Oct 3, 2012

COPSS Presidents’ Award: Samuel Kou

Bhramar Mukherjee, Treasurer/Secretary of the Committee of Presidents of Statistical Societies, presents her profile of, and interview with, the winner of the Presidents’ Awards, Sam Kou.

Congratulations to Samuel Kou, Harvard University, the 2012 COPSS Presidents’ Award winner! The Committee of Presidents of Statistical Societies (COPSS) annually presents awards to honor statisticians under the age of 41 for their outstanding research contributions and service to further the field of statistics (the call for 2013 nominations was in the last issue).

Profile

Samuel Kou was born in Lanzhou, China. He attended Lanzhou No. 1 High School and received his bachelor’s degree in computational mathematics from Peking University in 1997. He obtained his PhD in statistics from Stanford University in 2001 under the supervision of Professor Bradley Efron. After completing the PhD study, he joined Harvard University as an Assistant Professor. He was promoted to full professor there in 2008. He is the recipient of a US National Science Foundation CAREER Award, the IMS Richard Tweedie Award, and the Raymond J. Carroll Young Investigator Award. He is an elected Fellow of the American Statistical Association and the Institute of Mathematical Statistics and an elected member of the International Statistical Institute. He was an IMS Medallion Lecturer in 2009, and the recipient of the 2010 American Statistical Association Outstanding Statistical Application Award. He is currently an Editor of the Annals of Applied Statistics and an Editor of Chance magazine, in addition to serving or having served as an associate editor in Statistical Science, Journal of the American Statistical Association (Applications and Case Studies), Bernoulli, Annals of Applied Statistics, Journal of Multivariate Analysis and Statistica Sinica.

Samuel Kou has made fundamental contributions to interdisciplinary, methodological, computational, and mathematical statistics through his work on Bayesian and Monte Carlo methods, nonparametric methods, stochastic modeling and inference in biophysics, and stochastic modeling in finance and economics.

Samuel Kou actively works in the interdisciplinary field of nanoscale biophysics, a booming field that lies in the intersection of biology, chemistry, physics and nanotechnology, where the most state-of-the-art nanotechnologies are applied to study biological systems on an unprecedented single-molecule scale. His groundbreaking contributions include the first likelihood-based Bayesian data augmentation method for handling unobserved molecular Brownian diffusion in single-molecule experiments; the first fractional Brownian motion based Hamiltonian model to explain the experimentally observed conformational fluctuation of protein molecules, which had defied the classical Einstein Brownian diffusion model; and the first microscopic stochastic model that successfully explains the previously mysterious experimental findings in enzymatic reactions, which had contradicted the classical Michalis-Menten kinetics. Samuel Kou’s seminal contributions to Monte Carlo methods include the equi-energy sampler, a new Monte Carlo framework, for statistical sampling and inference, which has significantly improved the efficiency of Monte Carlo simulation and has provided a fundamentally new perspective to study sampling and inference problems; the multiresolution method, a general inference framework, to study diffusion processes; the sequential Monte Carlo method FRESS (fragment regrowth via energy-guided sequential sampling) to study protein folding. These methods have been successfully applied to a range of challenging scientific problems, such as DNA motif finding in computational biology, thermodynamic estimation in statistical mechanics, exploring the energy landscape of biomolecules, as well as the problem of protein folding. Samuel Kou’s works in nonparametric and empirical Bayes methods include using curved exponential family to construct model selection criterion, SURE (Stein’s unbiased risk estimate) inference of hierarchical models, and kernel estimation of doubly stochastic Poisson processes, among others.

Samuel Kou is a respected teacher, well-liked by his undergraduate and graduate students, and has played an important role in the training of students at Harvard Statistics Department, by first serving as Head Tutor and now as the co-Director of Graduate Studies.

The Presidents’ Award citation recognized Samuel Kou “for groundbreaking contributions to stochastic modeling and statistical inference in single molecule biophysics; for pioneering the equi-energy sampler; for fundamental contributions to Bayesian, empirical Bayes and nonparametric methods; and for outstanding service to the statistical profession and contribution to statistical education.”


Above: Samuel S. Kou, (right) accepting the COPSS Presidents’ Award from Xihong Lin. Photo courtesy of Eric Sampson/ASA

Professor Kou graciously agreed to respond to Bhramar’s questions, which we hope will be of interest to our readers.

What was your first reaction to winning the prestigious COPSS President’s Award?
I remember I was in my office preparing the lecture notes for the next classes, when Tony Cai, the committee chair, called, informing me about the news. I was quite surprised, because it was still in February and I thought the result would not be known until much later. And of course, I was overjoyed—it is such a great honor. I then shared the news with my parents and brother. They were all very happy.

Which part of your job do you like the most?
I enjoy being a statistician, working on a diverse range of problems. Many of the problems I work on come from real scientific issues: the challenge of analyzing experimental data or of constructing a stochastic model to explain the experimental puzzles. Along the way, I get the chance to learn some science. As John Tukey said, “The best thing about being a statistician is that you get to play in everyone’s backyard.”

What advice would you give to young people who are entering the profession as PhD students and assistant professors at this time?
Follow the heart, not the trend. I think only by doing what you love to do, by listening to your heart, can you proceed forward and enjoy the process and the fruits in the long run. I feel it is worth keeping in mind that what is hot right now might not be so hot five or ten years down the line.

Who are your most significant mentors? What impact have they had on your career?
I was fortunate to be a student of Professor Bradley Efron. I learned from him not only statistics, but more importantly how to approach a problem and how to think. Later on, at Harvard, I was lucky to get the chance to learn from and be mentored by Professors Don Rubin, Wing Wong and Jun Liu. Professor Sunney Xie at the Department of Chemistry and Chemical Biology of Harvard University is my science mentor. I picked up from him not only some chemistry, biology and physics, but a perspective of science and the role of statistics in science.

How did you start to work on statistical problems in Biophysics?
Not long after I joined Harvard Statistics Department, I met Professor Sunney Xie at a lunch (arranged by Professor Jun Liu). He told me that his group were doing experiments on single molecules to study biological systems. I was immediately fascinated by it, though I never heard of single-molecule experiments before. I asked him for some background papers to read. It took me a few months to get a rudimentary understanding of what they were doing. I realized that there were a lot of exciting statistical problems in the field because fundamentally at the single-molecule level everything is stochastic, where statistics has an important role to play. I then started to collaborate with Professor Sunney Xie.
Is there anything else you would like to share about our profession?

I love statistics. A statistician is like a nineteenth-century mathematician: on one hand they were working on problems in mechanics, fluid dynamics, optics, astronomy, etc., and on the other hand, they were working on theories, structures and methods. I guess in the modern world, statistics is one of the very few disciplines that still enjoys such interplay.

Finally, what are your hobbies/interests beyond statistics?
I like to travel and explore different cultures. Working in the academia is wonderful in this regard. I like outdoor hiking, though I am not a serious hiker. I like reading books on history, philosophy and general-audience science.

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Nominations are open for next year’s awards (the Presidents’, F.N. David, G.W. Snedecor, and the Fisher Lectureship and Award). See www.niss.org/copss for details and deadlines.

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