Ph.D. Candidate
Current
Philadelphia, Pennsylvania, United States
- Ph.D. candidate in the department of Mechanical Engineering and Applied Mechanics. Research focuses primarily on using machine learning in molecular dynamics methodologies to improve open boundary simulations and to.
- Developing complete fluids-based molecular dynamics simulation projects, including ideation and formation of methodology and mathematical basis, as well as, additional code sets, simulation runs, and data processing
- Implementing machine learning code into C++ based open boundary molecular dynamics simulations software (LAMMPS) to produce computationally efficient simulations with reducedruntime (up to ~100x improvement when tested.
- Constructing a supervised learning method using neural networks to predict atomistic forces and particle fluxes using Python and PyTorch
- Gathering data by observing nanoscopic droplet water growth of aerosols in atmospheric conditions using atomistic simulations
- Utilizing server-based and supercomputer-based operations to submit and manage 1000s of simulations and postprocess results