I'm interested in the societal implications of technology, especially machine learning. At MIT, I'm part of the Clinical and Applied Machine Learning Group, the Visualization Group, and the Data + Feminism Lab. My work aims to help make automated systems more understandable and easier to use responsibly. I've also worked on applications of machine learning in healthcare.


Workshops and Posters

Invited Talks and Panels

  • UCL-Toronto Ethical Innovation for AI workshop (July 2020). Understanding and Preventing Unintended Consequences of ML (talk and panel).
  • ACM Conference on Web Science (July 2020). Measuring the Interference of Machine Learning in Human Decision-Making (talk).
  • MIT Better World symposium in Atlanta, GA (October 2019). Trust Issues in Machine Learning (talk and panel).
  • Fair ML in Health at Data & Society Research Institute in New York, NY (October 2019). Deploying decision-aids: real-world considerations (talk).
  • Computational Cultures: Uncommon Knowledge at MIT Department of Philosophy (May 2019). Ethics across disciplines (lightning talk and panel).
  • Diversity and Inclusion Symposium by True Blue Inclusion in New York, NY (May 2019). Tackling Harm and Improving Accountability in the Automation of Talent Management (talk and panel).
  • Systems that Learn @ CSAIL Annual Meeting (August 2018). Bias in Machine Learning and Applications to Healthcare (talk).
  • Machine Learning for Healthcare Conference in Boston, MA (August 2017). Clinical Event Prediction and Understanding using Neural Networks (lightning talk).


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