Audience: Undergraduate Students

As multimodal artificial intelligence systems become increasingly embedded in everyday technology, there is a growing need to design human-centered AI agents that support and amplify human capabilities rather than replace them. This dissertation investigates how to build human-centered multimodal AI agents, framing human-centeredness as an agent-level objective that requires both accessible, assistive interaction and reliable,…

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and a leading cause of dementia. Neuroimaging and clinical biomarkers can reveal early disease changes, but building reliable machine learning models is difficult because data come from different scanners and sites, some modalities are missing, labeled cohorts are limited, and factors such as age and scanner/site effects…

Machine unlearning addresses the “right to be forgotten” under GDPR and enables privacy, copyright, and safety compliance in large language models. Training-based unlearning can remove targeted behavior on benchmarks, but it scales poorly, can degrade utility, and can fail under adversarial prompting that recovers supposedly forgotten content. This prospectus proposes inference-time behavioral unlearning: rather than…