Sarah M Brown

Assistant Professor of Computer Science

Computer Science & Statistics Department

University of Rhode Island

Kingston, RI

I am an Assistant Professor of Computer Science and director of the ML4STS Lab at the University of Rhode Island . Previously I was a Data Science Initiative Postdoc at Brown University affiliated to the Division of Applied Mathematics and hosted by Professor Bjorn Sandstede and I completed a Chancellor’s Postdoctoral Fellow in Computer Science at the University of California, Berkeley with faculty mentor Professor Mike Jordan. I completed a BS in Electrical Engineering with a minor in Biomedical Engineering in 2011 a MS in Electrical and Computer Engineering and a PhD in Electrical Engineering in 2016 advised by Jennifer Dy both at Northeastern University. My graduate studies were supported by a Draper Laboratory Fellowship and a National Science Foundation Graduate Research Fellowship. My other professional activities include teaching computational data analysis skills to researchers with The Carpentries and serving as the treasurer emeritus on the Women in Machine Learning, Inc Senior Advisory Council.


I am making AI more fair by enabling it to use subjective human expertise. I do this by modeling bias in the world, understanding the behavior of fair machine learning interventions, and building tools that directly allow domain experts to interact with data and learning systems. Through collaborations with social scientists who study societal bias, I am able to develop better models that can aid in detection of biases in datasets and prevent machines from replicating those biases. To understand where different types of interventions succeed and fail, I analyzed the behavior of fair machine learning algorithms. Finally, I collaboratively build tools that enable incorporating expertise and subjective assessments of data in the modeling process. Underlying all of these is a core technical approach of probabilistic model-based machine learning. I trained in developing probabilistic models for complex systems with multiple sources of uncertainty by collaborating with affective neuroscientists improving understanding of the spatio-temporal patterns of brain activity that create emotional experiences.


In my teaching, I aim to engage learners in a conversation about the material. I take care to practice the strategies for creating an inclusive computer science learning environment I learned in the Carpentries instructor Training.


In addition to standard research talks, I also speak on leadership topics though my service activities.


Outside of the lab, I am a passionate advocate for underrepresented STEM engagement at all levels. After a three year term as treasurer of Women In Machine Learning, Inc, I have moved to the senior advisory council. As a student, I held a variety of leadership positions in the National Society of Black Engineers at both the local and national levels including National Academic Excellence Chair.