Mathew Steininger

MDVax

MDVax.info @MDVaxAlerts

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 scraping 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.

Some Interesting Links:
CBS-WUSA9 Washington D.C. Interview
60 Minutes Feature
The Wall Street Journal
Maryland Today
Fox 5 Washington D.C. Interview
Technical.ly
Prince George's County Television
Capital Gazette

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crepkitchen

My startup, crepkitchen, was the solution to the constant issue of customers missing out on limited-edition clothing and sneakers. We created an online subscription service that instantly notifies users of product restocks and other essential information for limited-edition sneakers and clothing releases before they sell out. The notification then allows the customers to succeed during limited-edition releases to either flip the item for a profit or collect it.

The idea started to help some friends and myself get our shoes faster and cheaper, which became the basis of the 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 meeting with angel investors, crepkitchen was my first glimpse into the startup world, which I am eager to explore more of in the future.

Metrics:
• $15,000 MRR (monthly recurring revenue)
• 300 MAU (monthly active users)

Some Interesting Links:
Interview with the University of Maryland's Dingman Center for Entrepreneurship at the Robert H. Smith School of Business
Press Report from the Pitch Dingman Competition
2019 Pitch Dingman Competition Finals
Archive of Company Website

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Machine Learning Stock Predictions

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.

Some Interesting Links:
Blog Post of this Project
GitHub Project Page

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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.

Link:
Devpost

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Election in Tweets

Another hackathon project, Election in Tweets, 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.

Link:
Devpost

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