David Rémy Bellamy

I am independently researching ways to improve how LLMs learn.

Previously, I was a founding AI researcher at Lila Sciences working on scientific superintelligence.

News

5/2025: I left Lila to pursue independent research in RL!
12/2024: We publicly launched our company Lila Sciences!
12/2024: Labrador was awarded the 2024 Machine Learning for Health (ML4H) Best Paper Award.
11/2024: Labrador was awarded an oral spotlight presentation at ML4H 2024.
10/2024: DAG-aware Transformer accepted at NeurIPS 2024 workshop on Causal Representation Learning.
10/2023: GPT-4 evaluation on the US Anaesthesiology Board Exam accepted by the British Journal of Anaesthesia.
06/2023: Conformal prediction with LLMs accepted at ICML 2023 workshop on Neural Conversational AI.
05/2023: Joined Lila Sciences as employee #1 and founding AI researcher.
05/2023: I defended my PhD on the 2nd of May. My thesis is here. Thanks to my examination committee Andrew Beam, Tianxi Cai and Leo Celi.
09/2022: Deep learning for proximal inference paper accepted at NeurIPS 2022 main conference.
07/2022: Began consulting with Artera.ai on a causal machine learning project for precision treatment decision-making.
06/2022: Structural characterization of shortcut features accepted by the European Journal of Epidemiology.
03/2022: Began teaching Harvard’s Deep Learning course at the Medical School.
02/2022: Placed 2nd out of 60 teams in the Lab for Innovation Science at Harvard datathon.
01/2022: Attended the Causality Boot Camp at The Simons Institute for the Theory of Computing.
05/2022: I completed my Masters of Science in Biostatistics at Harvard University.
01/2022: I was awarded the Harvard Department of Epidemiology’s UCB Pharma Fellowship.
09/2021: I was selected as a founding member of the Harvard CAUSALab by Miguel Hernan and James Robins.
06/2021: Began teaching the full-time graduate summer foundations course in biostatistics.
10/2020: I published a systematic review of neural net performance on tabular healthcare data.
08/2020: Analysis of nonprofit vs. for-profit charity care spending accepted at the Journal of General Internal Medicine.
06/2020: I joined Andrew Beam’s lab at Harvard officially marking my transition to machine learning.
08/2018: Started a second PhD in Epidemiology at the Harvard School of Public Health to study algorithms for digital phenotyping with Elise Robinson.
01/2018: Left my PhD at Harvard Medical School to pursue a more ML-focused path.
08/2016: Started my PhD in Biological & Biomedical Sciences at Harvard Medical School to study transgenerational epigenetic inheritance in C. elegans with Eric Greer.
01/2016: Began teaching the University of Ottawa’s core Biochemistry course.
05/2015: Read Hinton, Bengio and LeCun’s 2015 Nature paper “Deep learning”. Curiosity for AI began.
01/2015: Paper on oncolytic viruses and the tumor microenvironment accepted at Nature Medicine.
09/2014: Joined Harvard Medical School as a student researcher in Yang Shi’s lab.