My research interests are in developing the principles and practice of trustworthy machine learning. Some recent highlights include (i) scalable, distributed, and robust machine learning, and (ii) metric elicitation; selecting more effective machine learning metrics via human interaction, primarily applied to ML fairness. Our applied research includes applications to cognitive neuroimaging, healthcare, and biomedical imaging. Some recent highlights include (i) generative models for X-rays and fMRI, and (ii) risk-scoring and prediction models.

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