Bio
I am a researcher at National Renewable Energy Laboratory (NREL) and earned my Ph.D. from the University of Michigan in Aerospace Engineering.
My work revolves around Scientific Machine Learning, the simulation of Complex fluid flows with High-Performance Computing, and Uncertainty Quantification. I develop methods tailored for renewable energy applications such as atmospheric flows, batteries, deposition reactors, and efficient engines.
Scientific Machine Learning (SciML)
The amount of data routinely generated by scientific calculations is an opportunity to improve our models, make them more efficient and extract novel information. I look for methods that intelligently use data and ML-inherited methods to solve problems that could not be tackled before. I work on:
- Probabilistic data augmentation
- Surrogate models and reduced-order models
- Information extraction from large database
- Data reduction methods
Complex fluid flows and High-Performance Computing (HPC)
Many engineering applications depend on some form of fluid mixing possibly coupled with small-scale phenomena, whether it be a bubbling flow in a bubble column reactor, a surface reaction in a deposition reactor, or an ignition kernel in an aircraft engine. Efficient numerical simulations that take advantage of novel computing architectures can enable affordable design optimization and allows gaining a deeper understanding of the limits and potential of the system at stake. I work on:
- Minimally dissipative methods
- Turbulent combustion modeling
- Chaotic dynamics of turbulence
- Surface reaction modeling
- Analytically reduced chemistry for HPC
Uncertainty quantification (UQ)
Decisions in energy systems typically involve financial an societal consequences. Therefore, appropriate quantification of uncertainty can enable critical decision-making. Uncertainty estimates are useful to objectively assess whether more information is needed and avoid over-confident conclusions. When decicions about extreme and rare events are needed, uncertainty estimates are even more instrumental but can be particularly challenging to compute. I work on:
- Bayesian inference
- Rare event probability estimation
- Uncertainty propagation