We use AI and UQ to accelerate scientific discoveries and improve decision-making.
Modeling the "Why" in
Businesses collect vast amounts of data, which they leverage with advanced modeling techniques to develop actions to enhance their operations. The goal is to create an AI engine that provides managers with actionable recommendations to produce a desired effect on key performance indicators.
Lowe's Innovation Fund
Causal Modeling of
The objective of this project is the development of an interactive causal modeling framework to understand affective polarization. The focus is on bridging the gap between data-driven methods for discovering causal relations and expert domain knowledge to mitigate the impact of data limitations.
Deep Learning for
In this project, we are using computational catalysis and deep learning to improve the analysis and understanding of surface chemistry. We will use large datasets of DFT energies and advanced neural networks to identify invariant material and molecular descriptors that can help us predict the behavior of chemical systems.
Nonlinear Bayesian Filter in High Dimensions
A general and fast computational framework is developed for nonlinear filtering in high dimensions. This is achieved by developing a robust probabilistic model to approximate the Bayesian inference problem and obtain samples from high dimensional posterior distributions.
Machine Learning in Catalysis
Machine learning models can help to reduce the large computational cost involved in computing various adsorption and transition-state energies of all possible surface states on a large number of catalyst models, and overcome the shortcomings of linear scaling relations for more complex chemistries.
UQ in Deep Learning
Exact Bayesian neural network methods are intractable and non-applicable for real-world applications. We propose an approximate estimation of the weights uncertainty using Ensemble Kalman Filter, which is easily scalable to a large number of weights.