David R. Bellamy

Since 2012, I went from being a biochemist to an AI researcher and bring a rare interdisciplinary skillset to the field. I am currently a founding AI scientist at a startup originating from Flagship Pioneering, where Moderna was founded. At work, I am developing post-training techniques to teach an LLM agent how to conduct scientific research autonomously. My prior work has been featured in venues such as NeurIPS[1] [2], Machine Learning for Healthcare [3], the European Journal of Epidemiology[4], and Nature Medicine[5] as well as an mRNA patent with the USPTO. I am an experienced software developer and obsess over high-quality code and research infrastructure to accelerate experimentation and learning.

I think that AI is going to transform the world more significantly than computers. I am excited to be a part of this transformation.

Harvard PhD

In my PhD, I built a pre-training dataset, bespoke data tokenizer for a non-canonical modality and a continuous embedding layer to adapt the Transformer to risk prediction in the intensive care unit. I also developed a neural net estimator to solve an ill-posed integral equation for causal inference in the presence of unmeasured confounding.

Interests

I am a perpetual learner with a lot of curiosity. In my spare time, I do my own LLM research, read math textbooks to strengthen my abstraction skills, solve programming and probability puzzles, learn new languages and read papers from fields like neuroscience, drug discovery and psychiatry. I also love the outdoors, traveling and watching movies!

Before AI

I got my start in the lab when I was 18 years old, developing the synthesis for a large organic ligand to coordinate Rhenium and lower the energy barrier for CO2 reduction. I proceeded to work in 6 different laboratories spanning the engineering of oncolytic viruses, the epigenetics of leukemia, mass spectrometry for peptidomics, and transgenerational epigenetic inheritance. I began a PhD in Molecular Biology at Harvard Medical School in 2016 simultaneously enrolling in Harvard’s Neuroscience PhD curriculum but by then was already so impacted by LeCun, Bengio and Hinton’s 2015 Nature paper “Deep learning” that I had to make a career change. Fast forward several years, I received my PhD in Epidemiology and Masters in Biostatistics at the Harvard School of Public Health in May 2023 under the supervision of Andrew Beam, Tianxi Cai and Leo Celi.

On the causality side, I was a member of the Harvard CAUSALab, where I was privileged to work with and learn from Miguel Hernán and James Robins. I have also consulted with Artera.ai on a causal machine learning project for precision treatment decision-making. Upon graduating, I joined my current company as a founding AI scientist.