Undergraduate, Diversity Scholarship
The Climate, Diversity and Inclusion Scholarship for Undergraduates is established to recognize and help first-generation statistics undergraduates through their accomplishments and initiatives.
I Am Texas A&M Science - Alan Dabney
Even for the most seasoned academic researcher or staff member, career inspiration starts somewhere, and it all begins with a story. Here's one from Texas A&M statistician Alan Dabney, who discusses his field’s applications to public health in our most recent edition of I Am Texas A&M Science.
In celebration of the 21st annual Student Research Week (March 19-23) at Texas A&M as well as March as Women's History Month, the College of Science is taking five with five different women involved in both innovative research and milestone firsts during the past academic year. Today's kickoff segment features Tessa Johnson '17, Texas A&M's first bachelor of statistics graduate.Read More →
Texas A&M statistician Irina Gaynanova has been selected to receive the American Statistical Association's 2018 David P. Byar Young Investigator Award, presented by the ASA Biometrics Section to honor the top original manuscript submitted by a new researcher for presentation at the Joint Statistical Meetings.Read More →
03:00 PM / 04:30 PM Blocker Building (BLOC), Room 457 979-845-3141
ERIC B. LABER
Associate Professor of Statistics
North Carolina State University
Recipient of the 2017 Raymond Carroll Young Investigator Award
Optimal Treatment Allocations in Space and Time for Online Control of an Emerging Infectious Disease
A key component in controlling the spread of an epidemic is deciding where, when, and to whom to apply an intervention. We develop a framework for using data to inform these decisions in real-time. We formalize a treatment allocation strategy as a sequence of functions, one per treatment period, that map up-to-date information on the spread of an infectious disease to a subset of locations where treatment should be allocated. An optimal allocation strategy optimizes some cumulative outcome, e.g., the number of uninfected locations, the geographic footprint of the disease, or the cost of the epidemic. Estimation of an optimal allocation strategy for an emerging infectious disease is challenging because spatial proximity induces interference among locations, the number of possible allocations is exponential in the number of locations, and because disease dynamics and intervention effectiveness are unknown at outbreak. We derive a Bayesian online estimator of the optimal allocation strategy that combines simulation-optimization with Thompson sampling. The proposed estimator performs favorably in simulation experiments. This work is motivated by and illustrated using data on the spread of white-nose syndrome, a highly fatal infectious disease devastating bat populations in North America.
Monday, 3/25/2018, 3:00 PM, BLOC 457
11:30 AM / 12:30 PM 979-845-3141
11:30 AM / 12:30 PM Blocker Building (BLOC), Room 113 979-845-3141