I work as full stack developer on Redfin's search experience, helping a user learn everything they could ever want to know about a home. It's our website's most highly trafficked page so I have gotten experience with backend and front end performance, user focused design, and extensive API and data flow services. I love that what I do directly affects users and that I make the home buying process easier, fairer, and more transparent through my work.

I've worked across the stack. I was the lead engineer on our complete revamp of our UI design library leading to 14% performance improvements and significant increase in user engagement and downstream core metrics. I write clean, secure, and performant APIs for my team, our Apps teams, and other internal users of the data we own. I create data services with low latency and high consistency using Kafka, AWS, and Postgres databases, specifically used to poopulate our home data across our website with the most up-to-date information.

My favorite feature projects have been re-vamping our schools section by working around spotty data to give users a full picture of their school options, adding recommendations to the details page so no one misses a home that may be right for them, and integrating flood data onto our website and maps to help raise transparency about homes especially as climate change affects home-buying decisions.

I lead an 8 week long course acclimating freshman to engineering at Illinois with a focus on the opportunities Illinois and the CS department can provide. I am a resource for the students and a mentor they can always turn to for professional, academic, and personal help.

I built a fully functionally prototype that integrates social media into Mixed Reality. I pulled data from Facebook and LinkedIn APIs to populate a database and queried it to populated a UI that I built in Unity for the purpose of bringing people together in VR and AR and providing context to these social interactions.

I built an article classifier for Medium blog posts that identifies what topic the article is about. All the user has to do is copy and paste in the url and it outputs the topic and the confidence level of the categorization.

The business goal is to identify articles a certain person would like based on a variety of interests. The model currently uses a Naive Bayes classifier and I'm now working on making it more complex to include the article's level of professionalism, indicators of level of interest as well as other factors that make it a good recommendation.

4Degrees is a startup that is a part of Techstars in Chicago that aims democratize the networking process. I've had such a wonderful experience learning from all the startups in the accelerator as well as hearing amazing advice from startup veterans.