Scholar
Malik Hassanaly
Google Scholar ID: hYX902wAAAAJ
National Renewable Energy Laboratory
Citations & Impact
Citations
808
H-index
18
i10-index
23
Publications
20
Co-authors
6
list available
Publications
Resume (English only)
Academic Achievements
- No specific academic achievements, such as publications or awards, are listed.
Research Experience
- Researcher at National Renewable Energy Laboratory (NREL), focusing on Scientific Machine Learning, Complex Fluid Flows and High-Performance Computing, Uncertainty Quantification, and Adversarial Robustness. Specific areas of work include:
- - Probabilistic data augmentation
- - Surrogate models and reduced-order models
- - Information extraction from large databases
- - Data reduction methods
- - Minimally dissipative methods
- - Turbulent combustion modeling
- - Chaotic dynamics of turbulence
- - Surface reaction modeling
- - Analytically reduced chemistry for HPC
- - Bayesian inference
- - Rare event probability estimation
- - Uncertainty propagation
- - Single-agent and multi-agent reinforcement learning
- - Anomaly detection
Education
- Ph.D. in Aerospace Engineering from the University of Michigan
Background
- Research interests include Scientific Machine Learning, simulation of complex fluid flows with High-Performance Computing, Uncertainty Quantification, and Adversarial Robustness. Focuses on improving the efficiency and reliability of wind turbines, batteries, deposition reactors, bio reactors, inverters, and efficient engines.
Miscellany
- No personal interests or other information provided.
