About Me
Hi I'm Scott, a graduate of The Colorado School of Mines with a Bachelor's in Computer Science and focus in Applied Math. After my May graduation, I'm looking forward to be returning in June as a Data Engineer at iCapital in NYC on the Data & Analytics team where I interned last summer (2024).
During my education, I have learned about computer science theory and its connection to mathematical concepts. This knowledge has been incredibly useful in furthering my experience and understanding of the ML/AI field as a whole. After my internship at iCapital I gained invaluable experience contributing on a software team and architecting scalable solutions in the cloud.
Since I tend to learn best through consistent, project-based activities, I decided to deepen my AWS understanding this fall (September - December 2024) by constructing a data pipeline to collect highly granular order book data and publicly available weather measurements, with the goal of automating arbitrage trades based on my speed advantages on the daily weather prediction markets of Kalshi. This data collection system functioned as expected, and ran uninterrupted for a month, but liquidity issues on the prediction market did not present ample scale to make the system profitable, when adjusting for cloud costs. Through this project, I gained practical experience architecting fast and efficient cloud solutions, and improved my cloud development workflow by adopting infrastructure as code to create resources on AWS.
I love working on projects outside of school, and currently a friend and fellow computer science student at The Colorado School of Mines, JP Broz, and I are working on a project we are calling Muse (Multimodal Unified Search Engine). During both our professional and academic endeavors, JP and I have found that current information and document retrieval systems are expensive, require intense manual setup, and generally don't scale well as the number of files stored in a personal or corporate knowledge base (such as OneDrive or Confluence) increase. Our solution, Muse, is an AI powered tool that allows anybody to quickly, accurately, and securely search across thousands of textually and visually rich documents, PowerPoint presentations, and images, using plain English. This system dramatically reduces the time it takes to find information, maximizing user productivity. We recently released a demo, and are excited to start shipping the product to early users in the coming weeks. If you are interested in trying Muse, sign up for early access here and follow Muse on LinkedIn.
In summary, all of my seemingly unrelated projects have taught me that I am interested in many fields, especially those related to AI/ML, cloud computing, and the technology space as a whole. In addition to my passion for building software solutions, I'm a lifelong athlete. I have been playing hockey since I was 6 years old and was captain of my rowing team at The Taft School. I also enjoy playing poker, hiking, running, and reading new AI research papers. Thanks for visiting my website, and feel free to reach out via any of my socials linked above.