I strive to bring state of the art advances in machine learning out of the research lab and in to products that people love. I sit at the intersection of technical research, software engineering, and product development.
Currently, I am honing my engineering skills as an intern on Amazon’s Consumer Payments team. I am a Junior at Rensselaer Polytechnic Institute studying Computer Science and Math. I also run The Inventor’s Guild, a thirty-plus person think-tank and consulting firm that promotes entrepreneurship throughout the universities our members have attended.
My Story So Far
My First Taste of Entrepreneurship
In sophomore year of high school, I started tinkering with airsoft guns. Using knowledge gained from being on my high school’s robotics team, I created a MOSFET-based device that only cost $6 to make and improved both the safety and rate of fire in addition to enabling the use of more powerful batteries. Over the next two and a half years, it went through three major design changes and was shipped to over one thousand customers with a satisfaction rate over 95%. You can read more about my (mis)adventures starting this business, ZC Airsoft, in my blog post here.
Getting Interested in Computer Science
One potential business opportunity I explored was adding a microcontroller to the MOSFET to enable programmable functionality like 3-rd burst, rate of fire control, and cycle completion. This required me to learn more about embedded systems, so I purchased an Arduino and a copy of K&R C and got to work figuring it out. Eventually, I got a prototype to work but was beaten to the market and decided not to release it as a product. While this brief foray into programming didn’t make any business impact, I did find that I had both an interest in and a knack for coding.
However, it wasn’t until senior year when I got involved in the world of daily fantasy sports that I would seriously dive deep into computer science. Seeking an edge, I turned to aggregating statistics from ESPN in Excel to make more informed decisions. My win rate increased as a result, and I set about trying to completely automate this process. I moved on to programming custom functions for Excel, then to using R to create custom models, and finally to having a near end-to-end system in Python.
By the end of high school, I had a data scraping and munging, machine learning, and optimization platform that was making quite a bit of money in football and basketball. Unfortunately, I had to stop this in college as playing daily fantasy sports was illegal in New York.
Starting College at RPI
In my first year at Rensselaer Polytechnic Institute, I was brought on as an Inventor to The Inventor’s Guild, where I made money contracting for a few local startups, attended my first hackathons, and tried to commercialize the work I had previously done in sports analytics, though unfortunately failed. I learned, among other things, how to “hack” RPI’s curriculum, get internships, and improve myself and my technical ability.
That summer, I got a job as a Data Science Intern at Haystax Technology. During my internship, I built the first of their natural language processing tools. Using latent dirichlet allocation topic models and a convolutional neural network, I solved the problem the platform had of not being able to tell the difference between sentences like “I’m going to bomb the school.” and “I’m going to the bomb the test.” or “I’m going to shoot something.” and “I’m going to a photo shoot.” You can read more about my experience at Haystax here.
Accelerating Through Sophomore Year
A variety of opportunities came up sophomore year that greatly accelerated my development. In the fall semester, I worked on Deep Remix, which was an attempt to remix music using deep learning that while ultimately unsuccessful, enhanced my deep learning ability. In the spring semester, I was brought on as an Investor to Contrary Capital, a de-centralized university focused VC fund, as an undergraduate researcher at RPI working on a Bill and Melinda Gates Foundation-funded data visualization platform, and as a product manager to Venue, an RPI-funded platform to allow students to verify their attendance using their phones. Additionally, I worked on the Linux kernel to get it fully working on the newest Apple Macbook 12″. You can read more about that project here.
My internship this summer was with Workday’s Data Science team, SYMAN, located in San Francisco’s Financial District. I worked on the OCR (optical character recognition) team to figure out how to automate the process of expense reporting. I researched how to find the areas of text that we were interested in from a picture of the receipt. Working closely with a colleague, I designed and trained a custom fully convolutional regression network that achieved over 80% accuracy on real data as well as scaled with 256 GPUs with 88% efficiency. You can read more about my experience at Workday here.
I am always looking to get connect with interesting people! If you are in Seattle and want to get coffee, please don’t hesitate to get in touch: email@example.com
Last update was 2017-09-16.