MedGemma Technical Report

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.

Postdoctoral research scientist at Google Research working on machine learning for healthcare, medical imaging, and responsible AI.

Research scientist at Google DeepMind working on multimodal AI and embodied agents. Her work spans vision-language learning, robotics, and using large models for perception and action.

Google DeepMind researcher working on machine learning, hardware accelerators, and distributed computing, and a co-author of the MedGemma technical report.

Research scientist at Google DeepMind working on multimodal large models and efficient deployment. His public work focuses on building practical AI systems and interfaces.

Robert Dadashi is a Senior Staff Research Scientist at Google DeepMind in Paris, where he has led post-training work on Gemma models. His published work spans reinforcement learning, imitation learning, offline RL, and factually consistent summarization.

Staff research scientist at Google DeepMind in Zurich working on machine learning and large language models. His research interests include deep learning and practical foundation model systems.

Armand Joulin is a research scientist at Google whose work spans natural language processing, machine learning, deep learning, and computer vision.

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.

Staff Research Scientist at Google DeepMind based in Zurich, focusing on multimodal medical foundation models and trustworthy AI for health.

Senior research scientist at Google DeepMind whose public work focuses on reinforcement learning from human feedback, alignment, and large language model evaluation.

Google DeepMind researcher whose public work includes MedGemma and multimodal learning for medical imaging and reasoning.

Edouard Yvinec is a research scientist at the Allen Institute for AI working on language models, active learning, and sequence modeling. He previously completed a postdoctoral appointment at FAIR in Paris and earned a PhD from Sorbonne Universite, where he worked on active learning under Guillaume Lample.

Léonard Hussenot is a research scientist at Google DeepMind whose public homepage highlights machine learning research and publications.

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.

Software engineer in Research at Google DeepMind interested in large-scale and multimodal models, especially for health applications.

Samuel Schmidgall is a researcher whose work focuses on large-scale multimodal and medical AI, generative models, and machine learning for healthcare. He was an author on the MedGemma technical report and maintains a public research homepage with publications and talks.

Research scientist at Google and member of the Med-Gemma team working on multimodal AI and generative AI for healthcare.

Research scientist at Google DeepMind working on multimodal and generative AI for healthcare.

Elena Buchatskaya is a research scientist at Google DeepMind whose public profile and Google Scholar page highlight work on machine learning for healthcare, multimodal models, and medical AI.

Research scientist at Google DeepMind focused on multimodal learning and vision-language models for healthcare.

Research scientist whose work focuses on multimodal machine learning and medical image analysis.

Research scientist at Google DeepMind working on machine learning and language models.

Nino Vieillard is a Research Scientist at OpenAI. Previously, he worked on Gemini at Google and on multimodal models at Mistral AI.