About

Welcome to my website! My name is Nathan LaPierre, and I am currently a Postdoctoral Research Fellow in the Department of Medical Oncology at the Dana-Farber Cancer Institute and Harvard Medical School, working in the lab of Alexander Gusev.

I am a computational biologist and machine learning researcher. My current work broadly focuses on “virtual cell” models, including tasks such as perturbation prediction and trajectory inference for single cell data. I develop machine learning methods towards these ends, and I am particularly interested in developing and applying deep generative models and foundation models. I am interested in applications of these models to cellular reprogramming and differentiation, as well as applications to aging, cancer, and other diseases. Broadly, my goal is to help bring about a future in which biology is much more quantitative and predictable, and thus amenable to control and optimization, leading to revolutionary progress in biomedicine and bioengineering.

In the past, I have worked on various problems in statistical genetics and genomics, including Mendelian randomization, fine mapping, deep learning for disease prediction, and metagenomics. I completed a PhD in Computer Science at UCLA and worked as a postdoc at the University of Chicago.

For more details, see the links below.

Important links:

Curriculum Vitae (pdf) | Google Scholar | GitHub | Twitter | LinkedIn