We accelerate discoveries using AI and UQ.
Modeling the "Why" in
Businesses generate significant amounts of data, which is used in conjunction with modeling to develop actions to improve some aspect of the business operations. The goal is to develop an AI engine to guide managers with actionable recommendations to generate a desired effect on the 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 closing the feedback loop between robust data-driven approaches to discover causal relations and expert domain knowledge to overcome the effect of data limitations.
Aflatoxin Prediction in
US Corn Fields
US Corn Fields
The goal of this project is to develop a general predictive modeling framework for calculating mycotoxin incidence in US crop fields. Prediction and control of the most potent carcinogenic mycotoxin, aflatoxin, is a fundamental challenge for US grain industry, poultry producers, and makers of dairy products.
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.