UCLA biomathematician earns prestigious peer award

Dr. Marc Suchard, assistant professor of biomathematics and human genetics at the David Geffen School of Medicine at UCLA, and his former graduate student, Dr. Benjamin Redelings, have won the 2006 Mitchell Prize. The award recognizes an outstanding research paper that applies a statistical approach called Bayesian analysis to solve an important scientific dilemma.

Bayesian statisticians calculate probabilities by blending prior knowledge and accumulated experience through the recording and collection of data.

In their award-winning article, Suchard and Redelings described a new Bayesian method that uses genetic information to estimate the evolutionary history of distantly related species. They incorporated measures of uncertainty into their calculations about which genetic material descends from a common ancestor. In this way, the scientists avoided troublesome biases in evolutionary reconstruction that occur when assuming these relationships are known.

Suchard and Redelings' findings about early branches in the evolutionary tree of life were published in the June 2005 edition of the journal Systematic Biology.

"Dr. Suchard is an accomplished scientist with a history of winning prizes for his research, even when he was a student," said Dr. Elliot Landaw, chair of the Department of Biomathematics at the David Geffen School of Medicine at UCLA. "I'm delighted to hear that his contributions to Bayesian science have been recognized by his peers."

Named after Bayesian statistician Toby J. Mitchell, the Mitchell Prize is jointly sponsored by the American Statistical Association's Section on Bayesian Statistical Science, the International Society for Bayesian Analysis and the Mitchell Prize Founders' Committee.

Suchard was a 1995 British Marshall Scholar, a 1996 Howard Hughes Medical Institute Predoctoral Fellow and a recipient of the 2002 Leonard J. Savage Award, also in Bayesian statistics. He earned his medical and doctor

Contact: Elaine Schmidt
University of California - Los Angeles

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