Research + Data Science

Currently, I lead a team of research and data scientists who specialize in content moderation, algorithmic amplification, and fairness in machine learning. Over time I’ve led research in many areas ranging from public health to employment equity. My research has crossed several disciplines, but the common thread has been focusing on whether technological and social changes come at the expense of marginalized communities.

For an overview of all past research projects, check out my resume.

Machine learning fairness

Since 2019 I’ve been specializing in the topic of fairness in machine learning and algorithmic systems. Highlights have included:

• Being the original AI Ethics expert at Indeed, where I launched a new initiative on how ML affects fairness in hiring

• Co-authoring “Challenges in Translating Research to Practice for Evaluating Fairness and Bias in Recommendation Systems”

• Being a 2-time fellow in AI Ethics & Governance at the Berkman Klein Center

Public policy research

In academia, I spent several years evaluating how public policies affect social inequity in nutrition and childhood obesity. This research was shared widely (see Communications) and influenced policy changes around the world. Accomplishments included:

• Multiple studies I led were cited in federal legislation to reform school nutrition standards

• Pioneering the first study to quantify the impact of state laws on adolescent weight change

• Speaking at a National Governors Conference in Mexico on how policy can improve public health 

Complex systems modeling

In 2012, I received a 5-year NIH award to study how complex systems modeling can be used to evaluate the impact of public policy on obesity and health disparities. I also developed multiple courses on Systems Thinking, including:

• Systems Science and Obesity (Johns Hopkins)

• Systems Thinking in Public Health (UT Health Science Center at Houston)