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Ridgway Scott makes computer-screen stars of molecules, money, and more.

The mathematician and computer scientist hones the high-tech tools of
biomedical, financial, and other researchers.

When a computer-software program crashes, others might curse their screens and huff away. But that’s just the moment Ridgway Scott wants to pull up his chair. Amathematician and computer scientist, Scott works on some of the “buggiest”—or most problematic—software, a challenge he says he enjoys because “the most interesting mathematics come out of this process.”

The math he’s talking about goes far beyond the use of numerals in the binary code that most computer languages are written in. He means the nitty-gritty—the underlying differential equations and numerical algorithms—that run not simple word-processing programs, like the ubiquitous Microsoft Word, but that can analyze large amounts of data, from genetic information to stock-market quotes, or simulate on a computer screen the real-life behavior of something very, very small or very, very big, whether an enzyme or a star. And his preferred PC is not a personal computer but a parallel computer, which has the power to harness thousands of computer processors and perform billions of computations at once.

A former L. E. Dickson instructor in mathematics at the U of C, Scott returned to Chicago last fall as a professor in the departments of computer science and mathematics. He earned his Ph.D. in mathematics in 1973 from MIT, where he was a pioneer in refining the finite-element method, the most widely used computational technique for engineering design and analysis. He later helped to establish parallel-computing centers at Pennsylvania State University and the University of Michigan. Most recently, at the University of Houston, he directed the Texas Center for Advanced Molecular Computation, a research group devoted to biomolecular design and funded by the National Science Foundation.

He has summed up his findings in two books and more than 100 papers on structural mechanics, fluid dynamics, nuclear engineering, computational chemistry, and daunting mathematical techniques—with names like “boundary element,” “finite difference,” and “spectral”—that are used to solve the partial-differential equations applied in engineering.

Although he’s still moving into his new office in the Ryerson Physical Laboratory building, Scott has long since unpacked one box: the box that contained his custom-built desktop computer. Its enviable specifications include two Intel Pentium II processors, a half gigabyte of RAM, and a 10-gigabyte hard drive; moreover, it’s linked to several others—nearby and just like it—to form a parallel computer. At Chicago, Scott’s using the high-powered machine to continue his previous studies and to launch some new software projects that will aid geneticists, financial analysts, and even astrophysicists.

In November, Daphne Preuss, an assistant professor in molecular genetics & cell biology, asked Scott to help her manage data collected as part of her study of plant genetics. She had been using her word-processing program to pinpoint repetitive DNA sequences, a painstaking process. Scott’s now looking at ways the sequences can be graphically presented. He’s also collaborating with senior lecturer Robert Almgren to create software that lets students in the master’s program in financial mathematics model stock-market scenarios using massive amounts of New York Stock Exchange data. And at the University/Argonne Center on Astrophysical Thermonuclear Flashes, he is helping to develop software that can simulate the violent explosions occurring when hydrogen from one star accumulates on another nearby star and ignites.

These additional projects have not subtracted from Scott’s ongoing work. He’s expanding his efforts in the field of computational fluid dynamics through a new research group being organized at Chicago by physicist and mathematician Leo Kadanoff. Scott’s work in this area might inform, for example, engineers designing steel-making systems that are based on complicated geometrical patterns.

He’s also producing results as the project leader of two research teams organized through the National Partnership for Advanced Computational Infrastructure (NPACI), a group of 46 research institutions and universities exploring how the computational power of parallel computers can most easily be applied in science and engineering.

One of Scott’s teams is refining computer-generated images of molecules. The team has already conducted simulations that reveal an open “side door” in the enzyme acetylcholinesterase, or AChE—a finding that may aid in the making of pharmaceutical drugs that target AChE. While clinical studies suggest, for example, that AChE inhibitors may be useful in enhancing memory in patients with Alzheimer’s disease, an effective inhibitor cannot be designed without a detailed understanding of the AChE molecule and how it might interact with the inhibitor.

That’s where Scott and his team enter the equation. It’s their job to figure out how to model the behavior of the more than 130,000 atoms involved in such a show. They have pushed the limits of the national supercomputer center at the University of California, San Diego, requiring no less than 128 processors to conduct one simulation.

The work of Scott’s other NPACI team may ease this process in the future. In February it released a new set of computer languages, called the P-languages, which, explains Scott, can reproduce structures with irregular shapes and random movements—like molecules—better than previously used languages that work best when applied to predictable grid-like patterns.

It’s Scott’s transferable skills that move him so easily from the cosmic to the atomic level and everywhere in between. He breaks down his approach to developing scientific-simulation software into these basic stages: Donning his mathematician’s hat, Scott represents the force of electrical charges and the other known physical laws affecting a particular molecule, for example, as differential equations—some already devised, others he must create. Next, he derives numerical algorithms to solve the equations. The computer scientist Scott then translates the algorithms into a computer code before his applied-math side uses the resulting simulation program to study the molecule’s behavior.
“The typical scientist or engineer just wants to load the program and run it,” he says. “I like that too, but it’s the process leading up to the code development that I find most interesting. If we don’t do our job, it ain’t going to run.”

Scott hopes to provide an “intellectual home” for all the software experts on campus who, like him, apply their skills across disciplines. He’s working with Robert Zimmer, deputy provost for research, and Rick Stevens, director of the mathematics and computer science division at Argonne, to form a computation institute that would foster the interdisciplinary development of software for use in the biological, financial, physical, and social sciences as well as the arts and humanities.

“There are lots of people in areas as diverse as linguistics, biology, and physics at the forefront of the computations field,” says Scott. “The institute should foster synergy among like-minded people in different departments.”—C.S.

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