Andrew Manigault, PhD

Software Engineer

I apply a scientifically-rooted approach to engineer creative, user-centric software solutions.

About

Coming from a background in health psychology research, I discovered my passion for software while using R to streamline my method for data analysis and manuscript writing. What began as curiosity for coding transformed into a deep appreciation for problem-solving through technology, leading me to pursue a career in software engineering to build meaningful, data-driven solutions.

My software engineering approach has been shaped by several key skills honed over a decade of research experience—scientific experimentation, writing, and learning. User acceptance testing and bug fixing greatly benefit from a scientific mindset—one that involves hypothesis testing and measurement. Similar to academic writing, software engineering is an iterative process that benefits greatly from continuous user feedback and regular revisions. Effective researchers and software engineers tend to learn quickly as they are required to keep up with an ever-evolving literature or technology landscape. My research background strengthens my approach to software engineering—a connection championed by respected engineers, who emphasize the integration of science and software. Indeed, David Farley defines software engineering as the application of an empirical, scientific approach to finding efficient, economic solutions to practical problems in software.

My main focus these days is developing applications and tools that solve practical problems for InductiveHealth and the Centers for Disease Control and Prevention (CDC). I love that my job provides me with the opportunity to frequently explore new tech stacks in support of a valuable public health mission—stopping disease through technology.

Outside of work, I'm usually running, hanging out with my wife, cat and dog, or experimenting with game design/development.

Please reach out if you have any questions or are seeking to collaborate. Cheers!

Experience

  1. 2023 — Present

    Software EngineerInductiveHealth Informatics

    Developed containerized tools for monitoring data quality within the National Syndromic Surveillance Program (NSSP) of the Centers for Disease Control and Prevention (CDC), improving data access, usage, and report sharing. Introduced several new applications and enhanced legacy web applications, refactoring backend and improving ease of data exploration.

    • R
    • SQL
    • Python
    • Docker
    • JavaScript
    • html
    • css
    • Linux
    • Bash
    • Palantir Foundry
  2. 2021 — 2023

    BiostatisticianBrown University – Center for the Study of Children at Risk

    Developed R packages/scripts to support research center activities including infant cry acoustic analysis, latent growth curve modeling, multilevel modeling, data cleaning and visualization. Trained machine learning classifiers to identify neonatal opiate withdrawal syndrome using infant cry acoustics.

    • R
    • SAS
    • MATLAB
    • SQL
  3. 2020 — 2021

    Post-doctoral Research FellowUniversity of California Los Angeles

    Developed R package to perform multithreaded transcriptomic origin analysis. Carried out Psychoneuroimmunology research activities, including statistical analysis and manuscript writting.

    • R
    • SAS
    • Java

Projects

Most of my professional work requires some level of CDC access and is not linked to my personal github. However, I tried to list some shareable projects on this website.

  • Web Portfolio (this website)

    A modern, responsive portfolio website built with Next.js, React, and TypeScript. This project was my introduction to this popular tech stack. I enjoyed the way React promotes modular design with components and found that very little was lacking for the purpose of a static web application.

    • Typescript
    • React
    • Next.js
    • Tailwind CSS

  • Neural Network from Scratch

    A Kaggle notebook showing how to code a neural network from scratch in R. I followed along a cool Python tutorial on writing a digit recognizer neural network. This project helped me gain clarity on how neural networks leverage back propagation to improve their predictions.

    • R
    • Python
    • Kaggle

  • ShinySnake

    An Interactive Web application made with R Shiny. The application illustrates how a very simple set of heurisitcs may be applied to automate the game "snake". This project introduced me to speeding up a given R function with a C++ substitution, adding Javascript and CSS to an R Shiny Application, and training a neural network to play the game using previous game states as inputs.

    • R
    • Shiny
    • shinyapp.io
    • C++
    • CSS
    • Javascript

  • Transcript Origin Analysis

    An R package to carry out a genomic analysis technique that compares gene expression between a sample and a reference expression profile. This project was a software port of some older Java code graciously shared by Dr. Steve Cole. I initially intended to re-write the procedure in R to better understand how it works. Here I learned how to compile and run Java code, how to do multithreading in R, and how to break down a larger program into data "checkpoints" to check the accuracy of my port.

    • R
    • Java

  • Hexpow Mobile Game

    A "connect-3" mobile game made with Unity. The intent of this project was to familiarize myself with C#, Unity, and the process of releasing apps on the Google Play Store. This project was the start of a new hobby (game dev) and taught me that Google will prune apps that are not regularly updated 😊. Forunately, the game is still playable on the browser as a WebGL build.

    • C#
    • Unity
    • Google Play Store

Publication