Google Gemini

Research scientist focused on machine learning and natural language understanding, with work spanning machine translation, semantic parsing, and large-scale language modeling.

Xinyun Chen's homepage identifies her as an AI research scientist at Meta Superintelligence Labs, previously a staff research scientist at Google DeepMind. It also lists a PhD in Computer Science from UC Berkeley and a BS in Computer Science from Shanghai Jiao Tong University.

Research Scientist at OpenAI focused on reasoning and agents. Previously at Google Brain, he worked on T5X, PaLM, Flan-PaLM, and Flan-T5, and he earned a PhD at MIT.

Brian Lester is a Senior Research Engineer at Google Brain and a PhD student at the University of Toronto advised by Colin Raffel. His public work focuses on efficient adaptation of large language models, including prompt tuning and related methods.

Luke Zettlemoyer is a professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington and a Senior Research Director at Meta FAIR. His research focuses on empirical methods for natural language semantics, machine learning for language understanding, and self-supervision for pre-training; he studied at NC State, earned his Ph.D. from MIT, and was a postdoctoral researcher at the University of Edinburgh.

Tulsee Doshi is a Senior Director of Product Management at Google DeepMind and currently leads product for Gemini Model. She previously served as Head of Product for Responsible AI at Google and holds both an M.S. and a Ph.D. in Symbolic Systems from Stanford.

Minhao Cheng is an assistant professor in the College of Information Sciences and Technology at Penn State. His homepage says he earned a PhD in computer science from UCLA under Cho-Jui Hsieh, served as an assistant professor at HKUST before joining Penn State in Spring 2024, and broadly studies machine learning with a focus on trustworthy machine learning and AutoML.

Associate professor at Polytechnique Montreal, core academic member at Mila, and CIFAR AI Chair whose research spans continual learning, reasoning, meta-learning, and reinforcement learning.

Member of Technical Staff at Anthropic working on reinforcement learning for LLMs; previously at Meta FAIR and the Kempner Institute at Harvard, with a Ph.D. from NYU and undergraduate study at Yale.

Assistant professor at Columbia University working on embodied AI, robot learning, world models, and computer vision. Previously a research scientist at Google DeepMind.

Sanjiv Kumar is a Google Fellow and vice president at Google Research. His public homepage says he leads teams working on large machine learning foundation models and generative AI, has spent more than 25 years building machine learning systems and products, and received a PhD in computer science from Carnegie Mellon University in 2005.

Member of Technical Staff at OpenAI focused on accelerating biomedical intelligence. He was previously a Research Scientist at Google DeepMind.

Scientist at OpenAI and president of the ML Collective research group, focused on understanding and controlling large neural networks. He completed a PhD at Cornell and previously helped start Uber AI and later founded Windscape AI.

Xinyi Chen is a PhD candidate in computer science at Princeton University and concurrently a research scientist at Google DeepMind. Her public homepage says she works at the intersection of machine learning, optimization, and dynamical systems, focusing on robust and efficient methods for sequential decision-making and control, and that she previously completed undergraduate studies in mathematics at Princeton.

DeepMind researcher working on machine learning, computer vision, and structured learning from video and language.

Shekoofeh Azizi is a staff research scientist and research lead at Google DeepMind. Her Google Research profile says she works on biomedical super-intelligence to accelerate scientific discovery, with a particular focus on cancer, and helps lead efforts behind Med-PaLM, Med-Gemini, TxGemma, Tx-LLM, and C2S-Scale. UBC's Centre for Internet Computing profile says she earned a bachelor's degree in electrical engineering from Sharif University of Technology and a PhD in computer science from the University of British Columbia.

Researcher at Thinking Machines, formerly at OpenAI, working on real-time multimodal systems across language, vision, and voice.

Adria Garriga-Alonso is a research scientist at Google DeepMind focused on machine learning and AI safety. His public profile notes a PhD in computer science from the University of Cambridge in 2020.

Claire Cui is a Google Fellow in the Google Brain team, where she leads research on deep generalist learning, large language models, multimodal learning, and interpretable AI. During her tenure at Google she helped found AdSense for Content and co-founded Google Health Research and Medical Brain; she holds a Ph.D. in computer science from Stanford and a B.S. in computer science from Tsinghua University.

John Canny is the Paul and Stacy Jacobs Distinguished Professor of Engineering at UC Berkeley. His Berkeley faculty homepage shows he taught CS188: Introduction to Artificial Intelligence in Spring 2025. An official Berkeley EECS news profile says he joined the Berkeley faculty in 1987 and earned bachelor's degrees in computer science and theoretical physics and electrical engineering from Adelaide University, followed by master's and PhD degrees from MIT, with contributions spanning AI, robotics, computer graphics, human-computer interaction, computer security, computational algebra, and computational geometry.

Ksenia Konyushkova is a research scientist at Google DeepMind in London working on computer vision and embodied AI. Before DeepMind, she co-founded Microverses and held postdoctoral roles at Imperial College London and UC Berkeley. She earned her PhD from EPFL under Pascal Fua.

Postdoctoral associate at MIT CSAIL working at the intersection of machine learning and healthcare. He earned a PhD in computer science from Cornell University and studies multimodal medical AI.

Research scientist at Google Research in New York City. He earned a PhD in computer science from NYU Tandon and works on language models, multimodal learning, NLP, and deep learning.

Jascha Sohl-Dickstein is a machine learning researcher known for diffusion models, learned optimizers, and work on large language model capabilities. His personal site, marked accurate as of January 2024, says he had recently joined Anthropic after roles at Google Brain and Google DeepMind.

Radu Soricut

Xinyun Chen

Hyung Won Chung

Brian Lester

Luke Zettlemoyer

Tulsee Doshi

Minhao Cheng

Sarath Chandar

David Brandfonbrener

Yunzhu Li

Sanjiv Kumar

Khaled Saab

Jason Yosinski

Xinyi Chen

Jean-Baptiste Alayrac

Shekoofeh Azizi

Rowan Zellers

Adrià Garriga-Alonso

Claire Cui

John Canny

Ksenia Konyushkova

Tiam Jaroensri

Siddhartha Brahma

Jascha Sohl-Dickstein