MedGemma Technical Report

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.

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.

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

Public research profiles show Armand Joulin as an author on work in natural language processing, information retrieval, and computer vision.

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

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

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

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.

Google researcher whose public profile lists work across natural language processing, machine learning, multimodal models, and computer vision.

Research scientist at Google DeepMind working on vision-language models; her public OpenReview profile also lists prior study at University College London.

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

Google DeepMind researcher and MedGemma coauthor with a verified Google Scholar profile.

Google researcher whose publications include the PaLM 2 technical report.

Postdoctoral researcher at Google DeepMind working on machine learning for medical applications and responsible AI.

Research scientist at Google DeepMind focused on multimodal machine learning, healthcare, and responsible AI.

Senior technical program manager at Google with experience spanning software engineering, distributed systems, and AI product delivery.

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

Researcher working on medical multimodal AI and foundation models, including MedGemma.

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.

Per Bjornsson is a Research Scientist at Google DeepMind working on natural language processing and machine learning.

Research scientist at Google Research and Google DeepMind focused on multimodal foundation models for medicine and their safe, equitable use, according to his official author page.

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

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 large models and efficient deployment. His public work focuses on building practical AI systems and interfaces.

Canonical link

Olivier Bachem

Sebastian Borgeaud

Jean-Baptiste Alayrac

Armand Joulin

Andrew Sellergren

Sahar Kazemzadeh

Fereshteh Mahvar

Tiam Jaroensri

Atilla Kiraly

Madeleine Traverse

Timo Kohlberger

Cían Hughes

Charles Lau

Liron Yatziv

Bram Sterling

Stefanie Anna Baby

Susanna Maria Baby

Jeremy Lai

Samuel Schmidgall

Per Bjornsson

Ryan Brush

Kenneth Philbrick

Mercy Asiedu

Howard Hu