I study core challenges in making machine learning responsible and trustworthy enough for deployment in real applications, especially healthcare. I focus on time series and NLP where we need:

• Robust methods for learning from data and labels that are are biased, missing, or noisy

• Models that generalize and adapt to ever-shifting distributions and requirements

• Tools to make models meet strong human requirements like early warnings, explainability, updatability, and safety/fairness.