I'm Nate Goodman

I like to build things

Recent Brown University Computer Science grad. Incoming Software Engineer at Amazon, Boston. Based in San Diego, California.

About me

I grew up in Arizona and had a lot of time to explore and create. I became deeply curious about storytelling and digital media, and through high school I made a lot of documentaries and narrative films. It wasn't until college that I learned about Computer Science and the ways I could use it to solve new and interesting problems. During my time at Brown University, I have taken classes ranging from Machine Learning and Algorithms, to Programming Languages and Distributed Computer Systems. Outside of class, I've done a bit of Computational Biology and Natural Language Processing research, and during my summers I've worked as a Software Engineer intern at Johnson & Johnson and Amazon. Outside of work, I love to travel, try new coffee, and learn new languages (currently learning Russian).

I'm currently working on a mobile app called Emit. This August I will be joining Amazon Web Services as a Software Development Engineer in Boston, working on distributed file storage at Elastic File System.

Skills and Interests

Startups & Product
Software Engineering


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💻 Amazon Internship
Improve Elastic File System Resilience with Chaos Engineering

Designed and implemented as Java-based CLI that injects periodic faults into Elastic File System resources in order to improve and build confidence in its resilience. Developed a novel approach to scheduling automatic attacks using AWS Maintenance Windows. Leveraged SSM and EC2 APIs to initiate CPU, Disk, VM and Network Latency attacks in groups of EC2 instances concurrently. Used IText, AWS CloudWatch, and internal ticketing APIs to auto-generate a PDF report detailing potential vulnerabilities.

Fault injectionAWSChaos EngineeringDistributed Systems
🔥 Mobile Application
Emit: Spontaneous Hangouts

Co-founded, designed, and developed a mobile application to organize spontaneous hangouts using React Native and Google Firebase. Published to Apple App Store and Google Play Store. Grew user base to 300+ users with two other founders. Implemented recurring events using Google Cloud tasks among other core features based on user-feedback.

Full Stack EngineeringEntrepreneurshipReact NativeGoogle Firebase
💻 Distributed Computer Systems Project

Used Go to implement the Raft protocol; uses passive replication to maintain a consistent log for a cluster. Implemented leader election, log replication, and core functionality for all three node states (leader, follower, and candidate). Implemented the Chandy-Lamport algorithm to provide distributed snapshots of nodes' logs, state machines, and events that transpired in the system as part of Senior Capstone.

Distributed SystemsSenior CapstonePassive ReplicationGolang
🛢️ Computer Systems Project

Used C to build a server to manage a binary search tree database of kv-pairs that can handle multiple client connections over a TCP/IP connection. Implemented fine-grained locking to allow for hundreds of concurrent clients to safely search for entries in, add entries to, and remove entries from the database.

NetworkingMulti-threadingThread safetySignal handling
🍲 Software Engineering Project
What's in My Fridge!

Created a personalized web application to recommend recipes based on the ingredients users have in their fridge. Personally implemented a k-nearest-neighbors search to suggest new recipes based on a user's favorites using word embeddings from FastText.

Full Stack EngineeringNatural Language ProcessingJavaScriptHTML/CSSJavaPythonPostgreSQL
💻 NLP Research
Marijuana Legalization Corpus Study

Currently working under the supervision of the Sloman Lab and AI Lab at Brown University to examine temporal trends in discourse about marijuana legalization on Reddit from 2008 until 2019. Built a stance detection model as a feed-forward layer using Tensorflow to predict whether a given post is pro/neutral legalization. Augmented the model to include configurable input such as author, subreddit, and LDA topic information. Abstracts accepted to ICT 2021 and CogSci 2021

Natural Language ProcessingSQLStance detectionTensorflowLDABERT
🧬 Computational Biology Research

Worked under the supervision of Ashley Mae Conard at the Center for Computational Molecular Biology at Brown University to build TIMEOR, a web-based tool to facilitate the analysis of multi-omics data over a time course. Paper published in BioRxiv in September, 2020.

R-ShinyWeb DevelopmentBioinformatics

Contact Me

Feel free to reach out to me at any time. I am always interested in new stuff!