We develop statistical methodology and computational tools for complex longitudinal, causal, biomedical, network-structured, imaging, and nonlinear data. Our work connects rigorous inference, modern machine learning, open-source software, and scientific applications.
Lab members
Director
Dhrubajyoti Ghosh, PhD
Assistant Professor, Data Science and Analytics
Longitudinal methods · causal inference · time series · biomedical statistics
Doctoral researchers
Imaan Shahid
PhD student, Data Science and Analytics
Longitudinal rank-based inference, incomplete clinical-trial data, and statistical software.
Param V. Kesireddy
PhD student, Data Science and Analytics
Machine learning, statistical computing, and structured data-science methods.
Faruk Muritala
PhD student, Data Science and Analytics; co-advised with Austin Brown
Nonparametric methodology and data-driven applications.
Master’s and recent master’s researchers
Pranay Kumar Peddi
M.S., Computer Science
Causal inference with graph neural networks for Alzheimer’s disease.
Hemanth Reddy Mandla
M.S., Data Science and Analytics
Data-science methodology and applied statistical computing.
Shelby Bradley
M.S., Data Science and Analytics
Recent master’s researcher in the lab.
Undergraduate researchers
Aya Vera-Jimenez
AI-generated misinformation, cross-prompt generalization, and interpretable modeling.
Samuel Jaeger
AI-generated misinformation and ensemble learning.
Calvin Ibenye
Machine-vs-human misinformation detection and adversarial evaluation.
Research areas
Click a research area to open the corresponding program on the Research page.
Longitudinal & Clinical Trial Statistics
LRST, multivariate longitudinal outcomes, multi-arm trials, missing data, covariate adjustment, design, power, and sample size.
Imaging & Neurodegenerative Disease
Alzheimer’s and Parkinson’s disease, MRI/CT, digital twins, survival prediction, disease staging, and virtual trials.
Nonparametric & Robust Inference
Rank procedures, U-statistics, global tests, distribution-free methods, resampling, and inference under complex data structures.
Data Science & AI
Machine learning, graph learning, representation learning, AI/misinformation, structured high-dimensional data, and computational methods.
Time Series & Higher-Order Dependence
Polyspectra, nonlinear processes, quadratic prediction, higher-order frequency-domain inference, and bootstrap methods.
Causal Inference & Discovery
Causal discovery, instrumental variables, Mendelian randomization, network-aware estimation, and graph-structured causal methods.
Active project clusters
LRST and longitudinal inference
Extensions of longitudinal rank-sum methodology to incomplete data, covariate adjustment, observational studies, multi-arm designs, and global treatment-effect testing.
Network-aware causal inference
Instrumental-variable methods, Mendelian randomization, PFCI, graph regularization, and causal node discovery in high-dimensional systems.
Neurodegeneration & imaging
Alzheimer’s and Parkinson’s disease, imaging-derived predictors, disease progression, survival, staging systems, and virtual imaging trials.
AI, networks & population data
Graph learning, misinformation detection, environmental and spatial health, socioeconomic outcomes, and other structured data-science applications.
Join the lab
We welcome motivated undergraduate, master’s, and doctoral students interested in statistics, data science, causal inference, machine learning, biomedical applications, or time series. Prospective students should email dghosh3@kennesaw.edu with a CV, a short description of research interests, and relevant quantitative or programming experience.