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I am a second-year PhD student in computer science at New York University,
where I am fortunate to be advised by Prof. Juliana Freire.
I also had the pleasure of being mentored by Dr. Flip Korn during my internship at Google Research,
and by Prof. Christopher Musco.
I am broadly interested in data-centric aspects of AI, especially scalable data integration and data explainability.
My research also involves representation learning for structured data.
Previously, I jointly pursued an Honors B.S. in Computer Science and an Honors B.A. in Mathematics at the University of Rochester in 2023,
where I am pleased to have worked with
Prof. Fatemeh Nargesian, Prof. Jiebo Luo,
and Prof. Daniel Štefankovič.
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"Perhaps you will not only have some appreciation of this culture;
it is even possible that you may want to join in the greatest adventure that the human mind has ever begun."
-- Richard Feynman. The Feynman Lectures on Physics (1964)
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Publications
(* indicates equal contribution)
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Kernel Banzhaf: A Fast and Robust Estimator for Banzhaf Values
Yurong Liu*, R. Teal Witter*, Flip Korn, Tarfah Alrashed, Dimitris Paparas, Juliana Freire
Preprint
[paper] [code]
"A novel linear regression-based estimator for Banzhaf values in interpretable machine learning."
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BDIViz: A Schema Matching Visualization Tool for Biomedical Domain Experts
Yifan Wu, Dishita Turakhia, Yurong Liu, Juliana Freire, Claudio Silva
Preprint
"A visualization tool that supports iterative exploration, direct manipulation, and value-centric analysis for biomedical schema-matching."
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Enhancing Biomedical Schema Matching with LLM-based Training Data Generation
Yurong Liu, Aécio Santos, Eduardo H. M. Pena, Roque Lopez, Eden Wu, Juliana Freire
TRL@NeurIPS, 2024
[paper]
"Schema matching with LLMs generating synthetic data for training column embeddings via contrastive learning."
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ArcheType: A Novel Framework for Open-Source Column Type Annotation using Large Language Models
Benjamin Feuer, Yurong Liu, Chinmay Hegde, Juliana Freire
VLDB, 2024 [paper] [code]
"A framework utilizing large language models for column type annotation, which supports fine-tuning and zero-shot learning settings."
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Sampling over Union of Joins
Yurong Liu*, Yunlong Xu*, Fatemah Nargesian
SIGMOD, 2023 (Companion) [paper]
"Random sampling over the set and disjoint union of joins, with sample uniformity and independence guarantees"
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