MongoDB Server Replication, Lead Engineer | Brown University Math-Computer Science '15
I am a Lead Software Engineer at MongoDB working on Distributed Systems for the Server Replication team.
I graduated from Brown University in 2015 with a joint concentration in Math-Computer Science.
I first began programming in high school and fell in love with it when I took my first class in college.
I love computer science because it is an incredibly rewarding way to answer real world questions. There is no better feeling than fixing the last bug in a program or getting an algorithm to work as intended. I’m especially drawn to the algorithmic, mathematical portions of computer science. I love solving hard problems.
Outside of CS, I enjoy running, playing Ultimate, and seeing Broadway shows.
Other Technologies: MongoDB, Git, LaTex
Brown University B.S. in Math-Computer Science with Honors, magna cum laude, 2015
Elected to Phi Beta Kappa and Sigma Xi Scientific Research Society
Independent Study in Reinforcement Learning
Introduction to Machine Learning
Design and Analysis of Algorithms
Introduction to Discrete Structures and Probability
Ordinary Differential Equations
MongoDB, Lead Software Engineer August 2015 - Present
Database engineer on the Server Replication Team working on
distributed systems, high availability, and fault tolerance.
I've worked on the initial sync process, two different
replication rollback procedures, the Raft consensus protocol,
distributed upgrade-downgrade protocols, and multi-document
View on Github
MongoDB, Software Development Intern Summer 2014
Created a pipeline in Python to automatically translate code documentation and
tutorials from English into other languages. I used an open source statistical
machine translation system called Moses to aid with the translations. I tested
multiple different translation, language, and reordering models and measured their
quality with the BLEU scoring metric, improving by over 150% from initial tests. I
also created a flask web app to allow contributors to edit and approve translations
that the machine or other contributors created. The code for the web app and
associated scripts are packaged into the package Pharaoh in PyPi.
View on Github
View on PyPi
Brown University CS Department, Teaching Assistant
Introduction to Computer Systems, TAFall 2014
Design and Analysis of Algorithms Course, Head TAFall 2013
Introduction to Object Oriented Programming, TAFall 2012
Camp Ramah in Wisconsin, Program Supervisor and CounselorSummers 2011-2013, 2015
Supervised campers and planned camp-wide programs. Managed the camp's IT infrastructure and IT staff. Restructured and supervised the boating program, and trained boating staff.
Meiklejohn Peer Advisor, Brown UniversityAugust 2013 - May 2015
Advised first-years on their introduction to college, including coursework and extracurricular activities.
Wubappella, Brown University February 2013 - May 2015
President and founding member of Brown’s only exclusively dubstep a cappella group.
Co-authored a paper at VLDB'20 on an analysis of multiple techniques to ensure
conformance between a TLA+ specification and its implementation. We
attempted two case studies: model-based trace-checking (MBTC) in the MongoDB
Server’s replication protocol and model-based test-case generation (MBTCG) in
MongoDB Realm Sync’s operational transformation algorithm.
Gave a talk on "Tunable Consistency in MongoDB" at the MongoDB.live annual
conference. This talk takes a deep dive into the consistency levels MongoDB offers,
what they mean, when to use them, and what their latency costs are. This talk was
adapted from this
paper published at VLDB'19.
Lobster is a React application that dynamically renders test logs as needed. It
allows users to search, filter, and bookmark interesting logs. During a company
hackathon two coworkers and I created Lobster, and we won the hackathon. In our
spare time, we continued improving it, and after a few months it was made the
default log viewer in MongoDB's continuous integration (CI) system, Evergreen.
As the default, it became actively maintained by the Evergreen team.
View on Github
For my senior honors thesis, with Professor Michael Littman as my advisor, I
conducted theoretical research into novel planning algorithms for stochastic and
infinite domains. The algorithms lexicographically maximize the probability of
reaching the goal and then among those tied for the maximum, they maximize expected
total reward. My research considers the termination conditions of the algorithms as
well as the runtime using various reward functions.
Conducted research with Professor Michael Littman at Brown University. I used
reinforcement learning techniques, particularly those for solving Partially
Observable Markov Decision Processes (POMDP) to research how optimal merchant
behavior varies across different consumer models.
We designed, developed, and tested a full-featured, networked Monopoly adaption
in Java with a group of four. Players attempt to choose optimal heuristics
with which the application makes decisions for each player in thousands of game
View on Github