معرفی
Souparno Ghosh is an Associate Professor in the Department of Statistics at the University of Nebraska–Lincoln, affiliated with the College of Agriculture & Natural Resources and the Institute of Agriculture and Natural Resources (IANR). His research focuses on Bayesian hierarchical models, machine learning applications in image and functional data analysis, bioinformatics, and developing interpretable statistical methods for healthcare and precision agriculture.
His work addresses challenges such as drug response prediction through deep learning frameworks, transfer learning across heterogeneous datasets, and uncertainty quantification in medical decision-making. Recent projects include topological regression models for QSAR analysis, federated learning systems integrating CNNs and regression forests, and spatio-temporal modeling of infectious diseases.
Dr. Ghosh has published extensively on topics ranging from cancer drug sensitivity prediction to agricultural phenotyping via computer vision. His methodological contributions include Bayesian variable selection techniques and novel feature representation approaches like REFINED CNN for improving neural network performance. Collaborative efforts span interdisciplinary domains, including oncology, agronomy, and disaster recovery analysis.
Key research trends in his publications emphasize: (1) integrating statistical rigor with machine learning for interpretability, (2) developing scalable algorithms for omics and imaging data, and (3) addressing challenges in model generalization and data heterogeneity. His work frequently bridges theory and application, with applications in precision medicine and sustainable agriculture.
No scientific awards or grants are explicitly listed in the provided information, but his active publication record and methodological innovations suggest significant contributions to computational statistics and data science.
