Fran Allen and the social relevance of computer science

If you haven’t read it yet, Peter Seibel’s Coders at Work (2009), is one of the best books about computer programming that doesn’t have actual code in it. It distills “nearly eighty hours of conversations with fifteen all-time great programmers and computer scientists,” with equal parts given to fascinating technical minutiae (including the respondents’ best/worst bug hunting stories) and to learning how these coders came to think the way they do.

So in a book full of interviews worth reading, it’s not quite accurate to say that Fran Allen stands out. It’s better to say that Allen is different; as a Turing Award recipient for her “pioneering contributions to the theory and practice of optimizing compiler techniques,” Allen spends much of her interview arguing that compiler optimization is woefully unstudied. Allen even argues that the popular adoption of C was a step backwards for computer science, which is kind of an alien concept for those of us today who almost exclusively study and use high-level languages.

Allen is also different in that she’s the only woman in Seibel’s book, and understandably, she has a few thoughts about their place in computer science. The summary of it is that she’s not at all optimistic about the “50/50 by 2020″ initiative (the goal to have women make up half of computer science degree earners by 2020). And the problem, Allen (who was a math teacher herself) is not in the curriculum:

I feel it’s our problem to solve. It’s not telling the educators to change their training; we in the field have to make it more appealing.

What I found particularly insightful in Allen’s interview with Seibel is that it’s not just about the need for more role models, because the current lack of women programmers is going to place a limit on that. In Allen’s opinion, girls have shown an equal aptitude for science, especially in medicine, biology, and ecology. So she suspects that the problem is with how _limited_ computer science can appear as a profession.

At my little high school in Croton, New York, we had a Westinghouse person nationally come in fifth. And they have a nice science program. Six of the seven people in it this year at the senior level are women doing amazing pieces of individual science.

What’s happening with those women is that they’re going into socially relevant fields. Computer science could be extremely socially relevant, but they’re going into earth sciences, biological sciences, medicine. Medicine is going to be 50/50 very soon. A lot of fields have belied that theory, but we [in computer science] haven’t.

I don’t necessarily think this perception that programming doesn’t seem to have a purpose behind obsessively sitting in front of a computer all day is exclusive to women. Even for those who’ve pursued a degree in computer science, it’s not clear how programming has relevance that is not an end to itself.

Check out this 2008 Slashdot thread, in which a recent computer science undergrad asks for suggestions of “Non-Programming Jobs for a Computer Science Major?” because he can’t think of ways to use computational thinking that doesn’t directly involve code. Or more recently, this screed by a NYU journalism professor, who sees coding as a trend du jour, little more than a pointless struggle to learn more code before a new language becomes hot and makes you obsolete.

I can’t claim to have insight myself, because when I left college with a computer engineering degree, I had no idea how to use it except to be a computer engineer, which I didn’t want to be, so I ditched it entirely at my first journalism job. Years later, I’ve slowly learned how to use programming to, well, practice journalism’s core function of interpreting and disseminating information. However, I attribute this to how much our world has become digitized with far fewer bottlenecks in applying computational thinking. So now it seems much more obvious that computer science can be as directly relevant to general society as medicine and ecology.

Non-scientists often assume that all scientists, and similarly left-brained people, can equally grok the concepts of programming. But this is as wrong an assumption as thinking that any programmer can easily pass the MCATs. Within the field of biological research, for example, there’s a difference of roles for biologists who can program and those who cannot.

The two fields of research are described as “wet-lab” and “dry-lab” work. In a recent issue of Nature, Roberta Kwok writes about how “biologists frustrated with wet-lab work can find rewards in a move to computational research“:

During her master’s programme in genetics from 2005 to 2008, Sarah Hird dreaded going into the lab. She was studying subspecies of red-tailed chipmunks and had become discouraged and frustrated by the uncertainties of molecular-biology experiments. She spent six weeks trying to amplify repetitive sequences in chipmunk DNA as part of an experiment to identify genetic differences between populations — but to no avail. Hird tried replacing reagents, switching to a different machine for running the polymerase chain reaction and decontaminating the sample-preparation area. Nothing worked. And then, for reasons that she never quite deciphered, the technique suddenly started working again.

By the end of her master’s, Hird had come to dislike working in a wet lab, and she decided not to apply for PhD programmes.

About six months after finishing her master’s degree, while working as a part-time technician at Louisiana State University in Baton Rouge, she discovered a better direction. The lab’s principal investigator had suggested that she learn a computer-programming language so that she could help with a simulation project. Hird, who had never programmed before, taught herself the language using a book and online tutorials, and quickly became engrossed.

“Once I started, it was like an addiction,” she says. She enjoyed developing algorithms, and she found the software-debugging process less frustrating than troubleshooting wet-lab problems. The work felt more under her control.

Later in her article, Kwok interviews a German biologist at the Max Planck Institute who offers this insight:

He notes that newcomers may stay more motivated if they can apply computational skills to real scientific problems rather than to the ‘toy’ exercises in a computer-science class. For example, a researcher who works with many image files could write a program to automatically perform processing steps, such as contrast enhancement, on thousands of images.

If young students – male or female – are turned off at the prospect of learning computer science, it’s not enough to just have role models. The usefulness of computational thinking are far too broad for just that. Why should only dedicated computer scientists benefit from the techniques and theory of programming, as if the importance of writing should only be left up to published writers?

Ideally the importance of computational thinking would be part of the general curriculum, and not just as a separate programming class, but integrated in the same way that you must read and write and perform calculations in your biology, physics, and economics class. But while we wait for that change to come about eventually – if at all – those of us in the field can help to increase diversity in computer science by increasing the visibility of computer science’s diverse impacts and applications.

After Allen complains about computer science’s too-narrow scope, Seibel simply asks, “So why do you like it?” She responds:

Part of it is that there’s the potential for new ideas every day. One sees something, and says, “Oh, that’s new.” The whole field gets refreshed very frequently. It’s very exciting to think about what the potential for all of this is and the impacts it can have.

Isaac Asimov made a statement about the future of computers-I don’t know whether it’s right or not-that what they’ll do is make every one of us much more creative. Computing would launch the age of creativity. One sees some of that happening-particularly in media. Kids are doing things they weren’t able to do before-make movies, create pictures.

We tend to think of creativity as a special gift that a person has, just as being able to read and write were special gifts in the Dark Ages-things only a few people were able to do. I found the idea that computers are the enablers of creativity very inspiring.

There’s a lot of other great stuff and stories in Allen’s interview, including her attempt to teach Fortran to IBM scientists, the need for compiler optimization in the age of petaflop-speed computing, and how other women in the industry, including one “who essentially was the inventor of multiprogramming”, have been robbed of their achievements. Read the rest of Allen’s interview, and 14 other equally great interviews with coders, in Seibel’s book, Coders at Work.

I'm a programmer journalist, currently teaching computational journalism at Stanford University. I'm trying to do my new blogging at