Welcome! I'm a PhD candidate at the University of Michigan, where
I work with Danai Koutra.
I am interested in how clever tricks that children use in language acquisition can be used for learning with sparse data, and how data mining and learning theory principles might be used by children in language acquisition. I also often use information theory in my work. My research has contributed methods for choosing unlinked pairs of nodes to investigate further with a link prediction method or experimental study, identifying subtle patterns in networks that are too infrequent to be discovered by frequency alone, and for discovering errors and missing information in incomplete knowledge graphs. Applications of my work include anomaly detection, suspicious behavior discovery, and city/urban planning, including current projects with the City of Detroit on transportation planning.
I also investigate the possible effects of data mining objective functions on user well-being.
I am grateful to have been selected for 2020-2023 NSF GRF and NDSEG fellowships. I am currently funded by the NSF GRF.
I try my best to be responsible with my research, and continually consider whether my work will be constructive or destructive to society.
Prior to Michigan, I received a B.Sc. in Computer Science from Purdue University, where I was fortunate to work with Jennifer Neville, Dan Goldwasser, and Daisuke Kihara.
Feel free to contact me at email@example.com.
In case you are wondering, the name of this website ("quickshift") is a nod to my life-long love of road trips, driving, and cars in general.