Jun 2020 – Sep 2020 · 3 mos

• Optimal container packing on shared datacenters

Jun 2019 – Sep 2019 · 3 mos

• Optimization and machine learning for motion planning and control of autonomous vehicles.

Sep 2018 – Dec 2021 · 3 yrs 3 mos

• Advised by Stephen Boyd. I published a book that introduces a framework for vector embedding, generalizing 100 years of methods. I also helped develop two major open source projects for convex optimization and data science, CVXPY and PyMDE, with over half a million monthly downloads.

Sep 2017 – Oct 2018 · 1 yr 1 mo · San Francisco Bay Area

• As a team member of Google Brain, I worked on TensorFlow, an open-source library for machine learning. Specifically, I worked on the core graph runtime, eager execution, and model inference on TPUs.

Apr 2017 – Jun 2017 · 2 mos

• Held a weekly tutorial section and helped draft the final exam for this graduate course in convex optimization, taught by Professor Stephen Boyd.

Sep 2016 – Sep 2017 · 1 yr

• Under the supervision of Professor Stephen Boyd, I helped design and implement version 1.0 of CVXPY, a Python-embedded DSL for convex optimization, and accelerated the classical von Neumman alternating projections algorithm for finding a point in the intersection of convex sets, a subroutine with which cone problems can be readily solved. Applications include machine learning, radiation treatment planning, finance, control, and more.

Sep 2016 – Dec 2016 · 3 mos

• Mentored a dozen groups in creating applied AI and machine learning projects, edited Professor Percy Liang’s lecture notes, held office hours, and graded assignments and final papers.

Jan 2016 – Jun 2016 · 5 mos

• Held office hours and graded for CS109, Probability for Computer Scientists, a required class in probability taught by professor Mehran Sahami and Chris Piech.

Feb 2014 – Jul 2015 · 1 yr 5 mos

• As an undergraduate researcher in the Stanford InfoLab, I discovered insights about and learned from large datasets. I helped compile the first large-scale, human-tagged open dataset describing affect in discussion forums in Massive Open Online Courses, and used machine learning and information retrieval techniques to prototype an intelligent intervention system that automatically detects and mitigates confusion in said forums. The work was published in the 8th International Conference on Educational Data Mining.

Jan 2014 – Mar 2014 · 2 mos

• As part of Stanford's CS198 program, I lead and teach a weekly discussion session to a group of 8 to 12 students enrolled in Stanford's introductory computer science class. I coach these students throughout the quarter, grading their assignments and providing feedback in 1:1 sessions. Additionally, I hold office hours in which I help students debug their programs.

Jan 2014 – Mar 2014 · 2 mos

• As a teaching assistant for an online version of Stanford's "CS144: An Introduction to Computer Networking", offered through the university's online class portal class.stanford.edu, I fielded questions from students and helped ensure that the course functioned smoothly.

Jun 2016 – Sep 2016 · 3 mos · Mountain View

• Applied techniques from data science, machine learning, and mathematical optimization to better understand Google workloads and provided actionable recommendations on how to better provision hardware for Google data centers. Pipeline deployed in production. (Python)

Jun 2015 – Sep 2015 · 3 mos · New York City Metropolitan Area

• Devised a methodology to conduct fleet-wide analyses of distributed operations passing through the storage stack, and applied it to optimize Colossus, Google File System’s successor, and chunkservers. Developed reporting and analytics tools for investigations into tail latencies. Findings led to an estimated 6 percent reduction in disk read system calls across the fleet. All coding done in C++ and Python.

Jun 2014 – Sep 2014 · 3 mos

• Researched and implemented policies to reduce garbage collection on flash devices. Simulations on production workloads consistently reduced overhead by 50 percent, and findings resulted in changes to the production codebase. All coding done in C++.

Jun 2013 – Sep 2013 · 3 mos · Mountain View

• As an intern on the production kernel team, I designed and developed each of the following:
• • Memory allocator that raises alerts when memory is misused. It recognizes and provides safeguards against buffer overflows, forbidden memory access, and improper treatment of address alignment.
• • Memory validation tool retrieves information about a memory mapping containing a specific address for a specific process.
• • Routine that ingests sequential files while guaranteeing that parsing will occur as atomically as supported by the sequential file interface.
• • Routine that makes weighted choices based upon a probability distribution.
• I also developed an automated test suite that exercises the kernel's cryptographic key retention service, reproducing, patching, and documenting kernel vulnerabilities along the way.
• All coding was done in C.

Aug 2010 – Jun 2012 · 1 yr 10 mos · Cupertino, CA

• As staff writer (2010 – 2012), produced print and online content, including articles, pages, and multimedia. Named "Outstanding All-Around Staff Writer," and author of news article nominated for the NSPA Story of the Year. A selection of published works can be viewed here: http://www.debugmind.com/my-portfolio/journalism/
• As news editor (2011 – 2012), led production of both print and online news content. Assigned stories and deadlines, edited content, and managed news staffers.
• As webmaster (2011 – 2012), facilitated Joomla-to-Wordpress migration, redesigned and launched site, and monitored availability throughout 2011 – 2012. Site named “Best of the West” student news online publication by JEANC, Oct. 15, 2011.

Oct 2009 – May 2010 · 7 mos · San Jose, CA

• As CFO of an eBay-sponsored student-run company that manufactured, marketed, and sold duct-tape wallets, responsible for proposing business models, selling stock, allocating, approving, and tracking funds, and liquidating assets.