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Alamo in downtown San Antonio, Texas

The University of Texas at San Antonio Carlos Alvarez College of Business is proud to host the 2023 Alamo Symposium in Statistics from March 10-11, 2023 at UTSA's Main Campus (1 UTSA Circle, San Antonio, TX 78249). The symposium will start early morning on Friday, March 10 and will end around noon on Saturday, March 11.

This one and half day symposium will bring together researchers and practitioners from various fields of statistics to promote data-based discovery and encourage opportunities for research collaborations in south Texas, especially among Hispanic researchers and practitioners. Connect with peers to exchange ideas on the developments of modern statistics methodologies and theory, as well as their applications. 

The conference will be composed of keynote presentations, organized/invited sessions, contributed sessions and poster presentations. Our featured keynote speakers include

-Professor Narayanaswamy Balakrishnan, McMaster University
-Professor David Scott, Rice University
-Professor Brani Vidakovic, Texas A&M University

Register for Symposium Submit Abstract/Poster by Feb. 15

statisticsDuring the conference, we will celebrate Professor Jerome Keating's 75th birthday and will also honor him for his outstanding contribution to the statistics society and statistics programs at UTSA. As a former department chair and program coordinator of the undergraduate statistics and data science program, Dr. Keating has dedicated himself to providing excellent education and training to both undergraduate and graduate students at UTSA, especially to Hispanic and underrepresented minority students in statistics and actuarial science. He is a fellow of the American Statistical Association and a Peter T. Flawn Endowed Professor at UTSA.

Image of Alamo Symposium in Statistics Keynote Speaker Balakrishnan

Narayanaswamy Balakrishnan
Department of Mathematics and Statistics
McMaster University, Canada

Title: Linear Prediction
Abstract: In this talk, I will briefly introduce the problem of optimal linear prediction and then point out various interesting connections of it to different notions in statistical inference. I will then discuss the idea of joint prediction and the associated optimality properties. I will also present some examples to illustrate the importance and usefulness of the developed results.

Professor N. Balakrishnan's Homepage (mcmaster.ca)

Biography
Professor Balakrishnan is a Distinguished University Professor at McMaster University, Hamilton, Ontario, Canada. He completed his Ph.D. in 1981 from the Indian Institute of Technology, Kanpur, India. Balakrishnan is a Fellow of the American Statistical Association, Fellow of the Institute of Mathematical Statistics, Elected Member of the International Statistical Institute, Honorary Member of the Greek Statistical Institute, and many more, including the Professor C. R. Rao Lifetime Achievement Award in 2020, by the Indian Society for Probability and Statistics. Balakrishnan's research interests include ordered data analysis, univariate and multivariate distribution theory, reliability theory, survival analysis, applied probability, stochastic orderings, nonparametric statistics, censoring methodology and statistical inference. “Bala”, as he prefers to be called, has well over 75 thousand citations of his work and an H-Index of 87! He holds editorial positions in more than 20 scholarly and professional journals, as well as over 60 distinguished visiting professorship positions in many universities around the world. He has been awarded an Honorary Doctorate Degree from the National and Kapodistrian University of Athens, Greece.

Image of Alamo Symposium in Statistics Keynote Speaker Scott

David W. Scott
Department of Statistics
Rice University

Title: Synergy of Nonparametric and Parametric Methods
Abstract: Parametric methodology by Fisher's maximum likelihood is 102 years old, largely supplanting Pearson's method of moments. Data science relies more and more on nonparametric methodology for function estimation and exploration. In this talk, we examine useful ways parametric and nonparametric approaches can complement each other, with examples in clustering.

Professor David Scott's Homepage (rice.edu)

Biography
Professor Scott's research interests focus on the analysis and understanding of data with many variables and cases. The research program encompasses basic theoretical studies of multivariate probability density estimation, computationally intensive algorithms in statistical computing, clustering, robust estimation, and data exploration using advanced techniques in computer visualization. Scott is a Fellow of the ASA, IMS, RSS, AAAS and ISI. He received the ASA Don Owen Award in 1993. He is the author of "Multivariate Density Estimation: Theory, Practice, and Visualization (John Wiley & Sons, 1992, 2nd edition 2015, and "STATISTICS: A Concise Mathematical Introduction for Students, Scientists, and Engineers (John Wiley & Sons, 2020). Scott is on the editorial board of the Probability and Statistics series of John Wiley & Sons. He served on the Committee on Applied and Theoretical Statistics, as editor of JCGS, co-editor (with W. Haerdle) of computational Statistics, and on the editorial board of Statistical Sciences. He has served as Associate Editor of JCGS, JASA, and the Annals of Statistics. Scott is currently co-editor-in-chief of Wiley's WIRES Review Journal, Computational Statistics. He has held several offices in the Statistical Graphics Section of the American Statistical Association, and elected to the Council of the IMS and IASC.

