Multimodal Models | Field

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

Research scientist in Tongyi Lab whose public homepage and OpenReview profile describe work on large language models, multimodal learning, and visual grounding. His public profiles also list affiliations with Alibaba Group and East China Normal University.

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.

Yuntian Deng is a machine learning researcher whose public work spans language modeling, reasoning, and large multimodal systems.

Yale Song is an assistant professor in artificial intelligence at Yonsei University and is also affiliated with the Stanford AI Lab while working part-time with Adobe Research.

Chuanqi Tan works on LLM research and applications. His personal homepage says he earned a PhD from Tsinghua University in 2019 and is also a postdoctoral fellow at The University of Hong Kong.

Research scientist and engineer focused on machine learning, computer vision, and natural language processing.

Research scientist working on large language models, reasoning, agents, and reinforcement learning.

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

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

Mingkun Yang works on multimodal large language models, embodied AI, and robotics. His public profile says he is a postdoc at Zhejiang University and a research scientist at Qwen.

Research scientist at Google DeepMind working on post-training, large language model evaluation, and multimodal alignment.

Olivier Bachem is a director and research scientist at Google DeepMind working on reinforcement learning from human feedback, language model post-training, and machine learning at scale. He earned his PhD at ETH Zurich, where he studied coresets and sampling methods for large-scale machine learning.

Computer scientist and reinforcement learning researcher, Professor at University College London, and former Principal Research Scientist at DeepMind.

Senior Staff Research Scientist at Google DeepMind working on machine learning, with a focus on efficient inference and training algorithms for large language and vision-language models.

Rishabh Singh is a research scientist at Google DeepMind working on human-centered AI, programming systems, and AI for software and problem solving. His work spans program synthesis, code intelligence, education, and interactive AI systems.

Founding engineer at Anysphere, previously at Google Brain, UC Berkeley, and Scale AI, interested in machine learning, statistics, and systems.

Professor at ISTA working on cryptography and machine learning, with interests including privacy-preserving machine learning, large language models, and algorithmic fairness.

Research scientist at Meta working on multimodal machine learning and AI. Previously worked on multimodal learning at Google DeepMind and earned a PhD in computer vision and machine learning from Ecole des Ponts ParisTech.

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

Member of Technical Staff at Google DeepMind working on machine learning, natural language processing, and large language models.

VP of Research at Google DeepMind working on robotics and embodied intelligence, with expertise in machine learning, reinforcement learning, neuroscience, and computer vision.

Google researcher whose public profile says he joined Google in September 2008 and has been with the Google Brain team since January 2015, with interests spanning information retrieval, learning to rank, machine learning, machine translation, and natural language processing.

Associate professor at the University of Virginia and Qwen contributor whose research focuses on personalization and recommender systems, online advertising, and AI systems.

Distinguished Scientist at Google Research and one of the inventors of the transformer architecture; his work also includes language models, speech recognition, and multi-agent reinforcement learning.

Researcher whose public homepage focuses on computer vision, multimodal foundation models, and embodied AI; publication context connects Shilong Liu to the Qwen2.5-Omni technical report.

Research director at Google working on music AI, multimodal generation, and human-AI interaction. He co-founded the Magenta project and has led widely used work on music generation with neural networks.

Jianwei Niu is a tenure-track research assistant professor in the School of Data Science at Lingnan University, Hong Kong. His research focuses on multimodal learning, computer vision, and embodied AI.

Research scientist at Google DeepMind interested in large language models and mathematical reasoning. He earned a Ph.D. in mathematics from Columbia University.

Alibaba senior technology leader and researcher associated with Qwen. Public profiles list him with Alibaba Group, and official Alibaba Cloud coverage identifies him as a chief technology officer leading large-model work.

Carrie Cai is a machine learning researcher with interests in generative modeling, reinforcement learning, and deep learning theory.

Professor of Linguistics and, by courtesy, Computer Science at Stanford University whose research spans natural language semantics, pragmatics, and AI; he directs CSLI.

Johan Schalkwyk is a speech and language researcher whose public profile highlights work on speech recognition, multilingual systems, conversational AI, and large language models.

Research scientist at Google DeepMind and PhD student at Imperial College London. His public site highlights interests in deep learning, reinforcement learning, and multimodal models, with work spanning Gemini, large-scale reinforcement learning, and self-driving.

Research scientist at Google DeepMind working on multimodal large language models. He completed a PhD at Tsinghua University and was a visiting PhD student at UC San Diego.

