Why basic research matters I: Mark Green

This summer, I gave a keynote presentation that covered more than 30 years of my own research. This gave me the opportunity to reflect on the research results that have made an impact, particularly in industrial practice.

The field of computer science moves so quickly that you can observe the long-term impact of your research within your own lifetime.  I found that the my most significant long-term impacts came from my pure or curiosity-driven research.  I have done a lot of applied research, but its impact has been short-term and quickly forgotten.  In addition, it’s the results of the pure research that I now teach to undergraduate students, including in our introductory computer science course.  History demonstrates how pure and applied research are intertwined.

In the 1970s, I started working on the problem of automating the design of graphical user interfaces. This may seem like an applied problem now, but long before the introduction of the Mac OS and Microsoft Windows, it was viewed as relatively useless research of no practical value. I can still vividly remember industry leaders calling me another of those crazy university researchers and insisting that “no one would ever want to use a mouse with a computer.”

In 1986, I wrote a paper on the foundations of user-interface software. At the time, it was viewed as a very theoretical paper. The journal editor thought the paper was so theoretical that I had to move some of the material, theorems and proofs to a technical report before he would accept it. He told me that no one would read the paper.

In 2005, I returned to Canada to start a new computer science program at a new university. I prepared by reviewing the standard undergraduate computer science curriculum produced by the two leading professional societies in the area.  Much to my surprise, this paper was listed as one of the topics that all undergraduate computer science students should be familiar with.  In 20 years, it had gone from too theoretical for an academic journal to part of the undergraduate curriculum.

In the 1980s, I was part of a small group of researchers investigating a “curiosity” that was an offshoot of our main line of research.  We didn’t think it would amount to much, but we wrote a few papers on it.  Over time the main line of research died out, but this offshoot started to gain traction. It has now become a standard part of software development, which I teach in our first computer science course.

Why is this important? After presenting the topic, I then state that I was one of the people who developed it. For first year students this demystifies the whole research process.  Research isn’t done by people who are long dead, but by people just like them. We need to teach our students early that they are the innovators of the future. They can’t just follow along – they can and must lead.  I believe it helps to have this message delivered by someone who has been there, who can make the story real.

If we do not expose our undergraduate students to pure research, we are doing them a great disservice. In fast-paced fields like computer science the “applied” topics that we teach them now are often out of date shortly after they graduate.  To prepare them for the future we need to whet their appetite for curiosity-driven research that will empower them to continue to explore new and different ideas long after they graduate.

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From farm to fork

ORION staff | October 9, 2015

It’s morning. Farmers across Ontario are waking up to tend to their animals. You might be sitting down to a plate of scrambled eggs—maybe even a few strips of bacon. We take it for granted that this food will be safe to eat. But we rarely think about why. That, in part, is thanks to people like University of Guelph systems design engineer Deborah Stacey. Relying on a high-performance computing network, her research helps inform the regulatory structures that ensure our food is free of contamination and that the animals it comes from are healthy. It is, in part, due to her work that we now have modelling programs such as NAADSM, the North American Animal Disease Spread Model. This is the software governments and industry rely on to plan for and prevent epidemics. “NAADSM allows you to put in various scenarios for various animal diseases to see how they would spread,” says Stacey. “My interest is in looking at the network connections within that: contact, moving animals from one herd to another, and licking or touching other animals. I’m interested in how these contact networks differ across industries, which could suggest a different path of disease spread.” This research is then used by organizations such as the Guelph-based Poultry Industry Council to help determine which transportation and feed networks most effectively limit or eliminate things like avian diseases—in other words, how to ensure your scrambled eggs are safe. Stacey’s work produces a staggering amount of data, and it requires a lot of statistical analysis. It’s done through the Shared Hierarchical Academic Research Computing Network, or SHARCNET, a consortium of Ontario universities, colleges and research centres using a shared system of distributed high-performance computing, linked together through the ORION network. “Studying these networks made me more aware of how we develop and distribute the food we eat,” Stacey says. “It was surprising to find out how critical these farming systems are, and that they can be understood using mathematical models. These human systems that we’ve evolved are incredibly complex, and it was enlightening to see how much we need to study this—our food safety and security depend on understanding these systems.” A  version of this story was originally published by ORION.  It has been edited for clarity, accuracy and brevity, and is republished here with permission. 
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