Joshua M. Wang

Hello!

I am a 5th year MD/PhD student at NYU School of Medicine. My research interests include building machine learning models that integrate multiple clinical data modalities to predict medical outcomes. Projects included forecasting COVID-19 outcomes from clinical data, predicting molecular mutations from digital histopathology, and identifying multi-omic biomarkers predictive of treatment response.

Oh, I’m also a finances, investing and stock market enthusiast 📈.

Interests
  • Machine Learning
  • Healthcare Informatics
  • Precision Medicine
Education

  • MD, 2026

    NYU School of Medicine

  • PhD in Computational BioMedicine, 2024

    NYU School of Medicine

  • BS in Biology, 2017

    Brown University

Projects

Deep-Learning Integration of Histopathology and Proteogenomics

Deep-Learning Integration of Histopathology and Proteogenomics

Capturing tissue morphologies predictive of cancer outcomes and correlating with expression profiles.

Automated Quantification of Tumor Associated Antigen from IHC

Automated Quantification of Tumor Associated Antigen from IHC

Standardize counting of tumor cells stained with antigen of interest.

Predicting COVID-19 Severity in NYC Patients

Predicting COVID-19 Severity in NYC Patients

Retrospective modeling using only routine clinical data to forecast disease severity for rapid triage.

Publications

Joshua M. Wang, Hong R*, Lazar AJ, Demicco EG, Tan J, Moreira AL, Razavian N, Schraink T, Gillette MA, Omenn G, An E, Rodriguez H, Tsirigos A, Ruggles KV, Ding L, Robles AI, Mani DR, Rodland KD, Liu W, Fenyö D (2023). Deep-Learning Integrates Histopathology and Proteogenomics at a Pan-cancer Level. Cell Reports Medicine.

PDF DOI

Joshua M. Wang, Wenke Liu, Xiaoshan Chen, Michael P McRae, John T McDevitt, David Fenyö (2021). Predictive modeling of morbidity and mortality in patients hospitalized with COVID-19 and its clinical implications: algorithm development and interpretation.

PDF DOI

Shen-Huan Liang, Matthew Z Anderson, Matthew P Hirakawa, Joshua M. Wang, Corey Frazer, Leenah M Alaalm, Gregory J Thomson, Iuliana V Ene, Richard J Bennett (2019). Hemizygosity enables a mutational transition governing fungal virulence and commensalism. Cell Host & Microbe.

PDF DOI

Joshua M. Wang, Richard J Bennett, Matthew Z Anderson (2018). The Genome of the Human Pathogen Candida albicans Is Shaped by Mutation and Cryptic Sexual Recombination. mBio.

DOI

Experience

 
 
 
 
 
Merck
Digital Pathology Summer Intern
Merck
June 2022 – August 2022 California
● Trained U-Net models on immunohistochemistry images to quantify tumor associated antigens.
● Compared stain deconvolution methods and transfer learning approaches to improve segmentation performance.
● Applied multiple-instance learning to identify histology features predictive of MSI-H in colorectal cancer.
 
 
 
 
 
Brown University
Undergraduate Student Researcher
Brown University
January 2014 – May 2017 Rhode Island
● Designed and developed Python scripts to identify genomic regions with mutation patterns violating inheritance by descent in Candida albicans: http://snpmap.asc.ohio-state.edu
● Developed R library to perform Gene Ontology enrichment for C. albicans research. Uses Selenium to request, pull and web scrape results from a canonical database lacking API access.
● Led a systematic investigation of RNA-Seq data to annotate novel untranslated regions and construct gene expression networks using weighted gene correlation network analysis.
 
 
 
 
 
Regeneron Pharmaceuticals
Bioinformatics Intern
Regeneron Pharmaceuticals
June 2015 – August 2015 New York
● Applied unsupervised clustering techniques to single-cell transcriptomics to identify biomarkers predictive of proprietary disease conditions.
● Created a visualization platform to analyze single-cell transcriptome data: http://scap.josh.wang
● Presented internship project to Senior VP of Research as one of four interns selected out of 175.

Contact