Welcome to the LSU Department of Experimental Statistics


Seminar Announcement

Tingting Hou

Department of Biostatistics, University of Florida

"Kernel Neural Networks for High-Dimensional Genetic Data Analysis"

April 26, 2024 at 1:30 p.m. to 2:30 p.m.

E134 Howe-Russell-Kniffen

The recent development of artificial intelligence (AI) technology, especially the advance of deep neural network (DNN) technology, has revolutionized many fields. While DNN plays a central role in modern AI technology, it has been rarely used in sequencing data analysis due to challenges brought by high-dimensional sequencing data (e.g., overfitting). Moreover, due to the complexity of neural networks and their unknown limiting distributions, building association tests on neural networks for genetic association analysis remains a great challenge.

In this talk, Hou will introduce recent efforts to address these challenges and fill the important gap of using AI in high-dimensional sequencing data analysis. In the first, Hou will introduce a new kernel-based neural network (KNN) test for complex association analysis of sequencing data. The test is built on our previously developed kernel-based neural network model (KNN) framework. Based on KNN, a MINQUE-based test is introduced to evaluate the joint association of high-dimensional genetic data with a disease phenotype of interest, considering non-linear and non-additive effects (e.g., interaction effects). In the second part, Hou will present a mixed neural network (MNN) framework for high-dimensional risk prediction research. MNN is a novel neural network approach that inherits features from both linear mixed models (LMM) and classical neural networks to model high-dimensional data and complex genotype-phenotype relationships. Through simulation studies, we demonstrate the advantages of MNN for high-dimensional risk prediction analysis in terms of robustness and accuracy.


Seminar Announcement

Dr. Kai Kang

Research Scientist within Computer Science and Artificial Intelligence Laboratory (CSAIL)
Massachusetts Institute of Technology (MIT)

"Deciphering Biological Complexity: A Statistical Approach"

April 19, 2024 at 1:30 p.m. to 2:30 p.m.

E134 Howe-Russell-Kniffen

Understanding the intricate biological mechanisms underlying diseases, such as cancer and neurodegenerative disorders, is crucial for developing effective therapies. Genomics and transcriptomics offer invaluable insights into disease pathology. Next-generation sequencing (NGS) and third generation sequencing (TGS) ease high-throughput RNA/DNA sequencing, revolutionizing biomedical research. The unprecedented amount of data generated daily needs an interdisciplinary effort to address computational challenges. In this talk, Kang will discuss a recent project that highlights the challenges and opportunities in computational biology, highlighting how statistical approaches can lead to deeper biological insights.

He will share recent work on developing statistical methods that computationally untangle complex mixtures of cell types from bulk RNA sequencing data. A notable feature of their approach is its capability to simultaneously estimate individual-level, cell type-specific transcriptomics profiles and cell-type proportions. The method uses Bayesian hierarchical modeling to capture the heterogeneous nature of mixture data, employing a Markov Chain Monte Carlo procedure for parameter estimation and hyperparameter tuning. By deciphering cellular heterogeneity in complex tissues and improving downstream analyses of bulk RNA-seq data and spatial transcriptomics, the method holds significant promise for affecting biomedical research.

Seminar Announcement

Dr. Enayet Talukder

Retired from Pfizer

"Navigating the Complexities: Drug Development and Innovative Clinical Trial Designs for Next-Generation Oncology Therapies"

Friday, April 12, 2024, 1:30 pm – 2:30 pm

E134 Howe-Russell-Kniffen

This presentation will explore the prescription drugs development process, from initial discovery to market approval, focusing on clinical trial designs for each development phase for Oncology therapies. The landscape of cancer treatment is rapidly evolving. The rise of targeted cancer therapies necessitates more intricate study designs. Unlike traditional chemotherapies, these therapies have distinct mechanisms of action, demanding innovative approaches to design to capture their effects. The objective of a Phase 1 clinical trial for chemotherapy is to obtain the maximum tolerated dose (MTD), while a targeted therapy may seek an optimum biological dose (OBD). Similarly, tumor shrinkage (endpoint: tumor response) may not be an appropriate efficacy measure for a targeted therapy when it slows the disease progression and helps patients survive (endpoint: progression-free survival) longer. This presentation highlights the key considerations for designing clinical trials, from selecting the appropriate patient population to determining sample size (by controlling Type I and Type II Error probabilities). We will review real-life examples of innovative designs in cancer patients.


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