Welcome to the LSU Department of Experimental Statistics

Seminar Announcement

Katherine Thompson
Associate Professor, Dr. Bing Zhang Department of Statistics
University of Kentucky

"Artificial Intelligence and Machine Learning Use Cases: Immersive Training Examples"

May 2, 2025, 1:30pm to 2:25pm
Red River, 323 Student Union

Artificial Intelligence (AI) and Machine Learning (ML) are rapidly expanding fields transforming education, research, and everyday life. This presentation will overview AI applications from agriculture to medicine and will describe how a gathering place for AI/ML consumers, users, and developers at the University of Kentucky produced immersive learning experiences for undergraduate and graduate students alike. Details will be provided about the University of Kentucky AI/ML Hub student learning experience at the interface of healthcare and ML. Using data from a Kentucky hospital, graduate students from multiple colleges received interdisciplinary training to build ML models that identify patients in Kentucky at high risk for missing colorectal cancer screenings in the Eastern region of Kentucky.

Seminar Announcement

Dr. Jenny Price

Nigatta University, Japan

"Machine Learning for Predicting Type 2 Diabetes"

April 25, 2025, 1:30pm to 2:25pm
Room: 116 Stubbs Hall

In this talk, Dr. Price will discuss the problem of healthcare becoming more difficult as the population grows older, and the less available specialists there are for diseases, specifically in this talk for type 2 diabetes. As more people need to see specialist care, but have no availability to do so, general practitioners step in to meet the need. Which therefore leads to an increased need for machine learning to help fill the gap between these two. We will discuss the problems currently facing machine learning as we try to tackle the challenge of medical data, distrust from medical practitioners, and machine learning limitations in healthcare to hopefully help to improve the care of patients. By using a combination of models, over sampling, cross validation, and continuous learning we can create models that can create a usable model for practitioners to use as a second opinion.

Seminar Announcement

Daiqi Gao

Postdoctoral Fellow

Department of Statistics

Harvard University

"Online Reinforcement Learning for Personalized Digital Interventions"

April 11, 2025, 1:30pm to 2:25pm
Room: M-Red River 329 LSU Student Union

HeartSteps is a mobile health intervention designed to help sedentary users increase and maintain physical activity. A key component involves
delivering activity suggestions via smartphone notifications to encourage short bouts of physical activity (PA). We use online reinforcement learning (RL) algorithms to learn the optimal policy for delivering notifications based on each user's evolving needs. The reward in the RL algorithm is defined as a user's commitment to PA, measured through weekly surveys. While there are 14 decision points per week, the reward is only observed at the end of the week, creating a challenge of bagged decision times with non-Markovian and non-stationary dynamics within each bag. To address this challenge, we leverage an expert-provided causal directed acyclic graph (DAG) to construct the states and formulate the problem as a periodic Markov decision process.

Seminar Announcement

Li Hsiang Lin

Assistant Professor, Department of Mathematics and Statistics

Georgia State University

"Deep P-Spline: Fast Tuning, Theory, and Application"

March 28, 2025, 1:30pm to 2:25pm
Room: LSU Student Union, C-Capital Chamber 329

Optimizing the structure of deep neural networks (DNNs), including the number of neurons and layers, is a challenging task. This talk introduces Deep Penalized Spline (DPS), a novel framework that redefines neuron selection as a knot placement problem in spline expansions. By incorporating a diJerence penalty, DPS automates the network structure selection process, enhancing computational eJiciency and precision of conventional DNN. Embedded within a latent variable model, the method enables an eJicient algorithm for optimizing the network architectures, bridging statistical methodologies with modern network design. Numerical studies demonstrate the scalability and practical effectiveness of DPS. Theoretical properties and diverse applications of DPS will also be explored.

