Author: hcho

My ongoing parallel computing research introduces a memory-efficient watershed delineation algorithm using OpenMP. These figures compare the performance of the new algorithm and its variants (mefshed*) with that of a fast CUDA-based benchmark algorithm published earlier this year (wdg). Try it yourself: https://github.com/HuidaeCho/midas

I was awarded as a Co-PI the NSF award 2303651 titled “POSE: Phase II: Growing GRASS OSE for Worldwide Access to Multidisciplinary Geospatial Analytics.”

I was awarded as the PI a contract titled “Leveraging PARETO for Rare-Earth Elements (REE)/Critical Minerals (CM) Recovery from Produced Water and Seismicity Response Optimization” as a part of the DOE NETL SA contract.

I was subawarded as a Senior Personnel researcher a water availability project for northern New Mexico as a part of the NSF award 2308358 titled “DISES: Water and Community Resilience Through Spatial Integration of Ecohydrological Processes and Traditional Sociocultural Knowledge.”

My recent research is published in Environmental Modelling and Software. This study introduces a new parallel algorithm called MEFA for calculating flow accumulation using the OpenMP API. The new algorithm has improved the performance of the fastest benchmark parallel algorithm by 30% using up to 17% less memory. Try it yourself: https://github.com/HuidaeCho/midas

Drs. Dan Ames, Gustavious Williams, Xiaohui Qiao, and I are running a Special Issue of the Water journal “Advances in Hydroinformatics for Water Data Management and Analysis, Volume II.”

I call for papers for a Special Issue of the Hydrology journal “Big Data and Machine Learning in Hydrology: Recent Advances and Trends.”

I started as a Co-PI a new project “Culvert Asset Management Program” Phase 2 funded by NMDOT. I will be responsible for GIS database management and hydrologic/hydraulic analysis.