lunes, 20 de agosto de 2012

How to Become a Star Grad Student: James McLurkin and the Power of Stretch Churn

ORIGINAL: CalNewport

The Famous Dr. McLurkin
In 2008, when James McLurkin graduated with a PhD in Computer Science from MIT, he was unquestionably a star. Four years earlier, TimeMagazine profiled James and his research on swarm robotics as part of their Innovators series. The next year, he was featured on an episode of NovaScience NOW. The producer of the show, WGBH in Boston, built an interactive web site dedicated to James, where, among other activities, you can watch a photo slide show of his life and find out what he carries in his backpack. Earlier this year, TheGrio, a popular African American-focused news portal, named James one of their 100History Makers in the Making — a list that also includes Oprah Winfrey and Newark, NJ mayor Cory Booker.

Perhaps most telling, even my brother, who finished his systems engineering degree in 2002, knew of James. “He’s the guy with the robots,” he recalled. “We watched a video of him in class.

In other words, James is famous in his field. So it’s not surprising that in 2009 he landed a professorship at Rice University — one of the country’s top engineering schools — in one of the worst academic job market in decades.

With these accomplishments in mind, this post asks two simple questions: How did James become such a star? And what lessons can we apply to our own quest to become remarkable?

The answers, as you’ll soon encounter, are not what you might first expect

A Star is Born

The direct source of James’ stardom is obvious. In 1994, as part of his senior thesis project at MIT, he designed a swarm of a dozen microrobots he called Ants. Inspired by the insect of the same name, the devices produced complex behavior — such as playing capture the flag — using only simple rules.

To call this swarm a breakthrough risks understatement. It wasn’t an advance, it was a leap. In the early 90′s, roboticists were just starting to discuss the potential of swarming groups of robots; no one was building fully functioning and autonomous swarms.

Our group at MIT was way out in front,” James recalls. “Carnegie Mellon had some work moving in that direction, but that’s it; no one in Europe at the time, for example, was even thinking about it.

When James published the paper documenting the project, it sparked a sensation that spread beyond the robotics community. James and his Ants were featured on Good Morning America. Magazines such as Discover, Omni, Popular Mechanics, and Business Week featured the project. The Pulitzer-prize winning humorist Dave Barry satirized the mini-robots in one of his widely-syndicated columns.

James became a star in the field.
After graduating from MIT, he stayed on for two years as researcher and lecturer — an honor considering his lack of any graduate degree at this point — before heading west to receive a masters from Berkeley. He returned to Massachusetts in 1999, earning his PhD at MIT while working on the side with the Bedford-based iRobot Corporation.

With the support of iRobot, which was impressed with his work on the Ants, James upped the ante again, designing a swarm of over 100, tissue box-sized robots with many more capabilities than his Ants. These new robots could form structures, explore rooms, and even coordinate perform a beep-based orchestra. No other robotics researcher had a deployment that could rival the size or complexity of James’ new swarm.

Once again, the media turned their spotlight on young engineer: generating the stories highlighted in this post’s introduction. The impact of this work made James’ path to professorship frictionless. Who wouldn’t want to hire one of the world’s most accomplished and well-known roboticists?

Decoding the McLurkin Factor
Steve Martin famously described the key to fame as: “be so good they can’t ignore you.” James confirms Martin’s axiom. By building two robot swarms that were an order of magnitude more complex than any that existed at the time or since, well-deserved stardom followed. But what lessons can we extract from his path to excellence?

A common reaction to James’ story is to emphasize the importance of thinking big. To borrow a phrase from Jim Collins, if you don’t have “Big Hairy Audacious Goals,” you can’t accomplish amazing things. According to this view, the core of the McLurkin Factor was his willingness, as an MIT senior, to think big — conceiving and executing the almost impossibly-ambitious Ants project.

This message resonates: it’s simple and it provides a satisfying little burst of enthusiasm. Not surprisingly, you encounter it often in the success literature. For example:

In his history of modern American food culture, The United States of Arugula, David Kamp cites Emeril Lagasse reading The Magic of Thinking Big to spark the growth of his food empire.

