Areas: Machine Learning, Algorithmic Fairness / Accountability / Transparency, Racial Justice
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.
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.
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.
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
Computer Science
Mysterious Projections: Multimodal LLMs Gain Domain-Specific Visual Capabilities Without Richer Cross-Modal Projections
Gaurav Verma, Minje Choi, Kartik Sharma, Jamelle Watson-Daniels, Sejoon Oh, Srijan Kumar
(ACL 2024)
Predictive Churn with the Set of Good Models
Jamelle Watson-Daniels, Flavio du Pin Calmon, Alexander D'Amour, Carol Long, David C. Parkes, Berk Ustun
(AFT Workshop @ NeurIPS 2023)
Algorithmic Fairness and Color-blind Racism: Navigating the Intersection
Jamelle Watson-Daniels
(in progress)
Jamelle Watson-Daniels, Solon Barocas, Jake M. Hofman, Alexandra Chouldechova
FAccT 2023 - ACM Conference on Fairness, Accountability, and Transparency [oral presentation]
Predictive Multiplicity in Probabilistic Classification
Jamelle Watson-Daniels, David C. Parkes, Berk Ustun
AAAI Conference on Artificial Intelligence, 2023 [Selected for oral presentation]
An Analysis of Emotions and the Prominence of Positivity in #BlackLivesMatter Tweets
Anjalie Field, Chan Young Park, Antonio Theophilo, Jamelle Watson-Daniels, Yulia Tsvetkov
Proceedings of the National Academy of Sciences of the United States of America, 119(35) 2022
Physics
Magnetic Drilling Enhances Intra-nasal Transport of Particles into Rodent Brain 2019.
Test Beam Demonstration of Silicon Microstrip Modules with Transverse Momentum Discrimination 2018.
Characterisation of Irradiated Thin Silicon Sensors for the CMS Phase II Pixel Upgrade 2017.
P-Type Silicon Strip Sensors for the New CMS Tracker at HL-LHC 2017.
Mechanical Stability of the CMS Strip Tracker Measured with a Laser Alignment System 2017.
NSF Graduate Research Fellowship (2020)
Ford Foundation Predoctoral Fellowship (2019)
Brown University Commencement Orator (May 2016)
Joslin Award for student leadership (May 2016)
Mildred Widgoff Award (for excellence in physics thesis preparation May 2016)