Dr. Dhrubajyoti Ghosh | Clinical Trials | Best Researcher Award

Postdoctoral Research Scholar at Duke University Hospital | United States

Dr. Dhrubajyoti Ghosh is a Postdoctoral Research Associate at the Department of Biostatistics and Bioinformatics, Duke University. His academic and research trajectory spans advanced statistical methodologies with strong applications in data science, biostatistics, longitudinal analysis, and time series modeling. Dr. Ghosh has contributed to diverse interdisciplinary projects, including Alzheimer’s disease, air quality, stock markets, and social media analytics. He has published in top-tier journals and collaborated on multiple funded projects, combining theory with real-world biomedical and environmental challenges. His work exemplifies a balanced integration of innovative statistical theory, computational tools, and impactful applied research.

Publication Profile 

Orcid

Education 

Dr. Ghosh earned his Ph.D. in Statistics from Washington University in St. Louis , where his thesis focused on time series analysis, uncertainty quantification, and applications in data science. He was advised by Professors Soumendra Lahiri and Tucker McElroy. Before that, he completed his M.Stat.  and B.Stat.  from the prestigious Indian Statistical Institute, Kolkata. His academic foundation has provided him with a rigorous grounding in statistical theory, methods, and real-world data analytics, enabling his impactful contributions in both academic and interdisciplinary research areas, including clinical trials and predictive modeling.

Experience 

Dr. Ghosh has served as a Postdoctoral Research Associate at Duke University, collaborating on biostatistical projects under Prof. Sheng Luo. , he held teaching assistant roles at Washington University in St. Louis and North Carolina State University. He also worked as a Research Assistant at the Laboratory for Analytic Sciences. His experience spans statistical software development, teaching undergraduate and graduate statistics, and collaborative research in longitudinal modeling, neurodegenerative diseases, and social media analysis, demonstrating a blend of strong theoretical foundation and practical implementation in applied statistics.

Awards and Honors 

Dr. Ghosh has received multiple recognitions throughout his academic career, including selection for student paper competitions at the International Indian Statistical Association (IISA) Conference. His work has been featured in renowned conferences such as JSM, IISA, LAS Symposium, and SDSS. He has contributed to prominent journals and served as a key collaborator in major interdisciplinary research projects, especially in medical imaging and neurodegenerative disease studies. His publications in high-impact journals, including Journal of Alzheimer’s Disease and Statistics in Biopharmaceutical Research, underline his growing reputation as an innovative and impactful researcher in modern statistical applications.

Research Focus 

Dr. Ghosh’s research focuses on advanced statistical methodologies, including time series analysis, non-parametric methods for longitudinal clinical trials, ensemble survival analysis, and predictive modeling using biomarkers. His contributions extend to social media analytics, air quality extremes, and causal inference in neuroscience. He has developed statistical tools for model validation, goodness-of-fit, and has proposed novel testing strategies for clinical applications. His interdisciplinary work involves statistical consulting in medical imaging, Alzheimer’s disease progression, and machine learning integration. His research addresses key methodological gaps in healthcare data science, striving for robust, interpretable, and scalable statistical solutions in public health.

Publication Top Notes

• THANOS: A Predictive Model of Electoral Campaigns Using Twitter Data and Opinion Polls

• A Non-Parametric U-Statistic Testing Approach for Multi-Arm Clinical Trials with Multivariate Longitudinal Data

• Demographic Distribution Matching Between Real World and Virtual Phantom Population

• XCAT 3.0: A Comprehensive Library of Personalized Digital Twins Derived from CT Scans

• Penalized FCI for Causal Structure Learning in a Sparse DAG for Biomarker Discovery in Parkinson’s Disease

• Polyspectral Mean Based Time Series Clustering of Indian Stock Market