I graduated from Carnegie Mellon (C/O 2017) where I majored in Cognitive Science, focusing in Machine Learning. After graduation, I worked as a contractor before joining Infoplay.ai in February of 2018 were I worked on Reinforcement Learning for Financial Markets. In May of 2019 I joined Zefr where I am currently a Senior Machine Learning Engineer
My interests in Machine Learning are heavily tied to the projects I work on, or the research I'm interested in.
I interned and did some contract work in medically related fields for a bit and the importance of privacy in the medical field cannot be understated. I believe that accomplishing privacy in ML will be the next big step in large scale adoption as the notion that users can keep their own data private (homomorphic encryption) while still getting access to these predictive models would address the concerns that many have. My favorite paper involving homomorphic encryption and deep learning would easily be [Low Latency Privacy Preserving Inference](https://arxiv.org/abs/1812.10659) which, in my opinion, is a masterful display of the authors understanding of computer systems, cool math, and machine learning. It is my paper of the year for 2019 and the year isn't even done yet.
I have a special place in my heart for Reinforcement Learning - for the longest time I was fascinated with the idea of creating an "artificial intelligence" and Reinforcement Learning always seemed like the closest chance we've had. Unfortunately, I've taken a step back from it for the time being - I think the constant stream of papers, difficulty of reproducing results, and slow (understandably) industrial adoption have discouraged me from continuing to pursue it as a career path.