Study Hacks

Seeking Full Capacity

Since becoming a professor, my productivity (as measured by original publications in quality venues) has improved.

I’m happy about this fact.

But I’m also convinced that I’m still leaving capacity on the table. As my expertise in my area grows, I’m reaching a point where I have more ideas per year than I have time to publish (which can be frustrating). If I could increase my deep to shallow work ratio just a little more, I could, I think, close that gap.

Accomplishing this goal, however, has proved difficult.

According to my Monthly Plan archives, since September 2012 I’ve launched at least six different plans aimed at increasing my research output, with the goal of closing this final gap.

None made a major impact.

With this in mind, I’m taking advantage of the beginning of summer to try, as I like to do every now and again, the most radical of productivity plans — no plan at all.

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The Remarkably Relaxed

Terence Tao is one of the world’s best mathematicians. He won a Fields Medal when he was 31. He is, we can agree, remarkable.

He is not, however, busy.

I should be careful about definitions. By “busy,” I mean a schedule packed with non-optional professional responsibilities.

My evidence that Tao is not overwhelmed by such obligations is the time he spends on non-obligatory, non-time sensitive hobbies. In particular, his blog.

Since the new year, he’s written nine long posts, full of mathematical equations and fun titles, like “Matrix identities as derivatives of determinant identities.” His most recent post is 3700 words long! And that’s a normal length.

As a professor who also blogs, I know that posts are something you do only when you have down time. I conjecture, therefore, that Tao’s large volume of posting implies he enjoys a large amount of down time in his professional life.

Here’s why you should care: Tao’s downtime is not an aberration — a quirk of a quirky prodigy — it is instead, I argue, essential to his success.

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The Productive Professor

I’m fascinated by people who produce a large volume of valuable output. Motivated by this interest, I recently setup a conversation with a hot shot young professor who rose quickly in his field.

I asked him about his work habits.

Though his answer was detailed — he had obviously put great thought into these issues — there was one strategy that caught my attention: he confines his deep work to long, uninterrupted bursts.

On small time scales, this means each day is either completely dedicated to a single deep work task, or is left open to deal with all the  e-mail and meetings and revisions that also define academic life.

If he’s going to write a paper, for example, he puts aside two days, and does nothing else, emerging from his immersion with a completed first draft.

If he’s going to instead deal with requests and logistics, he’ll spend the whole day doing so.

On longer time scales, his schedule echoes this immersion strategy. He teaches all three of his courses during the fall. He can, therefore, dedicate the entire semester to two main goals: teaching his courses and conceiving/discussing potential research ideas (the teaching often stimulates new ideas as it forces him to review the key ideas and techniques in his field).

Then, in the spring and summer that follow, he attacks his new research projects with the burst strategy mentioned above, turning out 1 – 2 papers every 2 months. (He aims for — and achieves — around 6 major papers a year.)

Notice, this immersion approach to deep work is different than the more common approach of  integrating a couple hours of deep work into most days of your schedule, which we can call the chain approach, in honor of Seinfeld’s “don’t break the chain” advice (which I have previously cast some doubt on in the context of writing).

There are two reasons why deep immersion might work better than chaining:

1. It reduces overhead. When you put aside only a couple hours to go deep on a problem, you lose a fair fraction of this time to remembering where you left off and getting your mind ready to concentrate. It’s also easy, when the required time is short, to fall into the least minimal progress trap, where you do just enough thinking that you can avoid breaking your deep work chain, but end up making little real progress. When you focus on a specific deep work goal for 10 – 15 hours, on the other hand, you pay the overhead cost just once, and it’s impossible to get away with minimal progress. In other words, two days immersed in deep work might produce more results than two months of scheduling an hour a day for such efforts.
2. It better matches our rhythms. There’s an increasing understanding that the human body works in cycles. Some parts of the week/month/year are better for certain types of work than others. This professor’s approach of spending the fall thinking and discussing ideas, and then the spring and summer actually executing, probably yields better results than trying to mix everything together throughout the whole year. During the fall, he rests the part of his mind required to tease out and write up results. During the spring and summer he rests the part of his mind responsible for having original thoughts and making new connections. (See Douglas Rushkoff’s recent writing for more on these ideas).

I’m intrigued by the deep immersion approach to deep work mainly because I don’t usually apply it, but tend to generate more results when I do. I’m also intrigued by its ancillary consequences. If immersion is optimal for deep work, for example, do weekly research meetings make sense? When you check in weekly on a long term project, it’s easy to fall into a minimal progress trap and watch whole semesters pass with little results. What if, instead, weekly meetings were replaced with occasionally taking a couple days to do nothing but try to make real progress on the problem? Even doing this just a few times a semester might produce better results than checking in every week.

I don’t know the answers here, but the implications are interesting enough to keep the immersion strategy on my productivity radar.

(Photo by moriza)

A Deep Day

I’m a big supporter of deep work. People often ask, however, how to fit this type of persistent concentration into a fractured knowledge work schedule.

To demonstrate my personal answer, I took a snapshot of my calendar from Monday (see the image to the right).

At 9:30, I began my commute, having already tackled enough small logistics to clear my head and allow me to start obsessing on a problem I’m trying to solve (I love thinking in the car). Once I arrived on campus at 10:00, I continued to obsess about this problem until an 11:00 meeting. I then had 2 more hours to obsess. At 2:00, I had another call. Then at 3:00, now mentally exhausted, I turned to a less cognitively demanding logistical task that I’m chipping away at, bit by bit, with the goal of avoiding a schedule-busting scramble the day before the deadline.

(I should note that I teach on Tuesday and Thursday, and, accordingly, devote those full days to class related work — which is why you don’t see such tasks on the sample Monday shown here.)

