CLICK HERE TO JOIN
There has never been a better time to be a statistician.
Texas A&M Statistics is hoping to capitalize on this current trend and our decades of success as a department by embarking on a unique effort to benefit our former, current, and future students. But to do so, we’ll need your help.
We’ve started the Statistics Former Student Network to leverage the rich and varied experiences, expertise, and skill sets of our former students to help enrich and extend the educational experiences for our present and prospective graduate and undergraduate students.
Because we believe the short- and long-term success of such a powerful group begins and ends with its potential members, we value your input, ideas, and advice. If you have interest in serving on the board, have suggestions for talks or webinars, wish to assist with mentoring students, or have any other ideas for SFSN, please contact us!
One of Texas A&M University’s best assets is its people — successful former students, colleagues, and friends with a wealth of institutional, industry, and inspirational knowledge to offer. We encourage you to consider giving back by paying it forward. Advantage, Aggies!
CLICK HERE TO GET STARTED
A network of possibility, created by Texas A&M statisticians for Texas A&M statisticians!
Know someone who’s not receiving our emails? It might be because they haven’t updated their Aggie Network profile! To update your contact information, visit aggienetwork.com.
MOST RECENT WEBINAR
Scott Fincher, Data Scientist, KNIME, Inc.
“Open Source Modeling Integrations with KNIME”
October 24, 2018, 11:30 am (CST), Blocker 457
We will post a link to the recorded presentation within 48 hours of the event.
Missed the webinar? We’ve got you covered! |
CLICK HERE to play the recorded video. |
PAST WEBINARS
June 15, 2018
Shahina Rahman, Post-Doctoral Researcher, Texas A&M University
Fast Clustering of High Dimensional Data (Click to view)
February 28, 2018 (Inaugural Webinar)
Mikyoung Jun, Associate Professor of Statistics, Texas A&M University
Predictive Statistical Representation of Observed and Simulated Rainfall Using Generalized Linear Models (Click to view)
CONNECT WITH YOUR PEERS
SFSN Twitter, Facebook, and LinkedIn groups are coming soon! Also, stay tuned for links to an online forum for SFSN members, currently in development.
CURRENT BOARD MEMBERS
Please feel free to contact the board if you have questions about SFSN, or if we can assist you in any way.
 |
Dr. Ersen Arseven
PhD Statistics ’74 Statistical and Process Consultant Nyack, NY |
|
 |
Marcy Johnson
MS Statistics ’88 Director, Quantitative Research, MD Anderson Cancer Center Houston, TX |
|
 |
Dr. Shane Reese
Ph.D Statistics ’99 Dean, College of Physical and Mathematical Sciences, Brigham Young University Provo, UT |
|
 |
Scott Fincher
MS Statistics ’17 Data Scientist, KNIME Austin, TX |
|
 |
Dr. Jinhua Huang
PhD Statistics (UC Berkeley) 1997 Head, Statistics Department, Texas A&M University College Station, TX |
StatLinks
Calendar
October 2018
Departmental Colloquia: Yufeng Liu
October 19, 201811:30 AM / 12:30 PM Blocker Building (BLOC), Room 113 979-845-3141
YUFENG LIU 
Department of Statistics and Operations Research
University of North Carolina at Chapel Hill
Sparse Regression for Block Missing Data Without Imputation
ABSTRACT
Supervised learning techniques have been widely used in diverse scientific disciplines such as business, finance, biology and neuroscience. In this talk, I will present a new technique for flexible learning of data with complex block-missing structure. We focus on data with multiple modalities (sources or types). In practice, it is common to have block-missing structure for such multi-modality data. A new technique effectively using all available data information without imputation will be discussed. Applications for the Alzheimer's Disease Neuroimaging Initiative (ADNI) data will be used to illustrate the performance of the proposed method.
Friday, 10/19/2018, BLOC 113, 11:30 AM
Departmental Colloquia: Thomas Santner
October 26, 201811:30 AM / 12:30 PM Blocker Building (BLOC), Room 113 979-845-3141
THOMAS SANTNER 
Department of Statistics
Ohio State University
Variable Selection for Deterministic Computer Simulator Output
ABSTRACT
This talk will describe a variable selection methodology to identify the “active” inputs of a deterministic simulator code. While the basic methodology can be used in many settings, the description here will use an enhancement of a Bayesian composite Gaussian Process (BCGP) model. The basic BCGP model assumes that the simulator output can be described as the sum of draw from a GP that is meant to describe the long-range mean of the output plus a draw from an independent GP that describes small-scale deviations from the long-range mean. A prior is placed on the model parameters that insures the process describing the mean is smoother than that describing local deviations from the mean. In this talk, the correlation function is assumed to be a Gaussian correlation function. Based on a Bayesian fit to an enhancement of the basic BCGP model the inputs having smaller estimated correlation parameters are judged to be more active. A reference inactive input is added to the data to judge the size of the correlation parameter for inactive inputs. The importance of design is indicated.
Joint work with Casey Davis and Christopher Hans.
Joint seminar with the Department of Epidemiology and Biostatistics, TAMU School of Public Health
Friday, 10/26/18, BLOC 113, 11:30 AM
Departmental Colloquia: Christian Mueller
November 2, 201811:30 AM / 12:30 PM Blocker Building (BLOC), Room 113 979-845-3141
