Terry Speed, professor emeritus at the University of California-Berkeley and head of bioinformatics at the Walter and Eliza Hall Institute for Medical Research in Melbourne, Australia, recently won the National Institute of Statistical Sciences 2014 Jerome Sacks Award for Multi-Disciplinary Research. According to the nominating committee, “Speed is a pioneer in the development and application of statistical methods for the analysis of biomedical and genomic data. His work exemplifies the best of applied statistics in cross-disciplinary research and is notable for its creativity, rigor, and relevance.” Read on to learn a little more about Speed.
NISS 2015 Jerome Sacks Award
If you know someone who, like Terry Speed, is a leader in sustained, high-quality cross-disciplinary research involving the statistical sciences, consider nominating that person for the NISS 2015 Jerome Sacks Award for Outstanding Multi-Disciplinary Research. To nominate an individual, submit as one PDF document the following information to sacksaward2015@niss.org by May 25:
- Nomination letter (maximum two pages)
- Contact information for two individuals (other than nominator) who have consented to provide letters of support
- CV
Previous winners:
- 2001 Elizabeth Thompson
- 2002 Max Morris
- 2003 Raymond Carroll
- 2004 Douglas Nychka
- 2005 Jeff Wu
- 2006 Adrian Raftery
- 2007 Cliff Spiegelman
- 2008 John Rice
- 2009 Ram Gnanadesikan
- 2010 Sallie Keller
- 2011 Emery Brown
- 2012 William Q. Meeker
- 2013 Kenneth P. Burnham
- 2014 Terry Speed
What got you interested in the field of statistics?
I studied math and statistics as an undergraduate, with a side interest in genetics. I liked all three, with statistics winning eventually, after I did a PhD in math, and later I came back to genetics.
Who were some of your influencers?
My number-one influencer was R.A. Fisher. He died in the year I took my first statistics course (1962), but his influence was there in both statistics and genetics.
What are you working on today?
Mainly cancer genomics and epigenomics. In the last few years, we have made an almost complete transition from microarrays to analyzing DNA and RNA sequence data. We study DNA variation using whole genome or whole exome DNA sequence variation, including changes in copy number, gene expression using the technique known as RNA-seq, methylation with sequencing bisulphite-treated DNA, and histone and other modifications using ChIP-seq data. The goal is typically to find molecular and genetic changes associated with clinically significant outcomes such as response or resistance to treatment, recurrence, or survival. We look not just for genes, but for broader events. Hot topics right now in cancer genomics are tumor heterogeneity and evolution and the response of the immune system.
What advice would you give an aspiring statistician getting into interdisciplinary work?
First, I never give advice. Second, if I did, I would just say the obvious: Find an area of application about which you feel passionately.
Anything else you would like to add?
I think motivation is everything in research. You have to gravitate to an area in which you love working.
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