Research

... at the intersection of machine learning, computer vision, numerical optimization, healthcare, physical science, and engineering. Our current focus is to build the theoretical and computational foundations for machine/deep learning and apply machine/deep learning to tackle challenging scientific, engineering, and medical problems.  

Trustworthy, Safe, and Deployable AI 

Modern machine/deep-learning (MDL) tools are highly performant on standard datasets, but break down quickly when being applied to real-world data, i.e., lacking robustness, and fall short of explainability (why it works), interpretability (whether it works understandably and plausibly), fairness (sub-populations are treated fairly), and many other social metrics. Our current research focuses on robustness, fairness, and safety (so that AI doesn’t cause liabilities), major roadblocks to deploying deep learning tools in high-stakes environments such as autonomous vehicles and healthcare. A few highlights of our work: 

Computation for AI

When machine/deep-learning (MDL) tools are applied to tackle science, engineering, and medical problems, domain knowledge such as physical laws, design targets, and medical conditions often maps to constraints in the resulting problem formulations. Standard practice to date turns these constraints into “soft” regularization that can easily lead to infeasible solutions. We aim to develop principled computing methods and software frameworks to solve deep learning problems with explicit constraints.  A few highlights of our work: 

AI for Science and Engineering 

People have mostly used machine/deep learning (MDL) to incrementally improve the solutions to many problems, but we firmly believe the true power of MDL lies in solving grand open problems in the hardest regimes. Our current focus is tackling difficult scientific inverse and design problems, collaborating with people from material science, civil engineering, and beyond.  A few highlights of our work: 

AI for Healthcare 

General, trustworthy AI is a grand and remote goal. Healthcare is a field that is relatively narrow and well-controlled (e.g., chest x-rays and CTs are taken in lab environments that are far less complicated than those countered by autonomous vehicles), and hence we anticipate that modern AI is likely to produce concrete impacts on healthcare in reasonably near terms. We are actively collaborating with multiple research groups of the medical school and M Health Fairview to modernize healthcare using modern AI. A few highlights of our work: