Dr. Hui-Yi Lin’s primary research interest is developing novel statistical methods for genetic studies and omics data analyses, especially interactions of genetic variants for cancers. She also works in big data analysis and machine learning fields. Moreover, Dr. Lin has profound statistical consultation experience with health professionals across multiple disciplines, such as basic science, health behavioral studies, epidemiology studies, and clinical trials. She serves as the Director of the Biostatistics & Bioinformatics Core at the Center for Translational Viral Oncology (CTVO) and as Co-Director of the Data Analytical Unit of the LSUHSC-NO Comprehensive Alcohol-HIV/AIDS Research Center (CARC) at LSUHSC. She is the Principal Investigator (PI) for a multiple-year Department of Defense (DoD) grant for developing polygenic risk scores (PRSs) for prostate cancer. She also served as PI for several NIH grants (such as R21) and as a Co-Investigator for several NIH grants. She has more than 175 peer-reviewed publications in high-impact journals. In addition to her work at LSUHSC, she actively participates in the international prostate cancer genome consortium. Dr. Lin also has extensive working experience at the 2 NCI-designated comprehensive cancer centers: Moffitt Cancer Center and Research Institute and Comprehensive Cancer Center at the University of Alabama at Birmingham (UAB).

# Keywords: Genetic Statistics In Cancers, Omic Data Analysis, Big Data Analysis, Machine Learning, and Clinical Trial  

Dr. Qingzhao Yu serves as the interim Associate Dean at the School of Public Health. Currently, she holds the role of Contact Principal Investigator for two significant NIH grants: An R01 project titled “Interactions between ES-miRNAs and environmental risk factors are responsible for TNBC progression and associated racial health disparities: a novel analysis with multilevel moderation inferences” and an R15 project titled “Trends of disparities in breast cancer progression and health care considering multilevel risk factors.” Dr. Yu has been the Principal Investigator, co-investigator, or biostatistician on numerous grants and projects. She has been authored in more than 130 peer-reviewed publications, one book, and four statistical software packages. Her research primarily focuses on advancing statistical methods for causal inferences and high-dimensional data analysis. These methodologies have found widespread application in uncovering mechanisms underlying observed health disparities, assessing interventions and health policies, as well as optimizing experimental designs. 

# Keywords: Bayesian Method, Machine Learning and Data Mining Techniques, Mediation Analysis, Optimal Experimental Designs, and Spatial Data Analysis.

Dr. Donald Mercante‘s research interests include design and analysis of experiments, multivariate permutation methods, and statistical models for correlated data. He is the principal investigator for the NIH-funded Data Management and Analysis Core for the LSU Superfund Research Center and the Co-Director of the Biostatistics and Epidemiology Research Design Core for the NIH-funded LaCATS Center grant at LSU-HSC. 

# Keywords: Clinical Trials, Study Design, Permutation Methods, and Correlated Data

Dr. Zhide Fang’s research interests include (1) developing novel statistical methodologies for analyzing high-throughput, large-scale genomic, epigenomic metagenomics data; (2)  developing efficient algorithms for Big Data; (3) developing methods and algorithms for constructing model-based experimental designs for the development of statistical theory.

# Keywords: Gene Expression, DNA Methylation, Microbiome, Big Data, and Machine Learning

Dr. Evrim Oral’s statistical methodology research involves several different areas. She specifically focuses on proposing robust estimation in generalized linear models (GLM) and survey sampling. She also develops novel estimators that utilize randomized response techniques (RRT) for sensitive topics. Her methodology research also includes proposing new estimation techniques and statistical tests for data subject to limit of detection (LOD). She does research on several other areas of survey sampling as well, such as ranked set sampling and assessing the effects of unit nonresponse and attrition in health-related studies. Her collaborative research interests are diverse. She has been working as a Co-investigator on several NIH-funded projects such as Quality of Life in Prostate Cancer Project (QPCaP) and The Women and Their Children’s Health (WaTCH). She worked for the LSU Health Care Services Division for many years and utilized big data from electronic health records (EHR) to improve LSU patients’ outcomes. She has been providing statistical consulting and supporting research in many departments and schools of LSUHSC, such as OB/GYN, Otolaryngology, Emergency Department, and School of Nursing. She is currently doing research with the Comprehensive Alcohol-HIV/AIDS Research Center (CARC) researchers and evaluating the effects of alcohol use in people with HIV.

# Keywords: Generalized Linear Models, Robust Statistics, Survey Methodology, Surveying Sensitive Topics, and Level of Detection

Dr. Anand Paul’s research interests are centered on the development of advanced mathematical and statistical models for Artificial Intelligence (AI) and Machine Learning (ML) systems. A key focus of my work is on exploring and formulating novel machine learning algorithms, utilizing techniques such as Markov Decision Processes and Hidden Markov Models to develop robust intelligence and knowledge discovery frameworks. Through these efforts, I aim to enhance the performance, reliability, and interpretability of AI systems.  I have experience in working with intelligent agents (reinforcement learning), using attention mechanisms in Natural Language Processing, and feature selections and dimensionality reduction techniques. My current research is focused on developing efficient mathematical models for gradient descent in ML systems, and Bayesian frame for selective quantization. Through these endeavors, I continuously seek to bridge theoretical advancements with practical applications, contributing to the evolution of resilient and intelligent AI systems. In LSUHSC I have started to work with Orthopedic data to provide insight into various intersections of AI with healthcare, aiming to improve diagnostic accuracy and patient outcomes.

# Keywords: Evolving Intelligence, Machine Learning, Mathematical Modeling, Statistical Analysis, Knowledge Discovery

Dr. Joonha Chang focuses on the methodological development of advanced algorithms. His specialties include latent class clustering, non-homogeneous continuous-time Markov chains, optimization, and signal processing algorithms. Dr. Chang aims to develop and refine methodologies to advance medical research and healthcare. His research projects include: (1) Latent classification of multi-modal non-homogeneous continuous-time Markov chains, (2) Integer programming formulation for clinical scheduling, and (3) Signal processing mobile typing patterns to passively detect intoxication.

# Keywords: Latent Class Clustering, Markov Chain, Integer Programming, Optimization, Signal Processing