Andrew Farabow

Andrew Farabow has extensive experience in machine learning engineering. Their most recent role is as a Machine Learning Engineer at Vake, a Techstars '20 company, where they are currently employed. Prior to this, they worked as a part-time Machine Learning Engineer at Vake and also interned at the company in the same role.

Before their experience at Vake, Andrew worked as an Undergraduate Research Assistant at Virginia Tech. In this role, they contributed to the development of an open-source library for epidemiological models and datasets, focused on forecasting the COVID-19 pandemic and seasonal flu. Andrew also used the library to implement and train influenza-forecasting models for the CDC FluSight Competition. Additionally, Andrew conducted research at Virginia Tech, contributing to the development of a ConvNet-based algorithm for predicting the position of a bat-inspired sonar sensor within a forest area. Andrew also built a simulation called SensorGrid and designed a Resnet-based object-detecting convolutional neural network architecture.

Prior to their time at Virginia Tech, Andrew worked as a part-time Machine Learning Engineer and intern at Decipher Technology Studios. In these roles, they improved the performance of a recurrent autoencoder used for identifying anomalies in service logs and contributed to the development of a predictive autoscaler using deep reinforcement learning. Andrew also implemented various deep reinforcement learning algorithms in PyTorch.

In addition to their technical roles, Andrew worked as a Contributing Author for the Computable AI Blog.

Overall, Andrew Farabow has a strong background in machine learning engineering, with experience in research, development, and implementation of machine learning models and algorithms.

Andrew Farabow earned a Bachelor of Science (BS) degree in Computer Science from Virginia Tech from 2019 to 2023. Prior to that, they completed their high school education and received a High School Diploma from Gonzaga College High School between 2015 and 2019.