Dr. Amanda Coston will join the UC Berkeley Department of Statistics as an Assistant Professor in the fall of 2024. Previously, she was a postdoctoral researcher at Microsoft Research on the Machine Learning and Statistics Team. 

Coston earned her Ph.D. in Machine Learning and Public Policy from Carnegie Mellon University, where her research focused on making algorithmic decision-making more reliable and equitable using causal inference and machine learning techniques. Her research has applications in child welfare, consumer credit lending, criminal justice, and health policy. Alexandra Chouldechova and Edward H. Kennedy advised her, and her thesis was titled "Principled machine learning for societally consequential decision making." Coston graduated magna cum laude with her B.S.E in Computer Science from Princeton, where Robert Schapire advised her.

"We are thrilled to welcome Amanda to UC Berkeley and have her join our community," said Chair Haiyan Huang.

Coston's research addresses significant issues in the validity, reliability, and fairness of data-driven algorithms, particularly in high-stakes societal applications such as healthcare, criminal justice, and consumer lending. 

"I am passionate about using data science to solve some of society's most pressing problems," said Coston. "It's a really exciting opportunity to do this at Berkeley, where we have world-class researchers, and I am looking forward to collaborating with faculty and students."

Her contributions have been recognized through several prestigious fellowships and awards, including the Meta Research Ph.D. Fellowship, NSF Graduate Research Fellowship, and the K&L Gates Presidential Fellowship in Ethics and Computational Technologies. Her research has also been featured in prominent publications like The Wall Street Journal and VentureBeat​. In 2024, she was named a Schmidt Sciences AI2050 Early Career Fellow.

-Alex Coughlin