My research focuses on trustworthy and reliable artificial intelligence, with particular interests in multimodal machine learning, uncertainty quantification, fairness, generative AI, and AI for healthcare. I develop AI methods that can reliably learn and make decisions under uncertainty, incomplete information, and heterogeneous real-world data — applied to medical imaging, longitudinal health data, and Alzheimer's disease and related dementias (AD/ADRD).
Fairness auditing and uncertainty quantification, so that models know when they don't know and treat patient groups equitably.
Learning from images, text, and structured data together, and generative models that create useful, diverse, and evaluable outputs.
Medical imaging, longitudinal health data, and Alzheimer's disease and related dementias (AD/ADRD).