I am an experienced researcher with a passion for deep learning and its applications to finance and health care. As a Ph.D. student in the Department of Theoretical and Computational Chemistry at the University of Cambridge, I developed molecular dynamics simulations to make fundamental theoretical contributions towards understanding non-equilibrium nano-scale flow. Projects included some of the first non-equilibrium calculations of nanoscale flow profiles induced by temperature and chemical potential gradients. Subsequently, as a postdoctoral associate at the Massachusetts Institute of Technology, I used methods from information theory to quantify immune cell signal discrimination between healthy and infected cells. I also developed kinetic Monte Carlo methods to optimize vaccination protocols against highly mutable pathogens. All of this work resulted in numerous publications.

Recently, I began working on a startup in an effort to apply methods from deep reinforcement learning and machine translation to algorithmic trading and financial portfolio optimization. On the side, I have been doing consulting projects for a few A.I. startups in finance and health care.

Modeling Skills

Recurrent Neural Networks (LSTMs), Deep Reinforcement Learning, Machine Translation, Molecular Dynamics, Monte Carlo, Stochastic Simulations, Systems Biology Modeling

Software Skills

Python, Numpy, Pandas, PyTorch-GPU, Amazon Sagemaker, Amazon EC2, Google Cloud Platform, LAMMPs, OpenAI gym, Stable Baselines, Bash, SLURM, Docker, Singularity, Git