AI/ML Scientist · Leader · Mentor
Connecting AI and
biomedical discovery.
I’m an AI/ML scientist and leader connecting computer vision, computational pathology, and multi-omics with translational medicine. I build computational methods that turn tissue images and molecular data into insights for cancer research and biomarker discovery.
My interests include AI foundation models and large language models (LLMs) for biomedical discovery, with an emphasis on connecting new methods to meaningful scientific questions.
Images · Models · Discovery
01 / Experience
Feb 2023 — Present
Regeneron Pharmaceuticals
Lead computational analyses that connect tissue imaging and molecular data to biomarker discovery and translational medicine. Develop computer vision methods, predictive models, and analytical pipelines, and build visualization tools and AI infrastructure in collaboration with cross-functional scientific teams. Mentor researchers exploring AI foundation models for computational pathology.
Aug 2022 — Feb 2023
Boston Consulting Group
Translated pharmaceutical business needs into machine learning solutions, including recommendation systems and digital sales optimization. Partnered with client leadership, IT, and sales teams to connect analytical insights with business decisions.
Aug 2017 — Jul 2022
NYU Grossman School of Medicine · Fenyö Lab
Developed deep learning and computer vision models for computational pathology, linking cancer morphology with molecular features and clinical outcomes. Integrated histopathology and proteogenomics in collaboration with researchers and clinicians to support translational cancer research.
Jun 2021 — Aug 2021
Novartis
Applied machine learning and statistical modeling to real-world data in KRAS-G12C-mutant non-small cell lung cancer. Built interactive visualization tools to help scientific teams explore patient heterogeneity and potential prognostic features.
Earlier research: University of Wisconsin–Madison · Record Lab, 2014–2017 / EMBL · Lemke Lab, summer 2016
02 / Education
NYU Grossman School of Medicine · 2017–2022
Dissertation: Linking Histopathology and Proteo-genomics with Deep Learning in Cancers
NYU Grossman School of Medicine · 2017–2020
University of Wisconsin–Madison · 2013–2017
Additional training: Business Analytics (2018) and Credential of Readiness (2022), Harvard Business School Online.
03 / Publication highlights
First and second authored publications from 2021 onward.
2025 · Gynecologic Oncology · Meeting abstractMUC16 as a novel therapeutic target for high-grade endometrial cancer ↗
2025 · CancerMitochondrial proteome landscape unveils key insights into melanoma severity and treatment strategies ↗
2025 · Cell Genomics · CPTAC contributorProteomic-based stemness score measures oncogenic dedifferentiation and enables the identification of druggable targets ↗
2025 · Journal of Proteome ResearchMelanoma proteomics unveiled: harmonizing diverse data sets for biomarker discovery and clinical insights via MEL-PLOT ↗
2025 · Cell · CPTAC contributorPrecision Proteogenomics Reveals Pan-cancer Impact of Germline Variants ↗
2024 · Clinical and Translational MedicineMitochondrial dysfunction and immune suppression in BRAF V600E-mutated metastatic melanoma ↗
2024 · Cell · CPTAC contributorPan-cancer proteogenomics characterization of tumor immunity ↗
2023 · Cancer Cell · CPTAC contributorIntegrative multi-omic cancer profiling reveals DNA methylation patterns associated with therapeutic vulnerability and cell-of-origin ↗
2023 · Cell · CPTAC contributorPan-cancer proteogenomics connects oncogenic drivers to functional states ↗
2023 · Cell · CPTAC contributorPan-cancer analysis of post-translational modifications reveals shared patterns of protein regulation ↗
2023 · Cancer CellProteogenomic data and resources for pan-cancer analysis ↗
2023 · Cancer CellHistopathologic and proteogenomic heterogeneity reveals features of clear cell renal cell carcinoma aggressiveness ↗
2022 · Annals of Oncology · Meeting abstractHeterogeneity and prognostic diagnosis of KRAS-mutated population and KRAS G12C subtype among patients (pts) with advanced NSCLC (aNSCLC): A real-world study aided by machine learning approaches ↗
2022 · Journal of Investigative DermatologyDeep learning and pathomics analyses reveal cell nuclei as important features for mutation prediction of BRAF-mutated melanomas ↗
2021 · Clinical and Translational MedicineThe human melanoma proteome atlas—Defining the molecular pathology ↗
2021 · Clinical and Translational MedicineThe Human Melanoma Proteome Atlas—Complementing the melanoma transcriptome ↗
2020 · CellProteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma ↗
2020 · Cell · CPTAC contributorProteogenomic Characterization of Endometrial Carcinoma ↗
2019 · Cell · CPTAC contributorIntegrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma ↗
2017 · The FASEB Journal · Meeting abstractPredicting and Interpreting the Hofmeister Effects of Different Salts with Nucleic Bases and Aromatic Compounds Using Solubility Assay ↗
* Equal contribution. Complete citations and additional publications are available on Google Scholar and in my CV.
04 / Beyond research
Taught machine learning and supported instruction in Deep Learning in Medicine at NYU. Mentored students and researchers at NYU and Regeneron, including graduate interns through 2026.
Guest editor for BioMedInformatics and reviewer for journals including Cell Reports Medicine, PLOS Computational Biology, and Molecular & Cellular Proteomics.
First place, NYU Langone Health Datathon (2019). Hilldale Undergraduate Research Fellowship (2016–2017).
05 / Media & interviews
Coverage of our Cancer paper on mitochondrial pathways in melanoma and potential therapeutic targets.
Read the news releaseA conversation with Yi Fu about developing Panoptes, connecting tissue images with molecular features, and the future of AI in computational pathology.
Read the interviewCoverage of our collaborative Cancer Cell study integrating proteogenomics and deep learning to investigate endometrial cancer.
Read the storyChinese-language coverage of our Panoptes study in Cell Reports Medicine.
Read on WeChatChinese-language coverage of our work connecting histopathology images with molecular characteristics in endometrial cancer.
Read on NetEaseThe original NCI story about Panoptes, preserved here with its text, image, and saved PDF.
Read the archived story06 / Get in touch
For opportunities and collaborations in biomedical AI, computer vision, and translational medicine.