Eric Lander to Give von Neumann Lecture in PhiladelphiaJune 3, 2002
Eric Lander of the Whitehead Institute/MIT Center for Genome Research. Photograph by Sam Ogden.
Genome research includes the mapping and sequencing of all the DNA of an organism (its genome), as well as studies of many or all the genes of an organism. These activities result in massive data sets.
Lander began his academic life as a mathematician and as a Rhodes Scholar at Oxford University. His PhD thesis, Symmetric Designs: An Algebraic Approach, was published by Cambridge University Press in 1983.
Taking a decidedly nonlinear career path, Lander joined the faculty of the Harvard Business School in 1981, holding an appointment there until 1990. During this period, along with a full teaching load in the Business School, he studied genetics and molecular biology; in 1986, with David Botstein and others, he began to produce an influential series of papers on genetic mapping that continues to this day. Modern molecular biology has produced a wealth of data that can be used to map genes; such efforts require new, computationally intense methods.
Lander became a Fellow of the Whitehead Institute in 1986, and a member of the MIT Department of Biology in 1989. Since 1999 he has been director of the Whitehead/MIT Center for Genome Research; the center was funded under the Human Genome Project to map, sequence, and analyze the human, mouse, and rat genomes and, indeed, has been a central and key contributor to these projects.
Included in his numerous awards are a MacArthur Foundation Fellowship (1987) for genetics, and election to the National Academy of Sciences (1998), the U.S. Institute of Medicine (1998), and the American Academy of Arts and Sciences (1999).
Researchers in the life sciences have made tremendous progress since 1953, when the double-helix structure of DNA was discovered. Today, entire genomes are known (to various degrees of accuracy), and more genomes are being determined all the time, resulting in an exponential growth of DNA sequence data. Massive experimental data sets, produced daily, are providing information about tens of thousands of genes. Such modern data-intensive experiments require extensive modeling, and computational and statistical analyses. This has created new opportunities and challenges for the applied mathematical sciences. I can think of no better place to start learning about these exciting opportunities than a lecture by Eric Lander.---Michael Waterman, University of Southern California