I build software to help others. Check out my previous projects, particularly MDVax, where I helped over a quarter million people schedule COVID-19 vaccine appointments.
I enjoy cooking, training Brazilian jiu-jitsu, and learning about all things finance and markets.
MDVax was a website and Twitter alert tool that helped individuals in Maryland schedule appointments
for the crucial COVID-19 vaccination. Because of the high demand and low supply, many appointments
were initially distributed on a first-come, first-serve basis, so naturally, these appointments filled
up promptly. The solution I engineered was a web scrapping system that continuously collected data on
appointment availability and aggregated that data in one place. When appointments became available, my
tool automatically updated the website and sent out a Tweet alert.
As a result, this project garnered significant press attention from many local, state, and even
national publications because of its usefulness and the public good. Linked below are several of the
interviews and articles that have been made about this tool. Notably, the website has over
250,000 unique users and 3 million page views. Similarly, the Twitter feed has grown to over 17,000
followers, 10 million impressions, and 1 million profile visits. The growth and support I received
from this project were humbling and inspiring because of all the gratitude people have sent after
successfully using my tool. I proudly accepted donations on behalf of a few essential charities, and
we’ve raised over $20,000 for various causes.
My company, crepkitchen, was the solution to the problem of making sure customers don’t miss out
on limited-edition clothing and sneakers. It was an online subscription service that instantly
notified users of product restocks and other essential information for limited-edition sneakers and
clothing releases before they sell out. The notification then guarantees that all of the customers can
succeed during any limited-edition releases.
The idea started to help some friends and myself get our shoes faster and cheaper, which then became
the basis of a startup founded in my senior year of high school. From pitching as a finalist in front
of hundreds to the entire University of Maryland’s Pitch Dingman Competition to generating over $90k
in annual profit, crepkitchen was a journey and a pleasure to build. Through this experience of
successfully founding and running a seed-funded startup, I learned many valuable
skills like
leadership, delegation, and public speaking and furthering technical skills like machine learning and
product development.
Technical Summary:
By utilizing numerous multi-threaded scripts of web scrapers
and rotating proxies, each implemented individually for
every e-commerce store, I used the requests library from
Python to keep a database and notify customers in almost
real-time when items were available to purchase.
Further along, I encapsulated all of this technology in a
mobile IOS app built in Swift, which connected to a MongoDB
database and utilized push notifications to alert the
customers. I also engineered a customer service bot in Java
that helped customers navigate the platform using keyword
extraction.
This personal project started while I was learning more
about technical analysis of stock charts. I used some of the
strategies I learned and programmed them by creating simple
indicators using quantitative finance formulas. Soon after,
I found I had a data set of several indicating statistics on
all popular stocks, so I decided to implement them into a
machine learning algorithm.
You can read more about this project via a published
explanatory blog post I wrote below or from this project's
GitHub page. This was my first blog post and garnered a lot
of positive feedback.
Disclaimer: This is not financial advice and should be
used for educational purposes only.
Please reference the blog post and github page for further
info.
Oravise
This project, called Oravise, was a web platform
brainstormed, designed, and engineered at the University of
Maryland’s hallmark hackathon, Bitcamp, in April 2019.
Oravise was a peer to peer scholarly website connecting
individuals curious about unsolved world problems across
academic fields, including astronomy, computer science,
mathematics, medicine, and other essential subjects with
researchers in those respective fields. The website allows
users to select an academic researcher from a list given the
users chosen subject, subcategory, and unsolved problem and
connect with them. I worked with a small team to build this
project and learned everything from Flask to learning new
APIs to making my own API and much more.
Another hackathon project called Election in Tweets and was
made at Vandy Hacks 2018. My teammate and I have always been
big fans of Nate Silver’s website FiveThirtyEight where he
predicts winners of political elections. We decided one data
set not used that often was social media analysis.
To analyze every tweet to predict this election, I had to
build my own Twitter API and save every tweet. Then ran this
data set through sentiment analysis to gauge the popularity
of specific candidates. The data was then compiled, and by
each state, we graphically represented our predictions based
on the model.
Our model performed better than expected and even
outperformed FiveThirtyEight by accurately picking over 95%
of the winners based on just social media interactions.