Caleb Belth
[keɪləb bɛlθ] (he/him)
Welcome! I'm a PhD candidate at the University of Michigan.
My research interests fall in linguistics, computer science, and the study of mind. I primarily use computational models and experimental studies—in tandem—to study the algorithms and representations humans use to learn phonological alternations.
I also research how contributions to our understanding of human learners can improve natural language processing.
Prior 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 this work include anomaly detection, suspicious behavior discovery, and city/urban planning, including 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 am currently funded by the NSF GRF.
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.
Publications
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The Greedy and Recursive Search for Morphological Productivity
Caleb Belth, Sarah Payne, Deniz Beser, Jordan Kodner, Charles Yang
CogSci, 2021
[pdf] [code] -
A Hidden Challenge of Link Prediction: Which Pairs to Check?
Caleb Belth, Alican Büyükçakır, Danai Koutra
IEEE International Conference on Data Mining (ICDM), 2020 (acceptance rate: 9.8%)
Selected as one of the best papers at ICDM 2020. Invited for potential publication at the KAIS Journal, Springer.
[pdf] [code]
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Mining Persistent Activity in Continually Evolving Networks.
Caleb Belth, Xinyi (Carol) Zheng, Danai Koutra
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), August 2020 (acceptance rate 17%)
[pdf] [code] [video]
Also accepted for presentation at the 16th SIGKDD International Workshop on Mining and Learning with Graphs. -
What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization.
Caleb Belth, Xinyi (Carol) Zheng, Jilles Vreeken, Danai Koutra
ACM The Web Conference (WWW), April 2020 (oral presentation, acceptance rate 19%)
[pdf] [code] [video]
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Personalized Knowledge Graph Summarization: From the Cloud to Your Pocket
Tara Safavi, Caleb Belth, Lukas Faber, Davide Mottin, Emmanuel Muller, Danai Koutra
IEEE International Conference on Data Mining (ICDM), 2019 (acceptance rate: 9%)
[pdf] -
When to Remember Where You Came from: Node Representation Learning in Higher-order Networks
Caleb Belth, Fahad Kamran, Donna Tjandra, Danai Koutra
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2019 (acceptance rate: 15%)
[pdf] [slides]
Also accepted for presentation at the 15th SIGKDD International Workshop on Mining and Learning with Graphs.
