Paul Allen School of Computer Science and Engineering, University of WashingtonI am Hanwen Xu, a Ph.D. student at the University of Washington, working at the intersection of generative AI and medicine, advised by Prof. Sheng Wang. Throughout My research builds large-scale multimodal foundation models that integrate pathology, radiology, and clinical records to decipher the complex cancer patient journey. I led/co-led projects such as GigaPath, GigaHeart, LLaVA-Rad and BiomedCLIP, aiming to advance multimodal AI for precision medicine and real-world clinical impact. Before my PhD, I earned a BS and MS in Department of Automation in Tsinghua University.
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University of WashingtonPaul Allen School of Computer Science and Engineering, University of Washington
Ph.D. StudentSep. 2022 - present -
Bioinformatics Division, Tsinghua UniversityM.S. in AutomationSep. 2020 - Jul. 2022 -
Department of Automation, Tsinghua UniversityB.S. in AutomationSep. 2016 - Jul. 2020
Honors & Awards
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Weil Family Endowed Fellowship in Computer Science and Engineering2023
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The Scholarship for Comprehensive Outstanding Performance of Graduate Students, Tsinghua University2021
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Merit Student, Tsinghua University2020
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National Scholarship of China2019
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Tsinghua Innovation Award of Science and Technology2019
News
Selected Publications (view all )
A whole-slide foundation model for digital pathology from real-world data
Hanwen Xu, Naoto Usuyama, Jaspreet Bagga, Sheng Zhang, Rajesh Rao, Tristan Naumann, Cliff Wong, Zelalem Gero, Javier González, Yu Gu, Yanbo Xu, Mu Wei, Wenhui Wang, Shuming Ma, Furu Wei, Jianwei Yang, Chunyuan Li, Jianfeng Gao, Jaylen Rosemon, Tucker Bower, Soohee Lee, Roshanthi Weerasinghe, Bill J. Wright, Ari Robicsek, Brian Piening, Carlo Bifulco, Sheng Wang, Hoifung Poon
Nature 2024 2,000,000 downloads
Prov-GigaPath is a large-scale digital pathology foundation model pretrained on 1.3 billion image tiles from 171,189 whole-slide images across 30,000+ patients and 31 tissue types in the Providence network. Built on a new GigaPath architecture that adapts LongNet for slide-level learning, it enables ultra-large-context modeling of gigapixel pathology slides. Prov-GigaPath achieves state-of-the-art results on 25 of 26 benchmark tasks and demonstrates strong potential for vision–language pathology modeling using real-world data.
A whole-slide foundation model for digital pathology from real-world data
Hanwen Xu, Naoto Usuyama, Jaspreet Bagga, Sheng Zhang, Rajesh Rao, Tristan Naumann, Cliff Wong, Zelalem Gero, Javier González, Yu Gu, Yanbo Xu, Mu Wei, Wenhui Wang, Shuming Ma, Furu Wei, Jianwei Yang, Chunyuan Li, Jianfeng Gao, Jaylen Rosemon, Tucker Bower, Soohee Lee, Roshanthi Weerasinghe, Bill J. Wright, Ari Robicsek, Brian Piening, Carlo Bifulco, Sheng Wang, Hoifung Poon
Nature 2024 2,000,000 downloads
Prov-GigaPath is a large-scale digital pathology foundation model pretrained on 1.3 billion image tiles from 171,189 whole-slide images across 30,000+ patients and 31 tissue types in the Providence network. Built on a new GigaPath architecture that adapts LongNet for slide-level learning, it enables ultra-large-context modeling of gigapixel pathology slides. Prov-GigaPath achieves state-of-the-art results on 25 of 26 benchmark tasks and demonstrates strong potential for vision–language pathology modeling using real-world data.
Multilingual translation for zero-shot biomedical classification using BioTranslator
Hanwen Xu, Addie Woicik, Hoifung Poon, Russ Altman, Sheng Wang
Nature Communications 2023
BioTranslator introduces a multilingual translation framework that maps free-text biological concepts to non-text data, enabling scientists to interact with biological data without relying on predefined vocabularies. It allows the discovery of novel entities such as cell types and generalizes to tasks like protein function prediction and drug target identification.
Multilingual translation for zero-shot biomedical classification using BioTranslator
Hanwen Xu, Addie Woicik, Hoifung Poon, Russ Altman, Sheng Wang
Nature Communications 2023
BioTranslator introduces a multilingual translation framework that maps free-text biological concepts to non-text data, enabling scientists to interact with biological data without relying on predefined vocabularies. It allows the discovery of novel entities such as cell types and generalizes to tasks like protein function prediction and drug target identification.