2019 Summer: GSOC!

This summer, I participated in Google Summer of Code (GSOC) 2019 as a mentor. GSOC is an awesome program where Google pays students stipend and have them work on open source projects under supervision of mentors.

How did I get involved? On March 8, Toby Hocking1 floated the idea of co-mentoring a student to work on XGBoost. Eager to get anyone to contribute to XGBoost, I took up on the offer.

What kind of work did we do? From May 6 to September 3, I mentored Avinash Barnwal2 to add a new objective function called Accelerated Failure Time (AFT). Well known model in the field of statistics, AFT is a popular model of choice for survival analysis, i.e. modeling time to event. See Avinash’s post for a summary.

Show me the code! See https://github.com/dmlc/xgboost/pull/4763

Personal takeaway Before GSOC, I had no idea what survival analysis is, let alone AFT. Thankfully, Avinash gave me a bunch of papers to get me up to speed. I also got to ask him many questions on the subject of survival analysis. I intend to write a series of posts to summarize what I learned this summer.

  1. Director of SICCS Machine Learning Research Laboratory, Northern Arizona University
  2. Ph.D. student at Department of Applied Mathematics and Statistics, Stony Brook University

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