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
Shahina Rahman, Post-Doctoral Research, Texas A&M University
“Fast Clustering of High Dimensional Data”
June 15, 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
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
August 2018
New Graduate Student Orientation
August 21, 201809:00 AM / 05:00 PM Blocker Building, Room 457, 453 979-845-3141
2018 Faculty Retreat
August 24, 201808:00 AM / 04:00 PM Hilton College Station Hotel & Conference Center 979-845-3141
Departmental Colloquia: Anuj Srivastava
August 31, 201811:30 AM / 12:30 PM Blocker Building (BLOC), Room 113 979-845-3141
ANUJ SRIVASTAVA 
Department of Statistics
Florida State University
Recent Advances in Elastic Functional Data Analysis
ABSTRACT
Functional data analysis (FDA) is fast becoming an important research area, due to its broad applications in many branches of science, including biostatistics and bioinformatics. An essential component of FDA is registration of points across functional objects. Without proper registration, the results are often inferior and difficult to interpret.
The current practice in FDA community is to treat registration as a pre-processing step, using off-the-shelf alignment procedures, and follow it up with statistical analysis of the resulting data. In contrast, Elastic FDA is a more comprehensive approach, where one solves for the registration and statistical inferences in a simultaneous fashion. The key idea here is to use Riemannian metrics with appropriate invariance properties, to form objective functions for alignment and to develop statistical models involving functional data. While these elastic metrics are complicated in general, we have developed a family of square-root transformations that map these metrics into simpler Euclidean representations, thus enabling more standard statistical procedures. Specifically, we have developed techniques for elastic functional PCA and elastic regression models involving functional variables. I will demonstrate this ideas using imaging data in neuroscience and bioinformatics, where biological structures can often be represented as functions (curves or surfaces) on intervals or spheres. Examples of curves include DTI fiber tracts and chromosomes while examples of surfaces include subcortical structures (hippocampus, thalamus, putamen, etc). Statistical goals here include shape analysis and modeling of these structures and to use their shapes in medical diagnosis.
Friday, 8/31/2018, BLOC 113, 11:30 AM