I am a fifth-year PhD candidate in the Department of Statistics
and the Machine Learning Department at Carnegie Mellon University,
advised by Ryan Tibshirani. My general interests are in computational
statistics, particularly for sequential decision-making and online forecasting.
As part of CMU’s Delphi Research group, I am researching nowcasting algorithms
using digital surveillance data. We are currently focusing our efforts
to develop forecasting and nowcasting models for COVID-19. During my PhD,
I also studied machine learning approaches in motion planning for
self-driving cars at Argo AI.
2021: Interned at YouTube as a Data Scientist working on estimating causal impact of video classifiers.
2019-2020: Returned to Argo AI as a research intern exploring preference-based trajectory selection.
2018: Worked as a motion planning intern for Argo AI, developing machine learning features for self-driving cars.
2015–2017: Researched at Laber Labs under Eric Laber, building educational games demonstrating reinforcement learning algorithms. You can read about it in AMSTAT or play some of the games.
2016: Worked on constructing prediction regions for multiple-objective Markov decision processes with Daniel Lizotte at University of Western Ontario [1].
2016: Interned at Duke-National University of Singapore Medical School supervised by Bibhas Chakraborty working on dynamic treatment regimes.
2014–2017 Prototyped anomaly detection methods for high-frequency data at SAS Institute [2, 3].