Behavioral Signal Processing methods provide the basis for modeling human behavior with high resolution. Computational methods developed for Behavioral Signal Processing applications can provide deeper understanding of behavioral phenomena, especially useful for neuropsychiatry. I am developing methods to support dynamic analysis of clinical interviews, online data collection, and neuropsychiatric batteries. These methods are enabling understanding of mechanisms which underlie several disorders including dementia, schizophrenia, trauma, and depression. My work features analysis on the Framingham Heart Study Cognitive Aging Cohort, DCAPS Distress Corpus, and Mental Health America.