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anoynmous resume .pdf

Original filename: anoynmous-resume.pdf
Title: Lucas Reynolds' Resume
Author: Lucas Reynolds

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Lucas Reynolds
lreynolds18@gmail.com ● +1 (810) 908-0956
https://lreynolds18.github.io https://github.com/lreynolds18 ● https://www.linkedin.com/in/lreynolds18
Whitmore Lk, Mi 48189


Michigan State University, East Lansing, Mi
■ Bachelor of Science (B.S.) in Computer Science, Cognate in Mathematics


Cumulative GPA: 3.75 / 4.00
Graduated with Honors

Institute For Cyber-Enabled Research
■ Technical Student

June 2016 – May 2017

Submitted over 10,000 jobs on the High Performance Computer to benchmark I/O with C++
Produced graphs with python (pandas, numpy, matplotlib) to prove that the scratch file
system had 8x better I/O bandwidth than research file system
Teaching assistant for Intro to Linux, Intro to HPCC, and Big Data workshops
Installed software requested by customers onto High Performance Computer

■ Back-end Developer


May 2015 – May 2016

Added user functionality to ARCS (Archaeological Resource Cataloguing System) by
creating a login modal, forgot password form, upload and crop profile picture, and a profile
page in cakephp and html
Implemented an angularjs ARCS plugin to give researchers admin control of ARCS
Took ownership of code and database quality - wrote scripts to clean mySQL database,
cleaned and tested large sections of legacy code, and tested user functionality

Global Observatory for Ecosystem Services
■ Web Developer

May 2017

June 2014 – November 2014

Updated mapping interface from Google Leaflet to ArcGis ESRI map on MRV website
Implemented a stratified plot sampling algorithm and sampling design form in python
(django) and javascript

GE PETT: Predix-Enabled Toy Train

Worked with four other MSU Computer Science seniors to designed a Bachmann N-scale
toy train-set to demonstrate the power of GE’s Predix
Used microcontrollers and sensors to locate and control all trains on the train-set
Implemented a website to visualize and interact with the data collected
Produced a video about the delivered product

Voice Analyzer

Applied machine learning and data techniques to classify gender, age, and dialect of a human
sound recording
Collected and processed around 15,000 sound clips of human voice by using python (librosa,
numpy, pandas, matplotlib)
Dealt with missing and vague data and used sampling techniques to get better data


Python, C/C++, Matlab, Java, Javascript, SQl, NoSQL, Hive, Pig, HTML, CSS


Linux/Unix, Hadoop (MapReduce), Spark, Frameworks (Django, Cakephp, Angularjs, React/Redux),
jQuery, Twitter Bootstrap, AWS, Docker, Vim, Version Control (git, svn)


MSU Data Science, MSU ACM

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