Caleb Belth

[keɪləb bɛlθ]

Welcome! I'm a PhD candidate at the University of Michigan, where my advisor is Danai Koutra.

I am interested in how learnability, computational efficiency, and information theoretic laws help children overcome sparse data to acquire language, and what that means for linguistic theory.

My prior research has been in a different sparse data setting: graph mining. 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.

On the side, I 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 cbelth@umich.edu.
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.