My name is Caleb Ellington and I'm a first-year Ph.D. student at Carnegie Mellon University advised by Eric P. Xing. I develop machine learning methods that are both accountable and human-interpretable for patient-specific precision healthcare.
Pattern recognition and generalization are at the heart of machine learning. However, one-size-fits-all medicine does not acknowledge the individual needs of underrepresented or undersampled demographics. I aim to create dynamic computational agents that make patient-specific decisions with explainable thought processes. In healthcare, the interpretability of an agent's reasoning is crucial for clinicians to either trust or disregard a machine's diagnosis.