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Justin Chen

New York
As seen in: Medium, The New York Times, AIChE Journal, Business Insider, Nature, Wiley Online Library, Yahoo Canada, Aol, arXiv, Harvard Health Publishing and

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Articles

10.20.26 -- Beyond "Subpar Service" And "Difficult Sponsors": The Mutual Enrichment Model

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By Charlie Kirk Fan, Delicia C. Henriques, Justin Chen, Louis Garguilo Verified
| Outsourced Pharma
Webinar: From iPSC Starting Material to Scalable GMP Manufacturing Developing scalable, high-quality iPSC sources can be complex as cell therapy programs move toward clinical manufacturing. This webinar explores an integrated approach combining GMP iPSC lines, working cell banks, GMP-compatible media, and standardized expansion protocols.
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The fight for EPA workers’ rights is a fight for the American public Original

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By Justin Chen
| The Hill Verified
Last year, the Environmental Protection Agency did something that should trouble every American when it tore up a legally negotiated union contract covering approximately 8,000 civil servants. The contract took years to negotiate and was undone in a single day, with claims that its protections somehow threatened “national security”.
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An open vision-language model for diverse medical applications

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By Sahar Kazemzadeh, Fereshteh Mahvar, Atilla P. Kiraly, Timo Kohlberger, Chufan Gao, Ronnachai Jaroensri, Shawn Xu, Cian O Hughes, Cían Hughes, Charles T. Lau, Justin Chen, Tiffany Chen, Jeremy Lai, Samuel Schmidgall, Lu Yang, Kejia Chen, Kenneth A Philbrick, Mercy Asaa Asiedu, Howard Hu, Howard Yang, Richa Tiwari, Sunny Jansen, Preeti Singh, Yubin Kim, Daniel McDuff, Catherine Kozlowski, Shreya pathak, Nino Vieillard, Sarah Perrin, Tatiana Matejovicova, Thomas Mesnard, Geoffrey Cideron, Michelle Casbon, Jean-Baptiste Alayrac, Vlad Feinberg, Sebastian Borgeaud, Cassidy Hardin, Robert Dadashi, Léonard Hussenot, Armand Joulin, Clement Farabet, Joelle Barral, Tris Warkentin, Jonathon Shlens, David Fleet Verified, Shekoofeh Azizi, Omar Sanseviero, Gus Martins, Phoebe Kirk, Anand Rao, Yossi Matias, Katherine Chou, Avinatan Hassidim, Dale Webster, Shravya Shetty, Yun Liu, David Steiner, Rory Pilgrim, Daniel Golden Verified, Lin Yang, Andrew Sellergren, Madeleine Traverse, Fayaz Jamil, Liron Yatziv, Bram Sterling, Stefanie Anna Baby, Susanna Maria Baby, Per Bjornsson, Shashir Reddy, Ryan Brush, Bashir Sadjad, Ines Mezerreg, Rachelle Sico, Aishwarya Kamath, Johan Ferret, Ramona Merhej, Alexandre Ramé, Morgane Riviere, Louis Rouillard, Jean-bastien Grill, Sabela Ramos, Edouard Yvinec, Elena Buchatskaya, Dmitry Lepikhin, Alek Andreev, Olivier Bachem, Victor Cotruta, Kavi Goel, Can Kirmizibayrak
| Nature Verified
Abstract Artificial intelligence has high potential for impact in healthcare applications, but its training and deployment are challenging due to diverse data, a complex spectrum of possible tasks and important privacy needs. High-performing foundation models that enable data-efficient fine-tuning for diverse downstream tasks can meaningfully accelerate development in this domain. We introduce MedGemma, a collection of medical vision-language foundation models based on Gemma 3.
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