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.
Recent News
Our work on Private Linear Regression received an ICML Spotlight.
I am co-organizing a workshop at NeurIPS this year on child safety.
"Evaluation without Generation" received a Spotlight at the AI4GOOD Workshop and was highlighted in MIT News.
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.