Research
Dr. Ashia Wilson runs the Optimization, Safety, and Evaluation (OSE) Lab. The lab develops foundations for machine learning systems that are efficient, safe, and meaningfully evaluated. Our work spans optimization and sampling methods for efficient and reliable learning at scale; AI safety, with a focus on image-based abuse, child safety, and epistemic harms such as homogenization and sycophancy; and evaluation methods that support valid, decision-relevant conclusions about AI systems and their societal impacts.
Optimization
Mathematical principles for making computation more efficient, and for preserving utility when learning must satisfy human-centered requirements.
Publications →Safety
Detecting and controlling harmful model behavior, and understanding how AI systems shape agency, knowledge, and opportunity.
Publications →Evaluation
Turning latent constructs into valid measurements, and aligning evaluation with the decisions and consequences that matter.
Publications →Recent News
"Evaluation without Generation" received a Spotlight at the AI4GOOD Workshop and was featured in Bloomberg News and MIT News.
Our work on Private Linear Regression received an ICML Spotlight.
I am co-organizing a workshop at NeurIPS this year on child safety.
I co-organized a tutorial at ICML on unlearning.
Our work studying Sycophancy received an honorable mention at this year's CHI Conference and was highlighted in MIT News.
I was named Junior Bose Award winner.
Multiple recent papers accepted to ICML, AISTATS, CHI, FAccT, COLT, and TMLR.