About

I am a PhD student in the Department of Statistics & Data Science at Carnegie Mellon University. I work with Professor Edward Kennedy and Professor Alexandra Chouldechova on causal inference problems related to algorithmic fairness.

In summer 2019, I was a Data Scientist Intern at Google in Mountain View, where I developed causal inference methods to estimate ads lift/incrementality. In summer 2018, I was a Data Scientist Intern at Google in NYC, where I applied statistical machine learning to text classification. In summer 2017 I was a Data Science intern at Box, Inc. in Redwood City, where I built a model to identify new marketing and sales leads.

Before starting my PhD, I worked as a (Senior) Research Specialist at the Center for Advanced Study of Language at the University of Maryland. I worked on research in psycholinguistics, bilingualism, and second language acquisition.

Outside of grad school, I enjoy hiking, running, swimming, music, and learning languages.

• 03/2021 Paper presentation at the 2021 conference on Fairness, Accountability, and Transparency (FAcct) (virtual).
• 12/2020 Paper presentation at NeurIPS workshop Algorithmic Fairness through the Lens of Causality and Interpretability (virtual).
• 08/2020 Paper presentation at the Joint Statistical Meetings (virtual).
• 04/2020 Presentation at the CMU Symposium on AI and Social Good in Pittsburgh, PA.
• 01/2020 Paper presentation at the ACM Conference on Fairness, Accountability, and Transparency in Barcelona, Spain.
• 12/2019 Paper/poster presentation at NeurIPS workshop ‘"Do the right thing": machine learning and causal inference for improved decision making’ in Vancouver, CA.
• 12/2019 Program Committee/Reviewer for the ML for the Developing World (ML4D) workshop at NeurIPS in Vancouver, CA.
• 05/2019—08/2019: Data Scientist internship at Google in Mountain View, CA.
• 05/2019 Poster presentation at the Atlantic Causal Inference Conference in Montreal, CA.
• 04/2019 Paper presentation at SIGBOVIK in Pittsburgh, PA.
• 01/2019: Poster presentation at the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES) in Honolulu, HA.
• 07/2018: Paper presentation at the Doctoral Consortium at the Educational Data Mining conference in Buffalo, NY.
• 06/2018—08/2018: Data Scientist internship at Google in New York City.
• 06/2018: Poster presentation at the Classification Society Meeting in Stony Brook NY.
• 05/2018—08/2018: Data Science internship at Box in Redwood City, CA.
• 05/2018: Poster presentation at the Atlantic Causal Inference Conference in Pittsburgh, PA.
• 11/2017: National finalist at the Citadel Data Open in New York, NY.
• 09/16/2017: Winner of the Citadel Data Open datathon at Carnegie Mellon, with fellow students Nic Dalmasso, Kwangho Kim, and Chirag Nagpal. (550+ student applications, around 125 students selected to compete)

• 2019 Travel grant, Conference on Neural Information Processing Systems (NeurIPS).
• 2019 Travel grant, AAAI/ACM Conference on Arti
  cial Intelligence, Ethics, and Society (AIES).
• 2018 Best poster award: Optimized Random Partition Tree-based Kernels. Convex Optimization Mini-Conference, Carnegie Mellon, Pittsburgh, PA. Co-authors: Benjamin LeRoy, Niccolò Dalmasso.
• 2017 Winner of the Fall 2017 Citadel Data Open at Carnegie Mellon. (550 student applications, 125 selected to compete; $20,000 prize). Teammates: Niccolò Dalmasso, Kwangho Kim, Chirag Nagpal.
• 2017 Best short talk: "Subspace Clustering," Statistical Machine Learning Mini-Conference, Carnegie Mellon, Pittsburgh, PA.