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Ethan Yan

A student researcher specializing in artificial intelligence for medical diagnostics, with a focus on developing computational models that enable faster and more accessible disease detection. Recognized by Davidson Fellows, ISEF, and NeurIPS, my research ALLocate and RetinAI advance AI-driven leukemia and ocular tumor screening for resource-limited communities.

Inveniam viam aut faciam

AI Innovator . Cancer Survivor . Biomedical Researcher

EthanYan

I’m a student researcher specializing in artificial intelligence for medical diagnostics, with a focus on developing computational models that enable faster and more accessible disease detection. Recognized by Davidson Fellows, ISEF, and NeurIPS, my research on the ALLocate and RetinAI systems advances AI-driven leukemia and ocular tumor screening for resource-limited communities.

Featured Research

My research develops AI-powered tools that detect cancers like leukemia and retinoblastoma faster and more affordably for early diagnosis.

Davidson Fellow Scholarship · NeurIPS Research Award · ISEF Third Award · AAAI Honorable Mention

AI for Leukemia Detection

A low-cost, AI-powered microscope that detects acute lymphoblastic leukemia (ALL) from bone marrow smears in under a minute with near-clinical accuracy. Built with 3D-printed robotics and deep learning, ALLocate automates a process that normally requires a hematopathologist, helping hospitals like UCSF Benioff, Sutter Solano, and Kaiser Oakland deliver faster, accessible diagnoses.

ISEF Top Award · ISEF 1st Place Grand Award · EUCYS Core Prize · KnowTheGlow Global Collaboration

Wearable Eye Tumor Detection System

A 3D-printed, AI-enabled headset for early screening of retinoblastoma and pediatric eye tumors using computer vision and deep learning. RetinAI detects early signs of leukocoria with 98% accuracy, enabling affordable home and clinic screening in collaboration with KnowTheGlow. Featured by Yahoo News, KTLA, and MarketWatch.

Peer-Reviewed Publications

Sun, S., Yin, Z., Van Cleave, J. G., Wang, L., Fried, B., Bilal, K. H., Lucas, F., Isgor, I. S., Webb, D. C., Singi, S., Brown, L., Shouval, R., Lin, J., Yan, E. S., Spector, J. D., Ardon, O., Boiocchi, L., Sardana, R., Baik, J., Zhu, M., … Goldgof, G. M. (2025). DeepHeme, a high-performance, generalizable deep ensemble for bone marrow morphometry and hematologic diagnosis. Science Translational Medicine, 17(802). https://doi.org/10.1126/scitranslmed.adq2162

Yan, E. (2024). Detection of acute myeloid leukemia using deep learning models based systems. In H. N. Costin, R. Magjarević, & G. G. Petroiu (Eds.), Advances in digital health and medical bioengineering (EHB 2023, IFMBE Proceedings, Vol. 109). Springer. https://doi.org/10.1007/978-3-031-62502-2_49

Manuscripts

Singi, S., Sun, S., Yin, Z., Gupta, R., Webb, D., Bilal, K., Dilip, D., Wang, L., Kumar, N., Nanda, S., Sanchez, N., Cleaves, J., Fried, B., Paulsen, S., Yan, E., … Goldgof, G. (2025). Interpretable multiple instance learning for hematologic diagnosis from peripheral blood smears [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-6933141/v1