About me

Physics, Data, Deep Learning

I am the lead for a growing team of Data Scientists at Lockheed Martin. I have one of the coolest jobs in the world, allowing me to apply deep learning and machine learning algorithms to business problems in a broad variety of domains. I’ve recently been focused on NLP, using CNN and LSTM based deep learning models to build end to end NLP pipelines.

I studied physics at The Colorado School of Mines and moved into the field of semiconductor reliability at IBM. In this role I worked with bleeding edge FinFet technologies on Power9 and Z14, focusing on leveraging machine learning to predict reliability failures in eDRAM. I have a MS in Electrical Engineering from UVM, and while I am not rasing my 4 children I enjoy working on my custom smarthome platform built using a Raspberry Pi and ESP8266’s.


M. Johnson, et. al. “Active reliability monitor: Defect level extrinsic reliability monitoring on 22nm POWER8 and zSeries processors,” 2016 IEEE International Test Conference (ITC), Fort Worth, TX, 2016, pp. 1-8.


Temperature stabilization in semiconductors using the magnetocaloric effect: US9222707B2

(3 Pending…)


CNN and LSTM Based Approaches For Document Classification. Nvidia GTC DC 2018

NLP From Scratch: solving the cold start problem for natural language processing Strata 2019

All opinions and views are my own and do not represent my employer.