Image of Alamo Symposium in Statistics Keynote Speaker Vidakovic

Brani Vidakovic
Department of Statistics
Texas A&M University

Title: Gamma-Minimax Wavelet Denoising of Signals with Low SNR
Abstract: One of the premises of robust Bayesian approach is that prior distributions can seldom be quantified or elicited exactly. It is assumed instead that a family of priors, Gamma, reflecting prior beliefs is elicited. The Gamma-minimax decision-theoretic approach to statistical inference favors an action/rule which utilizes information specified via Gamma, but guards against the least favorable prior in Gamma. This paradigm falls between Bayesian and minimax approaches. Under mild conditions on the model structure, the problem of finding the Gamma-minimax rule can be solved by a Bayesian machinery; the optimal rule is Bayes' with respect to the least favorable prior in Gamma.

We overview some early results in Gamma-minimax approach focusing on the results of Purdue's decision theory school in the context of robust Bayesian estimation. Some recent applications of Gamma-minimax in the context of wavelet shrinkage will be discussed. We demonstrate that wavelet shrinkage achieved with simple three-point priors can be competitive in the cases when signal-to-noise ratio is low. Some related Bayesian wavelet shrinkage methods will be discussed as well.

Finally, we argue that Gamma-minimax may see a renaissance in years to come with the increasing interest in Artificial Intelligence research. Some thinkers argue that particular aspects of human decision making can be well modeled by a Gamma-minimax paradigm.

Professor Brani Vidakovic's Homepage (tamu.edu)

Biography
Branislav Vidakovic, is a professor and head of the Department of Statistics at Texas A&M University, H.O. Hartley Chair in Statistics. Vidakovic holds a Ph.D. in statistics from Purdue University. His research interests include wavelets, Bayesian statistics, biostatistics, statistics in medicine, environmental statistics, and statistical signal and image processing. He is a fellow of the American Statistical Association and an elected member of the International Statistical Institute. Vidakovic has authored or co-authored several books and numerous journal articles.

Register for Symposium Submit Abstract/Poster by Feb. 15

Please complete the registration form below to attend the 2023 Alamo Symposium in Statistics. Your registration includes lunch and dinner on March 10. The symposium will start early morning on Friday, March 10 and will end around noon on Saturday, March 11.

-For faculty and researchers, the conference registration cost is $250 if you register by February 15 and $350 for registrations after February 15. -
-For students, the registration cost is $50.

Register for Symposium

Refund policy: After March 1, only 50% of the registration fee will be refunded. No refund will be provided after March 10.

The deadline to submit an abstract or poster is Wednesday, Feb. 15 at midnight. If you plan to submit both an abstract and a poster, please submit two separate forms.
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Please check back in February 2023 for our conference agenda and featured keynote speakers.

Register for Symposium Submit Abstract/Poster by Feb. 15

Double Tree by Hilton San Antonio Northwest
6809 N Loop 1604 W Acc Rd, San Antonio, TX 78249
(210) 690-0300

Registrants are responsible to book their own accommodations. However, a room rate of $169 per night (before tax and fees) at the Double Tree by Hilton San Antonio Northwest is available through the link below.

Book Now

Register for Symposium Submit Abstract/Poster by Feb. 15

Slide 1

Get the official visitors guide and see the list of things to do and upcoming events in San Antonio.

Slide 2
Visit the Riverwalk

Listed as one of the top attractions to see in San Antonio by Tripadvisor, the Riverwalk is 15 miles long and flows right through downtown.

Slide 3
Visit our School of Data Science

The first of its kind in the state of Texas, our world-class faculty and core staff work in tandem with hundreds of students to discover, teach, learn and use data science and analytics for positive societal and economic impact. Located in downtown San Antonio, the school is the cornerstone of UTSA's 10-year strategic growth plan.

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Register for Symposium Submit Abstract/Poster by Feb. 15

For more information or questions, please email Victor.DeOliveira@utsa.edu.

Symposium Planning Committee

Victor De Oliveira
Professor of Management Science and Statistics
Min Wang Min Wang
Associate Professor of Management Science and Statistics
Ph.D. Advisor
Wenbo Wu Wenbo Wu
Department Chair of Management Science and Statistics
Graham Weston Endowed Professor
Keying Ye Keying Ye
Professor of Management Science and Statistics

Register for SymposiumSubmit Abstract/Poster by Feb. 15

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