Welcome to the LSU Department of Experimental Statistics


Seminar Announcement

Christopher Peters, MApStat

Chief Data Scientist at Touch, employee 9 and former Principal Data Scientist at Zapier

"AI-Driven Business Innovation: Harnessing Bayesian Multilevel Models and Probabilistic Programming at the Intersection of Statistics, Decision Theory, and Game Theory"

Friday, February 2, 2024, 1:30pm-2:30pm

E134 Howe-Russell-Kniffen

This presentation delves into the innovative integration of artificial intelligence (AI) with advanced statistical methods, decision theory, and game theory, emphasizing their application in business for enhanced profitability and strategic advantage. The focus is on Bayesian multilevel models and their pivotal role in powering AI systems alongside LLMs and other methods. Attendees will gain insights into how these models effectively manage complex data structures and their importance in AI applications. Additionally, the talk will shed light on probabilistic programming, illustrating its role as a critical link that unites statistical modeling with machine learning in business contexts. Moreover, the synergy of decision theory and game theory with AI and Bayesian methodologies will be discussed, underscoring how these combined approaches inform optimal decision-making and strategy development in competitive business environments. Practical examples and real-world case studies will be presented to demonstrate the transformative potential of these integrated methods in shaping the future of data-driven business strategies.

View February 2 Seminar Annoucement

Seminar Announcement

Qing Guo, Ph.D.
Virginia Tech, Department of Statistics

"Advancing Data Acquisition: A Novel Contrastive Mutual Information Estimator for Bayesian Optimal Experimental Design"
Dec. 1, 2023 at 1:30 p.m. to 2:30 p.m.
E0134 HRKE

The efficiency of data collection is crucial in many areas, including agriculture, engineering, and intelligent conversational systems. In this talk, Guo will present her recent work on optimizing the data collection strategy by developing advanced machine-learning techniques. The proposed approach centers around leveraging the power of deep neural networks and maximizing the information gained from data within the framework of Bayesian optimal experimental design (BOED). To measure the information gain, she will introduce an innovative contrastive mutual information (MI) estimator to serve as an information-rich criterion under the BOED framework. This new MI estimator addresses the drawbacks of existing estimators by eliminating the need for explicit probabilistic descriptions of the model or likelihood functions. The performance of the proposed method is evaluated by both numerical examples and real applications.

View Dec. 1 Seminar Announcement

Seminar Announcement

Andrew G. Chapple, Ph.D.
Biostatistics Core Director

Stanley S. Scott Cancer Center & LSUHSC School of Medicine
Nov. 3, 2023, 1:00 - 1:50 p.m.
E0134 HRKE

A multi-armed trial based on ordinal outcomes is proposed that leverages a flexible non-proportional odds cumulative logit model and numerical utility scores for each outcome to determine treatment optimality. This trial design uses a Bayesian clustering prior on the treatment effects that encourages the pairwise null hypothesis of no differences between treatments. A group sequential design is proposed to determine which treatments are clinically different with an adaptive decision boundary that becomes more aggressive as the sample size or clinical significance grows, or the number of active treatments decreases. A simulation study is conducted for three and five treatment arms, which shows that the design has superior operating characteristics (family wise error rate, generalized power, average sample size) compared to utility designs that do not allow clustering, a frequentist proportional odds model, or a permutation test based on empirical mean utilities.

View Nov. 3 Seminar Announcement

Seminar Announcement

JMP Pro Statistical Software for Teaching and Research

Sept. 15, 2023

JMP Pro statistics software combines comprehensive statistical capabilities with an interactive, no-code interface. Its ease-of-use makes it a strong teaching tool, and its powerful analysis capabilities have led to adoption across academia and industry. All LSU faculty and students have access to JMP Pro (download on tigerware.lsu.edu).

On Sept. 15, the JMP Academic team will deliver two seminars on JMP Pro for basic-to-advanced data visualization, statistical modeling, and machine learning.

View Sept. 15 Seminar Announcement

Join the LSU Department of Experimental Statistics to prepare for an exciting career in data analysis. As the primary source of statistical education, research, and service at LSU and the LSU AgCenter, our faculty is focused on providing you with the skills and knowledge you need to thrive in this field.

Our department has a strong orientation towards applied statistics, and we offer both thesis and non-thesis programs leading to a Master of Applied Statistics (M.Ap.Stat.) degree and Ph.D. in Statistics. With a range of specializations, our programs are tailored to help you achieve your unique career goals.

Discover our unparalleled opportunities for research and expert statistical support, and join our community of passionate statisticians today.

Thanos Gentimis and President Tate
Combine harvests field

President William F. Tate IV speaks to LSU assistant professor in experimental statistics, Thanos Gentimis. He discusses his field of expertise, which is data analytics with a special interest in machine learning and neural networks. Thanos created the first digital agriculture class at LSU and discusses how AI will soon shape the world of agriculture and farming.

Read more about the future of AI at LSU