Seminar Announcement

Yong Zang

Showalter Scholar Associate Professor, Indiana University School of Medicine

"Curve-free dose optimization designs for oncology trials"

March 14, 2025, 1:30pm to 2:25pm
Room: LSU Student Union, M-Castilian 304

We propose two curve-free phase I/II clinical trial designs to optimize the dose of molecularly targeted agents (MTAs) and immunotherapies (ITs) by jointly modeling toxicity, efficacy, and immune response outcomes. Instead of employing complex parametric models, our approach leverages the inherent correlations among different clinical outcomes and incorporates the constrained dose-outcome order to enable efficient information sharing across doses. These designs enhance both the efficiency and transparency required for practical implementation in clinical settings. We also provide simulation studies and software demonstrations to illustrate the application of the proposed designs.

Seminar Announcement

Jeremy Brown

Professor, Department of Biological Sciences
Museum of Natural Sciences
Louisiana State University

"Bayesian Inference of the Evolutionary Past Using Genomic Data"

February 7, 2025, 1:30pm to 2:25pm
116 Stubbs Hall

Biologists have been interested in reconstructing the history of biological diversification on Earth ever since the time of Darwin. This field of study has since become known as phylogenetics. Phylogenetics was revolutionized starting in the early 1980s with the application of formal statistical approaches to inference using genetic data. Technical advances in the mid-2000s made it possible to sequence full genomes much more cheaply and quickly than ever before, which has radically increased the amount of available data. However, many challenges and open questions remain about how best to handle the scale and complexity of full-genome data in likelihood-based statistical frameworks. In this talk, I will discuss some strategies that my lab is pursuing to more efficiently and effectively reconstruct the evolutionary past from genomic data using Bayesian methods.

Seminar Announcement

Vasileios Maroulas

Associate Vice Chancellor for Research, Director of the AI Tennessee Initiative, Professor of Mathematics

University of Tennessee

"Grid Cell Navigation via Simplicial Convolutional Recurrent Neural Network"

November 8, 2024, 1:30pm to 2:20pm
145 Coates Hall

The brain's spatial orientation system relies on specific neuron groups for navigation, such as head direction cells for orientation and grid cells for mapping environments. These neurons work together in patterns, firing simultaneously to create directional and positional signals. To better understand and decode these patterns, we developed a new topological deep learning model that goes beyond traditional graph-based approaches. Our model, a simplicial convolutional recurrent neural network, uses topological structures to capture complex neural relationships. This method allows us to predict head direction and location from neural data without needing prior similarity measures, proving effective in head direction and trajectory prediction.

Seminar Announcement

Susan Murphy

Professor of Statistics and Computer Science

Harvard University

"Online Reinforcement Learning in Digital Health Interventions"

October 28, 2024, 1:30pm-2:20pm
134 Howe Russell East

In this talk, Murphy will discuss first solutions to some of the challenges we face in developing online RL algorithms for use in digital health interventions targeting patients struggling with health problems such as substance misuse, hypertension and bone marrow transplantation. Digital health raises a number of challenges to the RL community including different sets of actions, each set intended to impact patients over a different time scale; the need to learn both within an implementation and between implementations of the RL algorithm; noisy environments and a lack of mechanistic models. In all of these settings the online line algorithm must be stable and autonomous. Despite these challenges, RL, with careful initialization, with careful management of bias/variance tradeoff and by close collaboration with health scientists can be successful. We can make an impact!

Seminar Announcement

Michael Beck

Senior Chemist, Reference Atmospheres, LLC

"From Opticks to Optik: Parallel Histories in Statistics and Physics"

September 27, 2024, 1:30pm-2:20pm
145 Coates Hall

Statistics is a young discipline! One entertaining way to make this point is to map a chronology of landmark statistical publications in parallel with those of a more mature discipline, namely physics. What stands out is that physics transitioned from classical, Newtonian physics into modern physics during the same decades that saw the emergence of classical, or frequentist statistics.

All attending, will be eligible for dozens of door prizes (mainly used statistical books).

View September 27 Seminar Annoucement

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.

View September 27 Seminar Annoucement

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