Google

Researcher at Anthropic working on machine learning, language models, and reinforcement learning. Previously a postdoctoral researcher at Meta FAIR, and he earned a PhD in computer science from Harvard University.

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.

Resident at xAI and long-term visitor at the Center for AI Safety. He recently completed a PhD in electrical and computer engineering at the University of Texas at Austin, and his work is grounded in mathematical and statistical machine learning plus control theory.

Assistant professor in the University of Pennsylvania Department of Computer and Information Science whose research spans natural language processing, speech, and multimodal learning; previously a research scientist at Facebook AI Research.

Sebastian Gehrmann is a staff research scientist at Mistral AI working on language generation, information extraction, and the evaluation of generative systems.

AI researcher focused on reasoning and agents who worked on GPT-4, o1, and Deep Research; previously at Google Brain and earned a PhD at MIT.

Aakanksha Chowdhery is a machine learning researcher based in New York City. She works on large-scale machine learning across pre-training, post-training, inference, and system efficiency, and is known for contributions such as PaLM, Pathways, and Gemini.

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

Brian Lester is a Senior Research Engineer at Google working on natural language processing and efficient ways to adapt large pre-trained language models. His public work includes prompt tuning, SPoT, and FLAN-related research.

Research leader at Google DeepMind and Google Research focused on natural language processing and machine learning, including large language models such as PaLM and Gemini.

Postdoctoral fellow at Johns Hopkins University and incoming assistant professor at UNC Chapel Hill whose work focuses on multilingual NLP, grounded language understanding, and societal impacts of language technologies.

Research software engineer at Google DeepMind working on machine learning for code and formal reasoning; completed a PhD at TU Darmstadt on uncertainty quantification in deep learning.

Xinyun Chen is a machine learning researcher at Meta FAIR. Her work spans large language models, code generation, mathematical reasoning, and efficient inference.

Danny Driess is a research scientist at Google DeepMind whose work focuses on general AI, robot learning, and multimodal foundation models.

Jiahui Yu is a research scientist at Google DeepMind working on multimodal learning and large language models.

Ali Payani is a Research Scientist at Google DeepMind working on natural language generation and natural language reasoning. His public research interests include reasoning, planning, and grounding with language models.

Distinguished engineer on Google's Gemini LLM team working on machine learning systems, generative AI, and data center infrastructure.

Senior Staff Software Engineer at Google working on machine intelligence, data management, and large-scale machine learning systems.

Research scientist and engineer at OpenAI; previously worked at Google DeepMind on language models, reinforcement learning, and robotics, and is known for neural network interpretability and visualization.

Teddy Karrer is a research scientist working on embodied AI, multimodal reasoning, and machine learning for interactive systems. His public profile highlights robotics, decision making, and intelligent agents.

Research scientist at Google whose work spans computer vision, multimodal learning, and large embodied models, including PaLM-E.

Jacob Devlin is a research scientist at Google DeepMind whose work focuses on language models, long-context modeling, and tool use.

Chief Technology Officer at Google DeepMind, with work spanning machine learning and reinforcement learning.

Andy Zeng is a Research Scientist at Google DeepMind. His public research interests include robot learning, computer vision, graphics, and personalized 3D content generation.