Welcome! I'm a PhD candidate at the University of Michigan, where
I work with Danai Koutra.
Some of my interests are data mining, unsupervised learning theory, information theory, and philosophy.
My research focuses on developing graph mining methods that are able to operate on insufficient data by drawing on ideas from information theory and linguistics, such as entropy and insights from language learning. Current directions include exploiting sparsity to learn in settings with only positive examples. My research has contributed methods for 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 am grateful to have been selected for 2020-2023 NSF GRF and NDSEG fellowships. I will be funded by the NSF GRF starting Fall 2020.
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 firstname.lastname@example.org.
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.