Yun-Hsuan Sung is a machine learning researcher focused on multimodal learning, robotics, and representation learning.

Distinguished software engineer at Google Brain focused on large-scale computer systems and machine learning applications.

Research scientist at Google DeepMind.

Yuanzhi Zhu is a Qwen researcher whose public work includes multimodal and audio-language models.

Research Scientist at Google DeepMind in London working on large multimodal models, evaluation, agents, and computer vision; he completed a PhD at the University of Tuebingen and MPI for Intelligent Systems.

Aäron van den Oord is a Google DeepMind researcher known for generative and sequence-model research.

HyoukJoong Lee is a research scientist at Google DeepMind. His public work includes long-context and multimodal model research, including Gemini 1.5 and Gemini Diffusion.

Z.ai researcher focused on multimodal large language models and computer vision, with interests in large-model training and post-training.

Chengzheng Xu is a research scientist at Google DeepMind whose public homepage highlights work on vision-language models, multimodal learning, and efficient large-scale machine learning.

Research Scientist at Google DeepMind focused on agents, memory, and reasoning; completed a PhD at Stanford advised by Percy Liang.

Qazi Irfan is a research scientist at Google DeepMind. His public homepage highlights work spanning multimodal learning, visual reasoning, and efficient large-scale machine learning.

Research engineer focused on frontier multimodal AI systems; a founding member of Google's Gemini core team who previously helped start OpenAI's multimodal team.

Senior Staff Research Scientist at Google DeepMind working on AI for science, imaging, geometry processing, and differentiable simulation; previously at Adobe and NVIDIA.

Research scientist at Google with public work on machine learning systems, recommendation, fairness, and safety.

Research scientist at Google DeepMind working on efficient, adaptive systems that learn on the job and collaborate with people. She completed a Ph.D. in machine learning at Mila and Universite de Montreal.

Staff research scientist at Google DeepMind and associate professor at Purdue University working on sequential decision making, machine learning, and algorithms.

Research scientist at Google DeepMind.

Research scientist at Google DeepMind whose public profile also lists prior AI infrastructure leadership at NVIDIA, founding Mesosphere, and earlier research roles at FAIR.

Professor of computing science at the University of Alberta and Canada CIFAR AI Chair with public work on reinforcement learning, optimization, and scalable machine learning.

Eugene N. Ie is a Google DeepMind researcher with public work on machine learning and multimodal language models.

Research scientist at Google DeepMind working on machine learning and large-scale multimodal models.

Tony G. Cai is a researcher at Google DeepMind and a computer science PhD student at Columbia University. His public research interests include large language models, reinforcement learning, optimization, and robotics.

Researcher affiliated with the Qwen team at Alibaba Group on Google Scholar and coauthor of the Qwen and Qwen3 technical reports.

Research scientist at Google DeepMind working on language model reasoning and AI safety. He previously worked as a member of technical staff at Anthropic.

Research scientist at Google DeepMind focused on long-context and memory-augmented models. His public profile describes work on architectures and algorithms that help models reason over real-world data.

Researcher at OpenAI working on reasoning and scalable oversight, with prior work on chain-of-thought prompting, instruction tuning, and aligning language models with human preferences.

Research scientist at Qwen, Alibaba, whose public OpenReview profile lists work on vision-language models and large language models.

Research scientist at Z.ai focused on multimodal understanding and generation, large language models, and reinforcement learning. He works on pre-training, post-training, and evaluation of multimodal models.

Research scientist at Z.ai focused on multimodal understanding and generation, reinforcement learning, AI agents, and end-to-end models. He received a bachelor's degree from Tsinghua University and a master's degree from UCLA.

Research scientist at Google DeepMind in Mountain View working on machine learning, reinforcement learning, and robotics.

Research engineer focused on large-scale machine learning and AI systems. He has worked at OpenAI and publishes writing and projects on his personal website.

Founder and CEO of Google DeepMind, leading AI research and product development; his work spans AI, neuroscience, game playing, and structural biology.

Research Director at Google working on machine learning, production systems, and sociotechnical AI.

Chief Scientist at Google DeepMind and Vice President of Research leading Gemini, with work spanning scalable sequence learning, large language models, games, and robotics.

Rohan Anil is a research scientist at Google DeepMind. His public homepage highlights work on large language models, efficient machine learning systems, and multimodal AI.

Research scientist at Google DeepMind in London working on agentic reasoning, efficient inference, and large-scale post-training, with a background in high-dimensional statistics and theory.

