Utpal Kumar

Utpal Kumar

Geophysicist | Geodesist | Seismologist | Open-source Developer I am a geophysicist with a background in computational geophysics, currently working as a postdoctoral researcher at UC Berkeley. My research focuses on seismic data analysis, structural health monitoring, and understanding deep Earth structures. I have had the opportunity to work on diverse projects, from investigating building characteristics using smartphone data to developing 3D models of the Earth's mantle beneath the Yellowstone hotspot. In addition to my research, I have experience in cloud computing, high-performance computing, and single-board computers, which I have applied in various projects. This includes working with platforms like AWS, Docker, and Kubernetes, as well as supercomputing environments such as STAMPEDE2, ANVIL, Savio and PERLMUTTER (and CORI). My work involves developing innovative solutions for structural health monitoring and advancing real-time seismic response analysis. I am committed to applying these skills to further research in computational seismology and structural health monitoring.

Speeding Up Your Code with Parallel Computing in Python

macro photography of black circuit board
Parallel computing is essential for handling large datasets efficiently. In this post, we explore Python's threading, multiprocessing, and joblib libraries to speed up code execution. Learn the differences between threading and multiprocessing, and understand how to use joblib for optimized parallel processing, especially with NumPy arrays.

Topographic map clipped by coastlines in Python

Learn how to create visually compelling relief maps in Python by clipping topographic data to specific geographic boundaries, such as coastlines. This tutorial walks you through the process of using matplotlib and Basemap to focus your visualizations on land features within the Taiwan coastline, or alternatively, highlight bathymetric features by masking the land.

Plotting the geospatial data clipped by coastlines in Python

In this post, we explore how to perform geospatial interpolation using the Ordinary Kriging method and visualize the results within the coastline borders of Taiwan. Learn how to efficiently handle irregularly distributed geospatial data and automate the process of clipping data outside geographic boundaries for more accurate and meaningful visualizations.