Last year I started having delusions of grad school. By delusions I mean that on paper, in the numeric and text fields that actually make it into the application, I looked utterly unqualified to do machine learning research, let alone at a top school. I had no computer science publications, no masters degree, and a 3.3 GPA, in materials engineering. I was a self-taught washed up startup founder six years out of school with no advisor and three months work experience in my proposed field of NLP. Only two people had ever seen me write code; one was my current boss and the other was my ex-cofounder, who also happened to be my brother, so I couldn’t even scrape together a single letter of recommendation attesting to my programming talents. I did take a programming class once, CSE142: Intro to Computer Programming I, where I got a C. But next month I’ll be starting my PhD studies at Columbia advised by Zhou Yu and Luis Gravano. They’re really really good at what they do and there’s no school I’d rather be at. This is the story of how I leveraged the National Science Foundation Graduate Research Fellowship Program (NSF GRFP) to get my unqualified ass into grad school. The technical guide follows.
In June of 2021 I forgot to register my +1 at a wedding and got placed next to a cool dude named Logan who told me just to shoot my shot on PhD applications even though I didn’t feel qualified. After all, they’re kind of a crap shoot anyway. Lacking any proper mentorship I took this advice at face value and spent the rest of the summer reading papers and the autumn drinking cappuccinos and banging out applications at Amy’s Merkato and Fort St. George after work. At the end of the year I fired off my freshly minted applications and swapped caffeine for alcohol while the responses rolled in.