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 scalable graph methods for operating under missing or incomplete data. To do so, I draw on ideas from information theory and linguistics, such as entropy and insights from language learning. My research has contributed methods for evolving networks, which, missing the future, only contain the network’s past evolution, and knowledge graphs, which are missing facts about the world. 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 honored to have been selected for 2020-2023 NSF GRFP and NDSEG fellowships.
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.