Hello! My name is Kathir and I'm an engineer at Stripe. I spent my entire life in the Bay Area until I went to college in Los Angeles, and I'm currently living in Seattle. If COVID-19 did one good thing for me, it was giving me so much free time to the point that I decided to build this website from scratch; I hope you enjoy!
I started my career entirely focused on cloud computing and distributed systems, working on a team in SharePoint Online that was focused on building world class compute capacity management for SharePoint's massive amount of customer traffic. After a few years, I decided to make the move to Stripe, where I currently work on edge infrastructure. On top of that, living in Seattle has been the best move for my life that I never expected. The city works for my personality and I truly fit in here!
Outside of work, my favorite activities include hiking, watching movies, reading, weightlifting, photography, and playing/watching sports. I'm obsessed with the outdoors; I'm always looking for the next adventure. My strongest belief in life is that if I have a finite amount of time on this planet, I have no reason to not explore every inch of it that I possibly can before my time is up. And I plan to do exactly that.
This project looks at advanced NFL receiver data and performs a Principal Component Analysis and K-means Clustering to create a model that classifies the receivers into various groups based on their numbers. My idea was to be able to use this to better understand the tiers of modern receivers in the NFL and how they differ from the traditional roles that have been around for years.
Defense-adjusted Value Over Average is a football efficiency metric that compares the situational success of a given play to league-average results in that situation. This project scrapes DVOA data from the 1985 NFL season to the most recent season and creates a regression model that predicts the number of regular season games a team will win based on its team DVOA numbers.
View CodeThis article looks at the structure of the men's professional tennis circuit and takes a statistical approach to determine the what it takes to become the World Number 1 tennis player. With my buddy Kevin, I analyze the careers of the Big 3, their tendencies in tournament scheduling, and how they have optimized their ATP rankings through efficient planning and performance. We then use this information to assess the tennis landscape and what ultimately is required to be the best tennis player on Earth.
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