We use AI and UQ to accelerate scientific discoveries and improve decision-making.

Modeling the "Why" in
Business Operations

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
Affective Polarization

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.

ARO W911NF-22-1-0035

Deep Learning for
Surface Chemistry 

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.

NSF 2218841

From Causal Pairs to Causal Graphs

The paper proposes new methods for causal structure learning from observational data using probability distributions informed by cause-effect pair features. These methods are compared to traditional and state-of-the-art approaches on synthetic and real datasets, and are found to have similar or better performance while being computationally faster.

https://arxiv.org/pdf/2211.04312.pdf

Expert Knowledge in Causal Discovery

Comparative analyses show that knowledge that corrects mistakes in the model can lead to significant improvements and that constraints on active edges have a larger positive impact than inactive edges. However, the induced knowledge does not correct a higher number of incorrect edges than expected on average. 

https://arxiv.org/pdf/2301.01817.pdf

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. 

https://arxiv.org/pdf/1910.00623.pdf