معرفی
Sharmistha Guha is an Assistant Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. Her research focuses on Bayesian methods for analyzing complex, high-dimensional data, with applications in neuroscience, network security, and social sciences. She develops techniques for supervised network data analysis, causal inference, and data privacy preservation.
Education: Ph.D. in Statistical Science (Duke University, implied by dissertation recognition).
Research Interests: Bayesian high-dimensional regression, object-oriented regression, causal inference in randomized trials, probabilistic record linkage, Bayesian nonparametric mixture models, and integration of heterogeneous data types like networks and functional data. Her work addresses challenges in neuroimaging (dMRI/fMRI) and big data analytics.
Recent Contributions: Her articles span Bayesian methodologies for network analysis, differential privacy in treatment effect estimation, and applications in environmental health (e.g., PM2.5 exposure studies). Notable trends include leveraging Bayesian hierarchical models for complex data structures and advancing causal inference frameworks.
- Awards:
- 2022 Blackwell-Rosenbluth Award (ISBA)
- 2021 Savage Award Honorable Mention
- 2024 NSF-DMS Grant 2413721
- Grants & Advising:
- Secured NSF funding for heterogeneous data integration research.
- Mentored students like Jose Rodriguez-Acosta (NSF GRFP Fellow) and Jacob Pagel (NIH-funded CoSIBS participant).
Lab/Team: Leads a research group integrating statistical method development with domain collaborations in neuroscience and network security. Active in organizing workshops and serving on panels to foster academic engagement.
