Ashia Wilson

Ashia Wilson is an Assistant Professor at the Massachusetts Institute of Technology (MIT) in the Department of Electrical Engineering and Computer Science (EECS). She is affiliated with the MIT Laboratory for Information and Decision Systems (LIDS) and the MIT Statistics and Data Science Center (SDSC). Wilson earned her Ph.D. in Statistics from UC Berkeley (2018) and holds a B.A. in Applied Mathematics with a minor in Philosophy from Harvard University.

• Ph.D. in Statistics, UC Berkeley (2018)
• B.A. in Applied Mathematics, Harvard University

Her research focuses on the intersection of machine learning, statistics, and algorithmic fairness, with emphasis on optimization algorithms, differential privacy, and ethical AI. She investigates how machine learning systems can be designed to align with clinical priorities, ensure equitable resource allocation, and maintain robust data unlearning mechanisms.

Wilson's recent publications demonstrate trends in adaptive optimization, privacy-preserving algorithms, and fairness-aware machine learning. She explores challenges in clinical evaluation frameworks, adversarial relearning, and the ethical implications of algorithmic decision-making systems.

• ACM Conference on Fairness, Accountability and Transparency (FAccT) Best Paper Award (2024)

Wilson teaches graduate-level courses in data science, machine learning, and AI ethics at MIT, including 6.036 - Introduction to Machine Learning and 6.3950 - AI, Decision Making and Society. Her work bridges theoretical advances with practical applications in healthcare, judicial systems, and algorithmic economics.