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Tim Hwang
Tim Hwang

Posted on • Updated on • Originally published at

Lessons I've learned as a grad school dropout

I dropped out of grad school in December 2015 after two and a half grueling years. I left with practically nothing -- no first author publications, no degree, not even a single completed research project under my name. I hated my life so much that to me, losing a quarter of my twenties was a small price to pay for escape.

Roughly half a year later, I was starting my first day as a "real, professional software engineer." It's been almost four years since then, and I've seen some success in my new environment -- much, much more so than in academia, at least! The further I get from grad school, however, the more I'm able to objectively view my time there. In retrospect, it wasn't a waste at all: I've learned several invaluable things during my time in academia that continue to help me move forwards today.

Learning how to read

It didn't take me long to realize that for my entire life prior to grad school, I had never learned to properly read. While I don't consider myself to be particularly smart, I've always been a (comparatively) fast reader. I was skilled at identifying and extracting nuggets of important information from textbooks. This made me an efficient studier, so I did well on tests throughout school and college.

This model of learning quickly reached its limit in grad school. Research articles are incredibly information-dense such that for the most part, every sentence is important, and simply extracting nuggets is not enough. This is also quickly impressed into you by the fact that unlike in college, all papers have a page maximum instead of a page minimum. In addition, the sheer volume of reading that has to be done is several orders of magnitude higher than in college: if you are taking four courses, and each requires five articles per class, and each article is between 10 and 30 pages of minuscule text, you're basically consuming a textbook of highly compressed information a month. Reading is not a sprint, or a timed game of hide and seek. It is a slow, grueling, deliberate marathon. Once you are doing your own research, it's even worse: instead of having a handful of targeted articles prescribed to you, you have to trawl the entire ocean of prior research for the right articles.

When I became a junior engineer, I was stunned by the resistance many seemed to have against reading documentation. "It's too long. It takes too much time to find the information I need. I don't want to waste time going in circles." It was common to see people reaching for a more senior engineer the moment a roadblock was encountered (or worse, settling for a clearly insufficient solution). Even at that time, I strongly believed that the role of a junior wasn't to be as productive as possible or to produce the cleanest work possible, it was simply to learn as much as possible, as quickly as possible. I think I read the React documentation in its entirety at least once a month in my first six months. This was enough to set me apart from my peers and even some of my seniors in terms of subject matter expertise. So much information is readily available, with the only obstacle being volume.

To me, asking a senior and getting a single, targeted answer to a specific problem when a complete guidebook is readily available is akin to choosing a single nugget of insight over an untapped gold mine. The extra minutes or hours (or days, even) I'd spend wrangling with a problem on my own terms? A small price to pay.

Learning how to measure

I'll be frank. I remember very little from the thousands of pages I've read in grad school. I don't think I could give you five hypotheses, methods, and conclusions. I hardly even remember what classes I took. One thing I do remember is the endless debates with my advisor and lab mates about what to measure to support our hypotheses. It's important to measure the right thing.

My field (industrial and organizational psychology) was somewhat unique in its application of abstract psychological principles to real organizations. Unlike in, say, neuroscience, I/O psychologists don't tend to measure psychological phenomena directly. We don't measure stress by swabbing cheeks, or attention by attaching electrodes to people's scalps, or motivation by doing an MRI. This is simply intractable at the organizational scale. Instead, we are constrained to picking good proxy variables, or measurable variables that can act as sufficient substitutes for unmeasurable ones. (You could say that biological phenomena are ultimately proxy variables as well, despite there being fewer layers of indirection.) When planning a study, it was always a laborious back-and-forth to argue about what real-world outcomes mapped to what psychological principles, how this mapping could be established, and how to actually measure this.

There is a parallel in engineering management. While we can directly measure engineering results by setting monitoring infrastructure, logging, and health checks, any attempt to do the same for productivity and motivation must use a proxy variable. Complicating the issue further is the fact that prior findings in psychological and management science often don't apply well to engineering organizations (especially young ones). The solutions for an organization are often dependent on the unique constraints and resources available to that organization. There is simply too much individual difference in startups. (Though, for the record, I think the higher level the finding, the more generally applicable it is. Conclusions about motivation, stress, and burnout are more universal than conclusions about leadership, performance measurement, and organizational change.)

My paltry 2.5 years studying organization psychology are nowhere near enough to have answers for the management challenges engineering organizations are facing. But they are more than enough to help me realize that these problems are anything but trivial, that anyone who claims to have a one-size-fits-all solution to this challenge is either misguided or has an agenda, that the effectiveness of managers is organization dependent, and that the manager that fits your organization is worth their weight in gold. It's hard as hell to know what to measure, and the answer can be different for every organization. But by knowing how to measure, we can slowly get to the what to measure that is unique to us.

Learning how to be content

The last is the most personal. Complaining around the water cooler or breakfast area is one of the most common tropes in office culture. It was especially prevalent at my first job (and sometimes went on until it was nearly lunchtime), and I've lost many hours of my life listening to people airing out their grievances about anything and everything, from the (lack of) leadership to the dwindling office amenities to the stagnant wages to the workload. They complained about how their old colleague was living it up at that big company in the Bay, and oh, how nice it must be.

And the whole, time, a single thought would resonate through my mind. FUCK OFF!!! No matter how bad my job got (and it got fairly toxic at the end), it never got anywhere near how insanely difficult life in grad school was! I loathed living with four roommates. I loathed $1.39/lb chicken thigh from Walmart. I loathed seeing my friends with jobs enjoying their early twenties, trading their cheap hobbies like basketball for expensive ones like snowboarding. I loathed being seen as some kind of noble 21st-century ascetic, foregoing a comfortable life in the pursuit of knowledge. I loathed it all, and threw my academic life in the garbage the instant I had the chance.

What did I trade it for? Certainly not for a job in a gleaming tower where they give out RSUs like candy. I don't have nap pods, and I don't have steak and sushi flown into the office overnight cross country for lunch. People haven't ever heard of the companies I've worked for. But you know what? As a candidate with zero professional engineering experience, I was getting offers that were 500% of my grad student stipend. Workload? As an engineer, you're considered a workaholic if you work on weekends; in grad school, take weekends off and you die. Perks? Okay, the health insurance is less than stellar compared to what students get, but otherwise they're incomparable.

This isn't to discredit the individual struggles tech workers might face, or to say that the academic lifestyle is normal and not utterly unsustainable. However, to me it's incredibly important to maintain a sense of perspective. The five most valuable companies in the US are tech companies, and they're not shy about showing it. In the struggle to keep up with the Bezoses, it's easy to forget how easy tech workers have it overall. My wife is still in grad school and I live in a college town, so much of my friend group is still tied to academia in one way or another. I see flashes of the struggles I left behind, and it often fills me with guilt seeing how easy my life is in comparison. And it's not pity: ultimately, it's a fear of going back. What I have now, with all its flaws and day-to-day frustrations, is enough.

It's enough.

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