1. Safety and Transparency in Machine Learning

I am interested in developing fair and trustworthy machine learning methods. For instance, model multiplicity is the existence of multiple equivalent models for a prediction problem (also known as the “Rashomon” effect). I examine how predictions change across similar models and develop methods to measure this in different settings. Multiplicity offers transparency as well as flexibility. Broadly, I question how we formulate ML problems and offer insights on the social implications of those methods.

2. Race and Technology

Technology is changing the way we understand race and racism. I am interested in the dynamics of this shift and exploring anti-Black racism in socio-technical contexts. I pose questions at the intersection of social science and machine learning. For example, how have communities responded to and engaged with advances in tech? More recently, I am examining the role of algorithms in increased surveillance of Black and Brown communities.

3. Applied Data Science

Data collection, analysis and interpretation inform social policies more and more. I am interested in how these methodologies can be applied while being rooted in equity and justice. My work in this area has been community-centered. For instance, I led a team of data scientists to explore the disparate impact of COVID-19 on Black folks. In general, I support data literacy efforts, data storytelling and data science promoting social policy innovation.

Select Experiences

• Google. (Summer 2023)

  PhD Research Intern @ Core ML

• Microsoft Research. (Summer 2022)

  PhD Research Intern @ Fairness, Accountability, Transparency & Ethics (FATE)

• Data for Black Lives. (2020 – 2022)

  Director of Research

• Harvard College: Lowell House. (2019 - 2022)

  Chair of Equity, Diversity & Inclusion, In-residence Undergrad Advisor