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Caveat lector: This blog is where I try out new ideas. I will often be wrong, but that's the point.

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1.4.13

Why I Chose Academia


A little over a week ago I ran two panels at a conference called Beyond Academia organized by a group of UC Berkeley PhD students and post-docs.

This was a great conference particularly because this is the right time, and the Bay Area is the right place, for people with strong quantitative skills looking for other opportunities outside of academia. The startup-up culture, the high-density of exciting technical work, and the density of a highly educated populous offer a lot of options for people looking.

The desire to "jump ship" is further compounded by the terribly poor pay for post-docs and grad students. Most of our pay is set nationally by the NIH and is not adjusted for cost-of-living differences, which means that NIH-funded post-docs in San Francisco (with a median rent of $1363/mo) get paid the same as post-docs in Iowa City (with a median rent of $734/mo).

After however many years of education for a PhD my UCSF take-home pay after federal and state taxes, etc. is about $2800/mo. I'm a father; if I wanted to use UCSF daycare and live in UCSF post-doc housing I would be paying $1998/mo for daycare and at least $1099/mo for a studio. Imagine if I was a single parent? This would make my net take-home pay negative $297/mo.

Ivory Towers indeed.

You can see why this was such a highly-attended conference and why programs like Insight Data Science are exploding in popularity right now (disclosure: Jake Klamka of Insight is a friend of mine).

As some of you may know, I've done some work outside of academia with a fantastic, exciting company. My work with Uber has been fascinating: they're working on hard problems, they've got a lot of cool data for me to play with, and I really believe in the utility of the product and what it provides. Furthermore, unlike most startups they have a strong revenue stream; I was offered a lot of stock options and an incredibly high salary (for academics) to come on board.

Nevertheless, for a variety of reasons I chose to not leave academia to work with them full-time (though I continue to work with them, albeit in a different capacity now). My primary reason for staying in academia was because I love neuroscience.

Seriously, I love my job. I love science. I love discovery and uncertainty and failure and the challenges and joys that go along with it.

The decision to not join Uber full-time was done with full knowledge of just how much money I would be sacrificing (I know what their revenue graphs look like), both in terms of salary and long-term stock performance.

This is partly, of course, a decision from privilege: how many people even have the chance to not work for riches in lieu of their passions? I'm very grateful to my wife and to Uber's continued financial support that I can even get to make a decision like this. It was a very hard choice, largely because of how not-well-off my family is. They're not going to be able to retire for a long time (and not because they haven't saved enough to pay off their SUV or whatever other luxuries the US upper middle-class confuses for necessities). My father was incredulous (but supportive).

My choice was a gamble. What if I don't get the faculty job I want? Do I branch out on my own to try my hand at the start-up world?

What if all of the interesting neuroscience problems stop being in academia and move to industry? Like Ian Sommerville said in this fantastic post:
In the 1970s, there is no doubt that the most exciting work in practical computer science and software engineering was going on in universities and research labs. Working in industry mostly meant programming in FORTRAN or COBOL or doing ‘systems analysis’ – I was never very sure what that meant. Now, the challenging software engineering problems all stem from scale – dealing with vast number of computers, building systems with thousands of distributed components and so on. Universities, unfortunately, simply cannot afford to create such large-scale experimental environments and most of the leading-edge work concerned with scale has moved to companies such as Google and Amazon.
That could happen. I could be backing the wrong horse on this one, but I've made my decision for now and few decisions in life are final.

But at the end of the day I get to spend my evenings in the park with my son, work on problems very few people can, and get to work with a lot of freedom.


And I now know, down to the exact dollar, how much those freedoms are worth to me.