I am currently involved a number of projects:
I am part of a team of national lab researchers working on the IDAES Process Systems Engineering (IDAES PSE) Framework, a next generation multi-Scale modeling and optimization Framework to support the US power industry. The project is funded by the US Department of Energy’s Office of Fossil Energy through the Simulation-Based Engineering, Crosscutting Research Program.
I am developing equation-oriented surrogate modelling tools to aid in the design and optimization of advanced energy systems. The tools provide a way for important legacy simulations and pilot-stage energy generation technologies to be integrated into the IDAES PSE framework for techno-economic analysis and performance optimization. The highlight of my contributions so far is the development of
PySMO , an open-source surrogate modelling software providing users with a set of surrogate modeling tools which support flowsheeting and direct integration into a Pyomo and IDAES.
IDAES was a
winner of the prestigious 2020 R&D 100 award.
NAWI is a DOE-funded Energy-Water Desalination Hub headquartered at Berkeley Lab.
As part of NAWI, I am an active contributor to the development of the
Water treatment Technoeconomic Assessment Platform (WaterTAP), an open-source Python-based software package that supports the technoeconomic assessment of full water treatment trains and advanced water desalination systems. Details about the WaterTAP platform may be found on GitHub
here.
I am also heavily involved in research into how information from complex high-fidelity first principles and black-box models can be integrated into open-source EO-optimization frameworks such as WaterTAP using surrogate and AI/ML models.
ScienceSearch is a platform that enables search across different types of scientific data. ScienceSearch is funded out of the Office of Advanced Scientific Computing Research (ASCR), U.S. Department of Energy.
I am part of a team of researchers developing machine learning techniques for text labeling and automated metadata generation that will help improve indexing and scientific search.