About Me

About Jan 25, 2020

Hi there!

My name is Nathan Contreras. I'm a curious computer scientist. While I've worked a lot at the edges of the field – assisting with theory classes and doing research in systems – I've recently become very excited about machine learning. Right now, I'm very interested in language models and curious about improvements that can make their output more structured and logical when generating large amounts of text.

I am a student at Harvard University pursuing a Joint Concentration in Computer Science and Mathematics. I am expecting to graduate in May 2021.

Below, you'll find a summary of my experience and education.

Experience

Software Engineer at JPMorgan Chase

Upcoming Summer 2021

Software Developer (Contract) at Patch RX

December 2020 - January 2021 Virtual

Developed a backend for a virtual assistant chatbot in the consumer mobile application to answer users’ queries including questions about their medications, dosing schedule, and how to use the mobile application. Built using Serverless, Node.js, AWS Lambda and AWS Lex.

Software Engineer Intern at JPMorgan Chase

July 2020 - August 2020 Virtual (Due to COVID-19)

Developed an Android application with augmented reality features to increase guest engagement at the Perot Museum as a part of JP Morgan Chase’s Force for Good program. Application was built in Android Studio using Java 8 and Unity+ARCore. Back-end server used Python with Flask and SQLAlchemy integrated with MySQL. Worked with a team of five other interns in an Agile environment.

Teaching Fellow for CS121 at Harvard

August 2019 - December 2019 in Cambridge, MA.

Teaching Fellow for Harvard’s CS121: Introduction to Theoretical Computer Science course. Responsibilities included: answering students’ questions about homework and course materials during office hours; preparing and holding review sessions for students; grading problem sets and exams.

Software Developer Intern at SoundAround

June 2019 - August 2019 in New York, NY.

Trained and tested machine learning models (in Python using Keras and TensorFlow) for speech analysis. Designed and implemented a phoneme recognition model in Keras to meet processing power and energy constraints. Developed code to stream device microphone audio to multiple analysis services in real-time. Implemented other voice analysis features (such as emotion detection and speaking rate) in Java. SoundAround is an early-stage startup launching wearable devices with voice analysis features.

Teaching Fellow for CS152 at Harvard

January 2019 - May 2019 in Cambridge, MA.

Teaching Fellow for Harvard’s CS152: Programming Languages course, which covers the theory, design, and implementation of programming languages. Prepared and held review sections for students. Held weekly office hours to answer students’ questions on course materials. Graded and provided feedback on problem sets and exams. Received a Certificate of Distinction in Teaching by the Office of Undergraduate Education.

January 2019 (Winter break internship).

Researched the origins of next-generation blockchain applications. Organized information about Ethereum blockchain applications. Identified potential clients that develop decentralized software. Drafted personalized communications to potential clients.

Data Analyst at Bluebonnet Data

June 2018 - August 2019 in Cambridge, MA.

Data Analyst at a student-led startup with the mission of providing political campaigns with high-quality data analysis at a low cost. Provided data-driven insights for effective campaign strategy. Made queries in voter databases to target specific voter demographics. Automated the merging of data between voter databases. Developed software (in R) to automate expected-vote-analysis calculations on datasets from voter databases. Assisted in the creation (using R and Shiny) of a web-based platform to provide campaigns access to useful voter analysis tools. Produced reports on vote projections for diverse campaign strategies.

Machine Learning Research Laboratory Assistant at the University of North Texas

June 2016 - August 2016 in Denton, TX.

Collaborated in the design of an algorithm capable of extracting key phrases from scholarly articles that was three and a half times more likely than competing algorithms to identify relevant words from a given scholarly article. Wrote an optimized, multithreaded implementation of the algorithm in Java with Weka to facilitate fast and efficient key phrase tagging for large datasets.

Education

Harvard University: 2017-2021

Notable coursework:

Computer Science: MIT 6.867: [Graduate] Machine Learning; MIT 6.041: Introduction to Probability;  MIT 6.036: Machine Learning; CS91r: Supervised Research; CS61: Systems Programming and Machine Organization; CS161: Operating Systems; CS152: Programming Languages; CS121: Introduction to Theoretical Computer Science; CS124: Data Structures and Algorithms; CS263: Systems Security.

Math: Math 23a: Linear Algebra and Real Analysis I; Math 23c: Mathematics for Computation, Statistics, and Data Science; Math 117: Probability and Random Processes with Economic Applications; Math 131: Topological Spaces and Fundamental Group; Applied Math 106: Applied Algebra.

Other: Stat 110: Introduction to Probability; Ec10a/b: Principles of Economics.

Texas Academy of Mathematics and Science (TAMS) at the University of North Texas: 2015-2017

(An early college program for 11th and 12th graders to obtain two years of college credits at UNT)

Standardized Testing: SAT I: Math 790, Verbal 760. SAT II: Math 800, Biology-M 800. GPA: 4.0 / 4.0

Notable Coursework: Computing Foundations I (Data structures and formalisms in CS, used C++); Special Problems in Computer Science (Research course, used Java); Differential Equations I; Multivariable Calculus.