The final chapter of Jason Fried and David Hansson’s much hyped (but disappointingly generic) advice-guide, Rework, concludes: “If you want to do something, you’ve got to do it now. You can’t just say you’ll do it later. Later you won’t be pumped up…

Steve Pavlina, another fan of audacious goals, notes: “most people underestimate what goals are truly ‘realistic’ for them,” preventing them from taking the “dice roll” needed to win big.

And so on.

But is this good advice for the aspiring grad student? James is skeptical…

Beyond Audacious Goals

We never viewed the Ants project as a major jump,” James told me. “If anything, with this project, we were dialing back…our goal was to simplify greatly.

To understand this modesty, you must understand Rod Brooks’ robotics laboratory at MIT. During the mid-90′s, the lab was leading a revolution in robotics — moving the focus away from hulking, C3PO-style androids, and towards smaller, replaceable, biologically-inspired devices.

Next door to James’ office in the lab was Maja Mataric, now the head of USC’s Center for Robotics and Embedded Systems, who, during the 90′s, was a leading thinker on robotic swarms. (During this time, Mataric was writing papers with titles such as Coordination and Learning in Multi-Robot Systems.) James’ undergraduate supervisor was Anita Flynn, now the president of MicroPropolsion Inc., who, during the Ants-period, was shrinking the size of electronic motors — enabling the micro-robot revolution. (She’s well-known for building the world’s smallest robot, which at 10 mg is roughly the weight of a dozen grains of sand.)

James quickly integrated this knowledge into his repertoire of skills.

I went to the lab as an undergrad to interview for a position,” he recalls. “Anita told me they’re not hiring. So I came back with some robots I had built, and some I was halfway through building, and she said, ‘okay, you can work in the lab, and use our parts, but we can’t pay you.’

Once in the lab, James moved through project after project, under the careful supervision of Flynn, each expanding his abilities.

I had friends call me in the lab, in the middle of the night, and say: ‘you have to go out and do something,’” James recalls. “To have an MIT student say you have no life, that’s a problem. But I was having so much fun.

By the time he conceived of the Ants project for his thesis, James was an accomplished robot engineer with a number of successful projects under his belt. He also had a cutting-edge knowledge of microrobotics, and was “marinating” in a lab environment obsessed with biologically-inspired systems.

With this in mind, the idea of building a robot swarm that behaves like insects was not a big hairy audacious goal at MIT in the mid-90′s, it was water cooler conversation. And it made perfect sense that James — with his advanced robotics skills and enthusiasm to see projects to completion — took on the challenge.

The Bleeding Edge
James’ story is not unique in the annals of science. As Robert Weisberg, a psychologist at Temple University, points out in his book Creativity, great scientific discoveries are rarely the result of  “inspiration that blesses [only] geniuses.They tend instead to be advances made by individuals at the bleeding-edge of knowledge and technique in their field. To the progenitors of breakthroughs, the ideas often seem obvious and incremental. To those without their level of expertise, however, they can seem miraculous.

In Weisberg’s retelling of the discovery of the DNA double-helix, for example, Watson and Crick didn’t win the race to decode DNA’s structure because they were brilliant. Instead, it was because they had mastered the brand new technique of x-ray crystallography (which is used to probe the structure of molecules) and had recent experience decoding the structure of a protein from a tobacco virus that had properties similar to DNA.

Like Watson and Crick, James’ bleeding edge knowledge made his big break seem obvious.

Let’s unwind the implications:
To become a star, in graduate school or elsewhere, you need to make an important advance in your field.
Important advances require bleeding-edge expertise. (Once this expertise is gained, however, the breakthrough itself will probably seem obvious.)
Therefore: To become a star, you should focus on getting to the bleeding edge of your field as quickly as possible.

