ACM XRDS: Jeff Dean profile

by Edward Z. Yang

I was wandering through the Gates building when the latest issue of the ACM XRDS, a student written magazine, caught my eye.

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“Oh, didn’t I write an article for this issue?” Yes, I had!

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The online version is here, though I hear it’s behind a paywall, so I’ve copypasted a draft version of the article below. Fun fact: The first version of this article had a Jeff Dean fact, but we got rid of it because we weren’t sure if everyone knew what Jeff Dean facts were...


True fact: as a high school student, Jeff Dean wrote a statistics package that, on certain functions, was twenty-six times faster than equivalent commercial packages. These days, Jeff Dean works at Google, helping architect and optimize some of the biggest data-crunching systems Google employs on a day-to-day basis. These include the well known MapReduce (a programming model for parallelizing large computations) and BigTable (a system which stores almost all of Google's data). Jeff's current project is infrastructure for deep learning via neural networks, a system with applications for speech/image recognition and natural language processing.

While Jeff has become a public face attached to much of Google's internal infrastructure projects, Jeff stresses the fact that these projects require a mix of areas of expertise from people. Any given project might have people with backgrounds in networking, machine learning and distributed systems. Collectively, a project can achieve more than any person individually. The downsides? With all of the different backgrounds, you really need to know when to say: “Hold on, I don't understand this machine learning term.” Jeff adds, however, that working on these teams is lots of fun: you get to learn about a sub-domain you might not have known very much about.

Along with a different style of solving problems, Google also has different research goals than academia. Jeff gave a particular example of this: when an academic is working on a system, they don't have to worry about what happens if some really rare hardware failure occurs: they simply have to demo the idea. But Google has to worry about these corner cases; it is just what happens when one of your priorities is building a production system. There is also a tension with releasing results to the general public. Before the publication of the MapReduce paper, there was an internal discussion about whether or not to publish. Some were concerned that the paper could benefit Google's competitors. In the end, though, Google decided to release the paper, and you can now get any number of open source implementations of MapReduce.

While Jeff has been at Google for over a decade, the start of his career looked rather different. He recounts how he ended up getting his first job. “I moved around a lot as a kid: I went to eleven schools in twelve years in lots of different places in the world... We moved to Atlanta after my sophomore year in high school, and in this school, I had to do an internship before we could graduate... I knew I was interested in developing software. So the guidance counselor of the school said, 'Oh, great, I'll set up something', and she set up this boring sounding internship. I went to meet with them before I was going to start, and they essentially wanted me to load tapes into tape drives at this insurance company. I thought, 'That doesn't sound much like developing software to me.' So, I scrambled around a bit, and ended up getting an internship at the Center for Disease Control instead.”

This “scrambled” together internship marked the beginning of many years of work for the CDC and the World Health Organization. First working at Atlanta, and then at Geneva, Jeff spent a lot of time working on what progressively grew into a larger and larger system for tracking the spread of infectious disease. These experiences, including a year working full-time between his graduation from undergraduate and his arrival at graduate school, helped fuel is eventual choice of a thesis topic: when Jeff took an optimizing compilers course, he wondered if he could teach compilers to do the optimizations he had done at the WHO. He ended up working with Craig Chambers, a new faculty member who had started the same year he started as a grad student. “It was great, a small research group of three or four students and him. We wrote this optimizing compiler from scratch, and had fun and interesting optimization work.” When he finished his PhD thesis, he went to work at Digital Equipment Corporation and worked on low-level profiling tools for applications.

Jeff likes doing something different every few years. After working on something for a while, he'll pick an adjacent field and then learn about that next. But Jeff was careful to emphasize the fact that while this strategy worked for him, he also thought it was important to have different types of researchers, to have people who were willing to work on the same problem for decades, or the entire career—these people have a lot of in depth knowledge in this area. “There's room in the world for both kinds of people.” But, as he has moved from topic to topic, it turns out that Jeff has come back around again: his current project at Google on parallel training of neural networks was the topic of Jeff's undergraduate senior thesis. “Ironic,” says Jeff.