University of California, San Diego
Pavel Pevzner is Ronald R. Taylor Distinguished Professor of Computer Science at the University of California, San Diego.
Pavel Pevzner, an HHMI Professor and professor of computer science and engineering at UCSD's Jacobs School of Engineering, plans first to introduce undergraduates to what he calls "recreational mathematics," puzzles that can be addressed with computation. His goal is to teach students in biology, bioengineering, and biochemistry to think quantitatively.
"Biology has changed dramatically in the past 20 years. Although computation is now a critical part of the field, the undergraduate biology curriculum really does not reflect this sea change," said Pevzner. As an important step in that direction, Pevzner is planning an introductory bioinformatics course, available to any undergraduate, without any prerequisites.
Pevzner first discovered the difficulty of explaining math to the masses in the late 1980s, when he worked as a science magazine writer in Russia after he completed a PhD at the Moscow Institute of Physics and Technology. At UCSD, he is known for bringing a sharp mathematical eye to biology. Using computer science to analyze the human genome, he pioneered a "fragile breakage theory" that suggests that major evolutionary changes are more likely to occur in certain segments of the genome, much as earthquakes take place along geological fault zones. This ongoing work has implications for understanding diseases such as cancer.
As an HHMI Professor, Pevzner's primary goal is to recruit undergraduates to the field of bioinformatics. In addition to his puzzle-packed introductory course, he plans to design a collaborative research class in which undergraduate and graduate students team up to research and report on a bioinformatics project. Ultimately, Pevzner envisions a new generation of computational biologists, armed with the tools to use genomics and similar technologies effectively to help combat disease and unravel basic biological questions. "Every biologist should know some computational biology," he says. "But that's not all. If we do our job well, these kids will learn to love mathematics."