Total mentored projects: 8
Using neural ordinary differential equations to predict stochastic particle behavior will allow for prediction of the start and end behavior of cancer cells and tumors.
Testing how certain sections of an atomic structure bind differently will lead to more efficient structures for use in advanced electronics.
Developing a neural network model for predictive modeling of many-body interactions will help simulate collective dynamics of cancer cells.
Computational modeling of a specific atomic structure that is efficient at capturing CO2 from the air can help fight climate change.
Studying patterns in 2D materials can lead to discovering new useful material properties for technologies such as batteries.
Using machine learning to explore phase selection rules will help discover novel metal alloys with useful properties.