This last point is more difficult than it might seem. Many graduate students, for example, never arrive at the bleeding edge of their field. Instead, they reach a comfortable level of knowledge — enough to understand relevant research, and make their own acceptably-complex contributions, but not enough to make bold advances.

Put another way: Thousands of chemists could understand Watson and Crick’s 1953 paper on the double helix, but only a handful had the knowledge needed to have discovered it for themselves.

(Note: The idea of a comfortable level of knowledge is similar to Anders Ericsson’s notion of an “acceptable plateau” of ability where most people stall if they don’t deliberately push their skills forward.)

This motivates an obvious question: How do you get to the bleeding edge?

Returning to James’ story, we find a compelling answer…

The Power of Stretch Churn

Every semester, my supervisor, Anita [Flynn], had me write out goals,” James told me. “We would go back at the end of the semester and look at what I did and didn’t do. She would tell me, ‘it’s fine that you didn’t get this all done, but what’s not fine is your inability to estimate how long something will take.’

James describes this lesson as perhaps the most valuable he learned as an undergrad at MIT. Under the tutelage of his supervisor, he honed his ability to choose projects that were hard enough to stretch his ability, but still reasonable enough that he could complete them. She wanted him to be ambitious and set big goals, but she had no tolerance for goals so big that they were beyond his ability to finish in a reasonable time frame.

This should sound familiar. The type of stretch project James describes provide a perfect match with the theory of deliberate practice.

In a 2003 study of deliberate practice and sports stars, for example, researchers Janice Deakin and Stephen Cobley noted that elite figure skaters spent most of their practice time on jumps — one of the most difficult elements of their routines — while “second tier” skaters spent more time on the easier, more familiar elements of their routines.

James’ stretch projects are like figure skating jumps: they’re hard and uncomfortable, but completing them is the key to getting better.

With this in mind, I argue that the secret to James McLurkin’s success is his ability to choose the right projects. By resisting work that reinforced what he’s comfortable with, yet also sidestepping overly-ambitious projects, he consistently advanced his skill until he arrived at the bleeding edge of research robotics. Once there, the “breakthrough” projects that cemented his reputation became obvious next steps.

Put another way: stretch projects are an effective way to integrate deliberate practice into fields without clear competitive structures and coaching. If you’re a figure skater, a top coach can walk you through the hard jumps you need to get better. If you’re a grad student (or entrepreneur, writer, or knowledge worker), however, there are no such coaches to guide you through this process.

Stretch projects can fill this role.

To make this more concrete, let me give you a couple definitions:
  • Stretch Project: A project that requires a skill you don’t have at the outset.
  • Stretch Churn: The number of stretch projects you complete per unit of time
If you’re interested in building a rare and valuable skill in your field, ask yourself a simple question: What’s my stretch churn?

James’ value was off the charts.
To give you another example, in my own recent efforts to push out onto the bleeding edge of systems research on wireless networking (a shift from my grad student work on the theory side), I’ve fostered an obsession with my stretch churn. It’s tempting to fall back on the skills I’m comfortable with (i.e., “I’ll handle the theory, you guys figure out if it works”), and it’s equally tempting to try to change the field in one quixotic swoop (i.e., “Let’s revolutionize wireless broadcast!”), but neither would advance my knowledge, and I desperately want to get to the bleeding edge where the real advances are made.

With this in mind, I’ve spent the past few months in a constant state of discomfort — obsessing over channel coherence times and hacking complex wireless network simulators, among other decidedly non-theoretical diversions — and have been loving it: with each stretch project complete, I feel myself growing more knowledgeable.

(In a recent bid to accelerate this effort, I’ve begun reading through this year’s proceedings of the top three wireless conferences. My mantra: Expertise is destiny.)


In the quest for big accomplishment, there’s no escaping the discomfort of deliberate practice. As James’ story emphasizes: for many fields, grad students included, a metric such as stretch churn can be an easy way to integrate this hard work into your life.

Big goals are overrated. As is hard work for the sake of hard work. Master your field and the breakthroughs will slide into focus.

(Top photo by cctvprojdoc)

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