Here’s the take-away message: On non-teaching days I start with the assumption that the full day will be dedicated to thinking deeply on the projects that will best increase my career capital. I then (only reluctantly) squeeze in the other stuff that simply cannot be ignored. Because I assume the day is mainly about deep work, I tend to ruthlessly batch this extra stuff and push it toward the borders of my day, where it will have a minimal effect on what matters.

The Emersonian Doctoral Candidate

I’m flying down to Duke on Tuesday to speak with their graduate students. Preparing for the event inspired me to reflect on my own student experience. In doing so, an Emerson quote came to mind:

“To different minds, the same world is a hell, and a heaven”

Emerson does a good job of capturing the reality of a research-oriented graduate education. Even though students enter such programs — especially at top schools — strikingly homogenous, in terms of their educational backgrounds and achievements, after a few years, the group tends to radically bifurcate.

Some students love the experience and thrive. They dread the possibility that they might have to one day leave academia and take a “normal job.” To them, graduate school is Emerson’s heaven.

Other students hate the experience and wilt. They complain about their advisors, and their peers, and the school, and their busyness. They can’t wait to return to a “normal job.” To them, graduate school is Emerson’s hell.

I began to notice this split about halfway though my time at MIT. I loved graduate school, so I was mildly surprised, at first, to encounter cynical students secretly plotting to abandon ship after earning their masters degree, or to stumble into dark blogs with titles such as, appropriately enough, Dissertation Hell (” a place to rant…about the tortures of writing a dissertation”).

Why do such similar students end up with such different experiences?

Because I happened to be a professional advice writer at the same that I was a student, I studied the issue. I think the answers I found are important to our broader discussion because this Emersonian division is common in many professions, and understanding its cause helps us better understand the complicated task of building a compelling career and the pitfalls to avoid.

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Ambition as an Art Form

I’m fascinated by people who accomplish things of importance. I’m also fascinated by how little we understand this process.

Traditional career thinking, of course, says you must identify your passion then aggressively pursue it. As you know, I have little patience for such childish reductionism.

When we start thinking about our career aspirations like adults, and ask hard questions, the answers tend to be more complex.

When I studied this issue in the context of academia, for example, I found instead that famous researchers often had surprisingly subtle — and well-developed — strategies for pursuing important results.

Consider Richard Feynman and Richard Hamming. Both of these stars talk about a robust process in which they systematically built up collections of open problems, and then, over time, tested out new techniques against these problems, always sifting for a match. This approach required a careful balance between seeking new knowledge and working with what they already knew. I suspect they dedicated a lot of thought to tuning this balance.

The broader point here is that ambition is good. But it’s not simple.

At some point, you have to turn your attention from the advice of commentators whose main credential is success in providing advice, and actually steep yourself in the nuance of how people make remarkable things happen in your field.  I am increasingly convinced that this apprenticeship, which can be long and often ambiguous, is a necessary stepping stone on the path to big things.

An Interesting Experiment

 How do people succeed in academia?

I have notebooks filled with theories about this question, but I’ve increasingly come to realize that insights of this type — built on gut instinct, not data — are close to worthless. Most knowledge work fields are complex. Breaking into their upper levels requires a deliberate effort and precision that is poorly matched to the blunt, feel-good plans we devise in bouts of blog-inspired reflection.

This was on my mind when, earlier this week, I went seeking empirical insight into the above prompt, and ended up designing a simple  experiment:

1. I started by identifying well-known professors in my particular niche of theoretical computer science.
2. For each such professor, I studied their former graduate students. I was looking for pairs of students who earned their PhD around the same time and went on to research positions, but then experienced markedly different levels of success in the field.
3. Once I had identified such a pair, I studied the first four years of their CVs — the crucial pre-tenure period — measuring the following variables: quantity of publications, venue of publications, and citation of published work in the period.

Each such pair provided an example of a successful and non-successful early academic career. Because both students in a pair had the same adviser and graduated around the same time, I could control for variables that are largely outside the control of a graduate student, but that can have a huge impact on their eventual success, including: school connections, quality of research group, and the value of the adviser’s research focus.

The difference in each pair’s performance, therefore, should be due to differences in their own strategy once they graduated. It was these strategy nuances I wanted to understand better.

Here’s what I found…

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A Useful Rejection

Earlier in my career, I submitted a grant proposal to a prestigious program. At the time, I had an interesting research idea bouncing around, so when I saw the deadline approach, I hunkered down for a week of deep work to pull together a submission.

A few months later, while at a workshop, I met the head of the funding program. I told her about my proposal.

“I assume,” she said, “that you had the very best people in your field read it over first.”

I had not.

Soon after this encounter, I received my rejection notification.

Evidence-Based Betting

Reflecting on this experience, I now notice that in my rush to embrace deep work and purposeful bets I had overlooked a more prosaic piece of the puzzle: learning the rules that govern the area where I was making my play.

If I had followed the program director’s advice and pumped experts for feedback, I would have learned about what you absolutely need for a fundable proposal. I avoided this step, I think, because some part of me didn’t want these answers. By writing my grant in isolation, I could ensure an optimal experience, where I had to put in focused hours, but never really challenge myself too much.

This was fulfilling. But it was also a recipe for failure.

I was like the amateur runner who spends her training days doing hard (but not too hard) three mile jogs instead of the brutal interval work she really needs to improve.

I don’t think we emphasize enough the importance of evidenced-based metrics. Deep work is important. Making lots of bets is important. But if these efforts are not grounded in the reality of your field — including the hard truths about what you really do need to potentially succeed, not just what you know how to do — they are wasted.