Research Areas
Biography and Research Information
OverviewAI-generated summary
Shahrin Shahpar's research centers on statistical modeling and computational efficiency, particularly in the context of environmental phenomena. Their recent work explores methods for enhancing the statistical modeling of wildfire aerosols, employing a heterogeneous approach that leverages both R and GPU acceleration for improved efficiency. This research also touches upon the study of extreme precipitation events. Shahpar collaborates with Miaoqing Huang and Yaqian He at the University of Arkansas at Fayetteville, with whom they have co-authored three publications. Their work contributes to the fields of environmental science and computational statistics, aiming to refine analytical techniques for complex environmental data.
Metrics
- Publications: 3
Selected Publications
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Extreme precipitation (2025)
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Enhancing Efficiency in Statistical Modeling of Wildfire Aerosols: A Heterogeneous Approach with R and GPU Acceleration (2025)
Collaboration Network
Top Collaborators
- Enhancing Efficiency in Statistical Modeling of Wildfire Aerosols: A Heterogeneous Approach with R and GPU Acceleration
- Extreme precipitation
- Enhancing Efficiency in Statistical Modeling of Wildfire Aerosols: A Heterogeneous Approach with R and GPU Acceleration
- Enhancing Efficiency in Statistical Modeling of Wildfire Aerosols: A Heterogeneous Approach with R and GPU Acceleration
- Extreme precipitation
- Extreme precipitation
- Extreme precipitation
- Extreme precipitation
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