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Decoding Patterns of Success
Posts on Patterns of Success for Students
March 24th, 2013 · 49 comments
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:
- 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.
- 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)
March 3rd, 2013 · 31 comments
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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February 17th, 2013 · 34 comments
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:
- I started by identifying well-known professors in my particular niche of theoretical computer science.
- 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.
- 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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December 21st, 2012 · 70 comments
Getting Beyond Getting Things Done
I first read David Allen’s Getting Things Done (GTD) in 2003. I was a junior at Dartmouth and Allen’s ideas resonated at a time when my obligations were starting to overwhelm me. I committed to his system.
After a few years, by which time I was at MIT for graduate school, I found myself frustrated with the whole GTD canon and was ready to abandon it altogether.
My issue was simple: it wasn’t helping me become better.
I was good at full capture and regular review, and, accordingly, was quite organized. This was a good time in my life to ask me to submit a form or tackle a complicated logistical process. You could be confident that I would capture, process, and then accomplish it.
But I was missing the intense and obsessive wrangling with the hard problems of my field — the type of habit that made people in my line of work exceptional. My commitment to GTD had me instead systematically executing tasks, one by one, like an assembly line worker “cranking widgets” (to use a popular Allen aphorism).
I didn’t need to be cranking widgets. I needed to instead be crazily focused.
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October 26th, 2012 · 70 comments
The MIT Challenge
My friend Scott Young recently finished an astounding feat: he completed all 33 courses in MIT’s fabled computer science curriculum, from Linear Algebra to Theory of Computation, in less than one year. More importantly, he did it all on his own, watching the lectures online and evaluating himself using the actual exams. (See Scott’s FAQ page for the details of how he ran this challenge.)
That works out to around 1 course every 1.5 weeks.
As you know, I’m convinced that the ability to master complicated information quickly is crucial for building a remarkable career (see my new book as well as here and here). So, naturally, I had to ask Scott to share his secrets with us. Fortunately, he agreed.
Below is a detailed guest post, written by Scott, that drills down to the exact techniques he used (including specific examples) to pull off his MIT Challenge.
Take it away Scott…
How I Tamed MIT’s Computer Science Curriculum, By Scott Young
I’ve always been excited by the prospect of learning faster. Being good at things matters. Expertise and mastery give you the career capital to earn more money and enjoy lifestyle perks. If being good is the goal, learning is how you get there.
Despite the advantages of learning faster, most people seem reluctant to learn how to learn. Maybe it’s because we don’t believe it’s possible, that learning speed is solely the domain of good genes or talent.
While there will always be people with unfair advantages, the research shows the method you use to learn matters a lot. Deeper levels of processing and spaced repetition can, in some cases, double your efficiency. Indeed the research in deliberate practice shows us that without the right method, learning can plateau forever.
Today I want to share the strategy I used to compress the ideas from a 4-year MIT computer science curriculum down to 12 months. This strategy was honed over 33 classes, figuring out what worked and what didn’t in the method for learning faster.
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August 10th, 2012 · 47 comments
Less Than Ultra Learning
The surprisingly useful Riemann Zeta function in action. (Image from MathWorld.)
As part of my craftsman in the cubicle project, I spent this past week monitoring how I learn new information.
I wasn’t impressed.
At one point, for example, I needed to dive into a topic I didn’t know much about: how information disseminates in random power law graphs. I went to Google Scholar and begin downloading papers with promising abstracts. I printed three and skimmed another half-dozen or so online. In retrospect, I think I was hoping to find a theorem somewhere that described exactly what I was looking for in notation I already understood.
Not surprisingly, I didn’t find this magic theorem. The two hours I spent felt wasted. (Well, not completely wasted, I did learn about the Riemann Zeta function, which turns up way more often than you might expect.)
This experience recommitted me to cracking the code of ultra-learning. Mastering hard knowledge fast, I now accept, requires more than blocking aside time on a schedule; it also demands technique.
With this in mind, here was my first stab at cracking ultra-learning:
I bought a traditional leather chair (a longtime dream of mine). My wife and I still need to add some bookcases, a rich rug, and an old brass lamp — but my general theory here is that this library nook will be make it impossible to avoid mastering new bodies of knowledge, and perhaps also pipe smoking.
Under the assumption that I might need more than the power of The Chair to become an accomplished ultra-learner, I do have one more strategy to deploy — which is what I want to talk about in this post. It’s actually a strategy I’ve known for years (my PhD adviser taught me soon after my arrival at MIT), but have seemed to forgotten recently.
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August 7th, 2012 · 9 comments
A snapshot of the mind of Cal…
A reader recently noted the following:
It strikes me that you haven’t said much at all on your blog — or for that matter, in your books — about how one ‘succeeds’ at one’s personal life.
It’s true that I tend to keep things professional here at Study Hacks. But this doesn’t mean my personal life escapes similar scrutiny.
Exhibit A is the stack of moleskin notebooks shown at the top of this post.
They’re all full.
I keep one such notebook with me at all times for recording any “important thought” that I might want to revisit later during my monthly check-in. Some of the ideas, of course, relate to my work as a professor or a writer. The page below, for example, which is from September 2, 2010, shows the genesis of my Romantic Scholar series (though, at the time, I was calling it, ill-advisedly, the “aesthetics of student knowledge” series):
At least three-quarters of the notes, however, deal with living a better life outside of work. In other words, I put a lot of thought into hacking the personal — I just tend to be too private to share.
To understand my hesitance, I present Exhibit B:
This page, recorded on May 21, 2009, is one of several entries on the importance of the “500 push-ups project.” Something which I clearly deemed urgent.
August 2nd, 2012 · 36 comments
My dissertation. The pages shown here are from a proof that caused significant consternation.
A Novel Dissertation
I began working on my PhD thesis in the summer of 2008. I defended a year later, in early August, 2009.
There’s nothing unusual about this timing. What was unusual, however, was my approach.
By June 2008, I had a fair-sized collection of peer-reviewed publications. The standard practice in computer science would be for me to take the best of these results, combine them, fill in the missing details, add a thorough introduction, and then call the resulting mathematical chimera my dissertation.
To me, naive as I was, this sounded like a waste of a year. So I decided I would prove all new results.
This strategy worked fine for a while, keeping me engaged and happy, but then, in April, 2009, things took a turn toward the difficult. It was during this month that I accepted a postdoc position that would start in September. This meant that I had to defend my thesis over the summer. Suddenly the allure of tackling all new results began to wane.
Here’s a scenario that became common:
- I would be working during the day on an important proof.
- At some point in the late afternoon I would find a flaw.
- A helpful voice in my head would point out that my whole future depended on finding a fix — without a fix, it argued, the thesis would crumble, I would be kicked out of graduate school and end up homeless, likely dying in a soup kitchen knife fight.
- After heading home, I would continue, obsessively, seeking a fix — ruining any chance at relaxation that night.
After two weeks of this exercise, I decided something needed to change.
It was then that I innovated my shutdown philosophy…
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