Senior Staff Research Scientist at Google DeepMind and CTO of the Gemini app, with work spanning speech, language, vision, and large-scale AI systems.

Vice President of Engineering and Research at Google and site lead for the Google Center in Israel; he also leads Search, Research, and AI for Crisis Response.

Senior staff research scientist at Google working on algorithms for decision making under uncertainty and online learning.

Professor of Computer Science at the Hebrew University of Jerusalem and Visiting Faculty Researcher at Google, with work spanning algorithms, algorithmic economics, and AI-related decision systems.

Researcher at OpenAI working on frontier AI across coding, reasoning, post-training, and multimodal systems, with earlier work in accelerated computing and machine learning.

Researcher at Alibaba Group working on multimodal large language models; public profile and publication context connect Hang Zhang to the Qwen2-VL technical report.

Research scientist at Z.ai focused on multimodal understanding and generation, reinforcement learning, AI agents, and end-to-end models. He received a bachelor's degree from Tsinghua University and a master's degree from Peking University.

James Manyika is a Google leader whose public work focuses on research, technology, and society.

Linjie Li is a research scientist at Alibaba Group and a contributor to the Qwen2.5-Omni Technical Report.

Senior Staff Research Scientist at Google DeepMind working on language modeling, speech recognition, machine translation, and multimodal understanding.

Researcher at Google Research whose public work includes multimodal and vision-language modeling, with arXiv publications tied to PaliGemma and related transfer work.

Second-year PhD student at Peking University focused on audio-language foundation models, trustworthy AI, and embodied AI; coauthor of Qwen2-Audio.

VP at Google DeepMind working on deep learning, computer vision, and language understanding.

Research engineer at Alibaba Group working on audio and multimodal foundation models, multimodal RL, and speech processing; coauthor of Qwen2.5-Omni.

Research scientist and GPT-4 coauthor known for work on generative modeling, diffusion methods, and machine learning systems.

Machine learning engineer and researcher interested in large language models and multimodal audio-language systems; coauthor of Qwen2-Audio.

Research scientist at Moonshot AI with public scholarly work on multimodal and generative models.

Research scientist at Z.ai with research interests in multimodal understanding and generation, large language models, and reinforcement learning. He received a bachelor's degree from the University of Science and Technology of China and a master's degree from Tsinghua University.

VP of Research at Google DeepMind and Professor of Information Engineering at the University of Cambridge, known for work in probabilistic machine learning and Bayesian statistics.

Research scientist at Google DeepMind working on the theoretical foundations of machine learning.

Adrian Ibarz is a Google DeepMind researcher whose public work spans machine learning, reasoning, and large multimodal models.

Research scientist at Anthropic focused on building trustworthy AI systems and studying the effects of scaling; his publication list includes the Gemini technical report and related frontier-model work.

Research scientist and founder of rabinovich.ai, with work spanning multimodal generative models, visual perception, and immersive experiences.

Anmol Kalra is a research scientist at Google DeepMind. His public homepage presents his work and publications in machine learning and AI systems.

Research scientist at Google DeepMind working on large language models and natural language processing.

Software engineer at Google DeepMind and PhD student at Cornell working on machine learning for social impact, with interests in LLMs, generative models, and optimization.

Chris McLeavey is a research scientist at Google DeepMind working on generalist multimodal models at the intersection of language and vision.

Research scientist at Google DeepMind working on large language models and multimodal models. He earned a PhD in computer science from Stanford University.

Assistant Professor in the Department of Computer Science and Technology at the University of Cambridge, with research focused on making AI systems safer, more efficient, and more robust.

Research scientist at Google DeepMind focused on human-computer interaction, accessibility, and interfaces for AI systems.

Research scientist at Google DeepMind working on multimodal language models and long-context machine learning systems.

Jianfei Chen is an assistant professor at Monash University. His research spans computer vision, machine learning, multimodality, and trustworthy AI.

Director of Product Management at Google DeepMind leading ML and AI platforms, model developer experiences, and workflows that power the Gemini app and API.

Principal statistician at Google DeepMind whose work spans causal inference, statistics, and machine learning.

Distinguished professor emeritus of electrical engineering, computer science, and mathematics at UC Berkeley. His research focuses on numerical linear algebra, parallel computing, and communication-avoiding algorithms.

Julien Perolat is a research scientist at Google DeepMind whose public homepage highlights work on game theory, multi-agent learning, reinforcement learning, and responsible AI.

Research scientist at Google DeepMind working on large-scale multimodal models.

Google researcher whose publications include the Gemini technical report.

