I'm currently working on my PhD at Harvard University where I am developing methods for the analysis of single-cell genomics data. I also work on methods for CRISPR-Cas9-based genome-editing tools (like base-editing) to study cancer progression. My PhD work is funded by an NIH F31 award through the NCI and focuses on applying innovations in deep learning to analogous problems in the analysis of single-cell data. You can read more about me and my studies at my website.
I am passionate about and interested in:
- 🧿 (single-cell) epigenomics and chromatin biology
- 🧬 Functional annotation of variation in the human genome
- 🧮 Deep learning
- ✂️ Functional CRISPR studies
- 🧠 Psychiatric disorders
- 🧫 Cancer biology
- 🐍 Implementing open-source tools for genomics in python.
- 🎨 Computer graphics art
For tutorials and resources: link
- Michael E. Vinyard, et al. . Nat. Chem. Biol. DOI: 10.1038/s41589-019-0263-0 (2019).
- Chen, H., Lareau, C.†, Andreani, T.†, Michal E. Vinyard†, et al. . Genome Biol. DOI: 10.1186/s13059-019-1854-5 (2020).
- GitHub
- Huidong Chen, Jayoung Ryu, Michael E. Vinyard, Adam Lerer, & Luca Pinello. . bioRxiv, DOI: 2021.2010.2017.464750 (2021).
- GitHub
- docs
Current progress and potential opportunities to infer single-cell developmental trajectory and cell fate
- L. Wang†, Q. Zhang†, Q. Qin†, N. Trasanidis†, Michael E. Vinyard†, et al., . Curr. Opin. in Syst. Biol. ISSN 2452-3100. DOI: 10.1016/j.coisb.2021.03.006 (2021).
- Stein, D.†, Chen, H.†, Vinyard, M.E.†, et al. . Front. genet. DOI: 10.1101/2020.07.30.229534 (2021).
- GitHub
- singlecellVR.com
Dyanmically generated stats are implemented from GitHub Readme Stats
† denotes equal contribution