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My parents still live in a renovated version of the same house where they have lived my entire life.
The legacy of our mothers, fathers, and families is in every word that we speak and on every page that we write.
I am a research assistant professor at the Department of Computer and Information Science at the University of Pennsylvania. I am broadly interested in machine learning and programming languages research.
Thirteen years of prep school left me with a mastery of different language varieties than were found in and around the places where I grew up.
At 18, I left Virginia to attend Harvard College, and after about a year or so I picked up some of the features of a New England and more standardized Northeastern ways of speaking.
My maternal grandmother was at the top of her class at the Virginia Randolph School, but because segregation and cost limited her educational options, she was not able to go to college.
Despite these limited options, she made the best of what she had, even practicing with me the French vocabulary that she still remembered from her high school days.
But more importantly she gave me the gift of music.
She could play anything she heard on the piano, and thus I was gifted with music as another way to communicate.
Machine learning is increasingly used in real world systems where failures can be catastrophic, such as autonomous vehicles, medical diagnosis, and legal decision making. Sample Complexity of Estimating the Policy Gradient for Nearly Deterministic Dynamical Systems.
For such applications, there are many desirable correctness properties that machine learning models should satisfy, including safety, robustness, fairness, causality, and interpretability.