Computer Science PhD Student
I am a 2nd year PhD student in Computer Science at Brown University in the Encrypted Systems Lab where I study bias, audits, and verifiability in machine learning under the advise of Prof. Seny Kamara. I also work closely with Prof. Sarah M. Brown at the University of Rhode Island. Previously, I was an undergrad at the University of Maryland in College Park, where I worked with Prof. John Dickerson.
My work mostly falls at the intersection of machine learning and theory. I am interested in questions which examine and critque data-driven technological solutionism: when does it make sense to use machine learning, and when can the technology do harm?
Lately my research has been focused on understanding the efficacy of machine learning in achieving fair and equitable outcomes for underrepresented and marginalized groups. With the use of Optimal Transport, I am working to develop better measurements of equity and fairness, as well as better algorithms for the detection of bias.
- Kweku Kwegyir-Aggrey, Rebecca Santorella, Sarah M. Brown. Everything is Relative: Understanding Optimal Fairness with Optimal Transport (arxiv)
- Kweku Kwegyir-Aggrey, Sarah M. Brown. Measuring Bias with Wasserstein Distance, Workshop on Dataset Curation and Security co-located at Neurips 2020. (PDF) (Poster)
- Featured in Prof. Charles Isbell's Keynote Address at Neuips 2020 (Dec. 2020)
- Joined the Encrypted Systems Lab at Brown University (Aug. 2020)
- Started my PhD in computer science at Brown University (Sep. 2019)
- Completed internship at Adobe Applied Research in San Francisco (Aug. 2019)
- I graduated from the University of Maryland in College Park. I studied math and computer science. (May 2019)