seminar in Optimization & Data .

Ahmet Atayمشاهده پروفایل

استاد

• Transnational communication
• Digital media studies
• Cultural identity and globalization
• +۵ مورد دیگر

Prof. Ahmet Atay holds the rank of Professor of Communication Studies at the College of Wooster, serving as Program Chair of Global Media & Digital Studies and liaison to the Digital Visual Storytelling Pathway. His academic journey includes degrees from Marmara University (B.A., 1998), Ohio University (M.A., 2001), University of Northern Iowa (M.A., 2003), and Southern Illinois University (Ph.D., 2009). His research focuses on transnational communication, digital media's societal impact, cultural identity formation in global contexts, and queer diasporic experiences. He actively explores media representation of gender/sexuality, critical pedagogy, and autoethnography as research methodologies. Notable projects include editing works on millennial media ecology, intercultural communication pedagogy, and transnational queer cinema. Teaching responsibilities include courses on intercultural communication, mediated gender studies, and digital storytelling for social justice. He has held leadership roles in professional organizations like the National Communication Association and Central States Communication Association. Prof. Atay's recent publications emphasize digital ethnography, decolonizing communication studies, and queer pedagogy innovations. His work bridges theoretical frameworks with practical educational applications, addressing contemporary issues like diversity in academia and media's role in shaping cultural narratives.

• Reinforcement Learning
• Multi-Agent Systems
• Deep Learning
• +۲ مورد دیگر

Shimon Whiteson is Professor of Computer Science at the University of Oxford, leading the Whiteson Research Lab focused on reinforcement learning, multi-agent systems, and deep learning. His research develops algorithms for efficient learning in complex environments. Current work explores meta-reinforcement learning frameworks that enable agents to rapidly adapt to new tasks, with applications in autonomous driving simulation and robotics. Recent innovations include novel methods for offline reinforcement learning, multi-agent coordination, and morphology-aware control. Publications demonstrate advances in: Meta-RL algorithm design for few-shot adaptation Multi-agent reinforcement learning environments and benchmarks Imitation learning in autonomous driving Bayesian methods for sample-efficient learning Research outputs include widely used benchmarks and tools including JaxMARL for accelerated multi-agent RL research. Current doctoral supervision focuses on temporal abstraction in RL, multi-agent coordination, and reinforcement learning theory.

Jason D. Leeمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۷ مورد دیگر

Jason D. Lee is an associate professor of Electrical Engineering and Computer Sciences (EECS) and Statistics at the University of California, Berkeley. Previously, he held academic positions at Princeton University as an associate professor, and was a research scientist at Google DeepMind. He completed his Ph.D. in Computational and Mathematical Engineering at Stanford University under the advisement of Trevor Hastie and Jonathan Taylor. For students and collaborators, his primary contact email is jasonlee@princeton.edu, though prospective students and postdocs are asked to include "filter_student" in the subject line. Ph.D., Computational and Mathematical Engineering, Stanford University (2015) B.Sc., Mathematics, Duke University (2010) Lee's research lies at the intersection of machine learning, statistics, and optimization, focusing on the theoretical foundations of artificial intelligence. His work addresses fundamental questions in deep learning, including optimization landscapes, representation learning, and reinforcement learning theory. He has made significant contributions to understanding how gradient descent operates in neural network training and has developed provably efficient algorithms for various learning scenarios. His ten most recent publications represent a diverse yet coherent body of work across machine learning theory, focusing on topics such as Gaussian multi-index models, transformer learning capabilities, shallow neural networks, and optimization techniques. These publications appear in top venues including COLT, ICML, NeurIPS, and JMLR. Among his notable accolades are: Samsung AI Researcher of the Year Award (2023) NSF Career Award (2022) ONR Young Investigator Award (2021) Sloan Research Fellow in Computer Science (2019) NIPS Best Student Paper Award (2016) Princeton Commendation for Outstanding Teaching (ECE538B) Lee has advised numerous students including Alex Damian, Wenhao Zhan, Eshaan Nichani, Tianle Cai, Zixuan Wang, and Yunwei Ren. Former advisees have gone on to positions at institutions like NYU Courant, Facebook AI Research, UW, MIT, Duke, and Microsoft Research. His research group and collaborators span multiple institutions, working on theoretical and applied aspects of machine learning and artificial intelligence, with a particular focus on the optimization and learning dynamics of neural networks and transformer models.

• Machine Learning
• Statistics
• Theoretical Machine Learning
• +۷ مورد دیگر

Fanny Yang is an Assistant Professor in the Computer Science Department at ETH Zurich . She previously held postdoctoral positions at Stanford University and a Junior Fellowship at the Institute for Theoretical Studies at ETH Zurich, advised by Nicolai Meinshausen . Her PhD was completed at the EECS Department of UC Berkeley , supervised by Martin Wainwright . Research Interests : Theoretical foundations of machine learning and statistics , particularly focusing on overparameterized models and high-dimensional data . Developing trustworthy ML models with emphasis on distributional robustness , domain generalization , and interpretability . Applications in medical diagnostics and treatment effect analysis , aiming to address reliability issues in real-world domains. Recent Work Trends : 2025 Articles : Theoretical analysis of semi-supervised multi-objective learning , test-time scaling with verifiers, and foundation models for efficient randomized experiments . 2024 Articles : Studies on robust mixture learning , privacy-preserving data synthesis via optimal transport , confounding quantification in causal inference , and semi-private learning frameworks. 2023 Articles : Investigations into active vs. passive learning in high dimensions, inductive bias in noisy interpolation , and semi-supervised novelty detection using model ensembles .