Researcher working on multimodal foundation models, including Qwen3-Omni and related speech-language systems.

Postdoctoral researcher at UC Berkeley and Berkeley AI Research interested in natural language processing, machine learning, and human-computer interaction.

Research scientist in Tongyi Lab and technical lead of Qwen2.5-Omni, with public work on end-to-end speech understanding and generation.

Mahesh Shanmugam is a research scientist at Google DeepMind whose public homepage highlights work on multimodal representation learning, self-supervised learning, and generative models.

Mark Bosma is a senior research scientist at Google DeepMind. His public homepage highlights work in machine learning, reinforcement learning, and neural networks.

Research scientist at Google DeepMind in Switzerland working on large multimodal models and generative AI.

Researcher and engineer focused on machine learning, distributed systems, and applied algorithms; his personal site also highlights interests in psychology, neuroscience, and evolutionary biology.

Research scientist at Google DeepMind and PhD student at Stanford University. His homepage highlights work on machine learning, reinforcement learning, language models, and recommendation systems.

Assistant Professor of Computer Science at the University of Southern California and incoming part-time Visiting Faculty Researcher at Google DeepMind; her research combines linguistic structure and machine learning for natural language processing.

Research scientist at Google DeepMind working on large language models, multimodal language models, and computer vision, according to his public OpenReview profile.

MohammadHassan Moghimi is a senior staff software engineer at Google DeepMind whose work focuses on multimodal models for vision and natural language, including parameter-efficient tuning, adaptation, and evaluation.

Research scientist at Google DeepMind working on multimodal machine learning, reinforcement learning, and mathematical optimization.

Research scientist at Google DeepMind in London. He completed a PhD at the University of Oxford, where his work focused on natural language processing and computational argumentation.

Ravi Seethapathy is a research scientist at Google DeepMind. His public homepage presents work at the intersection of machine learning, science, and large-scale AI systems.

Senior research scientist at Google DeepMind with public work on machine learning evaluation, uncertainty, and reliability.

Research scientist at Google DeepMind working on large language models and multimodal models. He completed a PhD in computer vision and machine learning at the University of Oxford.

Research scientist at Google DeepMind working on multimodal, multilingual, and efficient machine learning.

Research scientist at Google DeepMind in New York working on vision-language and multimodal large language models. He is completing a PhD in computer science at Carnegie Mellon University.

Research scientist at Google DeepMind in Mountain View working on large multimodal foundation models and agents. He received a PhD from the Chinese University of Hong Kong.

Postdoctoral scholar at Stanford HAI and incoming Assistant Professor at the University of Southern California, with research on large-scale machine learning, multimodal reasoning, and efficient training.

Radu Soricut

Jiabo Ye

Aakanksha Chowdhery

Yuntian Deng

Yale Song

Chuanqi Tan

Mohammad Norouzi

Yuhuai Wu

Jiahui Yu

Xinyun Chen

Mingkun Yang

Yueting Wang

Olivier Bachem

David Silver

Hanie Sedghi

Rishabh Singh

Will Isaacs

Vladislav Kolesnikov

Jean-Baptiste Alayrac

Koray Kavukcuoglu

Yifeng Lu

Raia Hadsell

Yonghui Wu

Hongning Wang

Noam Shazeer

Shilong Liu

Douglas Eck

Jianwei Niu

Aitor Lewkowycz

Jingren Zhou

Carrie Cai

Christopher Potts

Johan Schalkwyk

Sharat Muralidharan

Yiang Gu

Yun-Hsuan Sung

Zhifeng Chen

Sebastian Faust

Yuanzhi Zhu

Matthias Minderer

Aäron van den Oord

HyoukJoong Lee

Ling Chen

Chengzheng Xu

Kelvin Guu

Qazi Irfan

Vedant Misra

Alexander Vladymyrov

Alex Beutel

Amelie Royer

Branislav Kveton

C. Le Lan

Clement Farabet

Dale Schuurmans

Eugene N. Ie

Tiago Cai

Tony G. Cai

Peng Wang

Brennan Saeta

Donald W. McFadden

Jason Wei

Qinyu Chen

Yipeng Wang

Zihan Jiang

Andrew M. Dai

David Dohan

Demis Hassabis

D. Sculley

Oriol Vinyals

Rohan Anil

Sebastian Borgeaud

Vincent Vanhoucke

Yossi Matias

Alina Beygelzimer

Avinatan Hassidim

Ben Wang

Hang Zhang

Hao Xu

James Manyika

Linjie Li