Akaash Kambath

Alumni (5th Year MIDS 2021)


Data Science
Software Development


Hi! I'm a 5th Year MIDS student who graduated in Summer 2021. My current interests include Data Ethics and Tennis Data Analytics. I'm currently a Software Engineer at MongoDB, and graduated from Cal with a Bachelor of Science in Electrical Engineering and Computer Science and a minor in Data Science.

Here are a couple of my favorite projects that I've worked on in recent months:

  • Capstone project: Modeled and mapped post-September 2021 eviction rates in California at the census tract level. This was motivated by the expiring eviction moratorium set in place in 2020 due to the pandemic, coupled with a lack of validated and accurate eviction data. The motivation for this project was to provide transparency and insight on which neighborhoods and communities are most at-risk in California once the eviction moratorium expires in September 2021. We hope that our findings can help individuals such as state legislators divert resources and public assistance programs to the neighborhoods that will need them. This project was a finalist for the 2021 5th Year MIDS Capstone Award and was published in U.C. Berkeley's D-Lab newsletter. We worked with U.C. Berkeley's Urban Displacement Project while completing this.


  • Using NLP techniques, developed a model which, given a cardiovascular disease related post from either the r/AskMen or r/AskWomen subreddits, classifies whether the post came from r/AskMen or r/AskWomen. The motivation for this was to build off of work done by researchers at UCSF, who recently published work concluding that women experienced 1.5x delays in treatment for cardiovascular disease compared to men. They hypothesized that these delays may be attributed to differences in how women and men describe their symptoms of cardiovascular disease. We examined the model's features to uncover the most impactful words that were being used by the model to predict which subreddit a post originated from. Doing so enabled us to gain insight on the differences between the descriptions of cardiovascular disease made by men and women.