Malik Hassanaly uses his expertise in statistical methods, machine learning, and computational fluid dynamics to develop more accurate and efficient numerical models. While obtaining his doctorate, Malik mostly worked on turbulent combustion applications with an emphasis on aircraft engines and continues to work on combustion-related topics. At NREL, he also works on manufacturing processed for solar cells, uncertainty quantification for weather prediction and battery modeling.

For additional information, see Malik Hassanaly's LinkedIn profile

Disclaimer: Any opinions expressed on LinkedIn are the author’s own, made in the author's individual capacity, and do not necessarily reflect the views of NREL.

Research Interests

Design optimization

Data-assisted uncertainty quantification

Scientific machine learning

Multi-fidelity methods

Data reduction

Surface chemistry modeling

Anomaly detection/prevention

Education

Ph.D., Aerospace engineering, University of Michigan 
 
M.S., Aerospace engineering, University of Texas at Austin 
 
M.S., General Engineering, Ecole Centrale de Lille, France 

Professional Experience

Intern, R&D, Maia Eolis (2012–2013) 

Intern, Software Development, RTE (2012) 

Intern, Managing Solutions, Areva NP (2010)

Associations and Memberships

American Institute for Aeronautics and Astronautics 

Combustion Institute 

American Physical Society

Society for Industrial and Applied Mathematics 

American Institute of Chemical Engineers

Featured Work

Adversarial Sampling of Unknown and High-Dimensional Conditional Distributions, Journal of Computational Physics (2021)

Surface Chemistry Models for GaAs Epitaxial Growth and Hydride Cracking Using Reacting Flow Simulations, Journal of Applied Physics (2021)

Classification and Computation of Extreme Events in Turbulent Combustion, Progress in Energy and Combustion Science (2021)

Data-driven Analysis of Relight Variability of Jet Fuels Induced by Turbulence, Combustion and Flame (2020)

Lyapunov Spectrum of Forced Homogeneous Isotropic Turbulent Flows, Physics Review Fluids (2019)

A Self-Similarity Principle for the Computation of Rare Event Probability, Journal of Physics A: Mathematical and Theoretical (2019)

Ensemble-LES Analysis of Perturbation Response of Turbulent Partially-Premixed Flames, Proceedings of the Combustion Institute (2019)

Using Machine Learning to Construct Velocity Fields from OH-PLIF Images, Combustion Science and Technology (2019)

Large Eddy Simulation of Soot Formation in a Model Gas Turbine Combustor, Journal of Engineering for Gas Turbines and Power (2018)

A Minimally Dissipative Low-Mach Number Solver for Complex Reacting Flows in OpenFOAM, Computer and Fluids(2018) 

Awards and Honors

Richard and Eleanor Towner Prize for Distinguished Academic Achievement (2019)