Peilin Yu
CIT 543
115 Waterman St
Providence, RI 02905
I am a Ph.D. Candidate in Computer Science advised by Prof. Stephen Bach.
I specialize in addressing weak supervision challenges through innovative methods that navigate limited, indirect, and/or noisy forms of supervision while minimizing human effort. Additionally, I am deeply interested in exploring the advantages of harnessing cutting-edge foundation models (GPTs, Llamas, CLIP etc.) to elevate the efficacy of weak supervision workflows. I also intern at Microsoft Azure AI, where I research novel techniques to enhance multi-modal foundation models for generating more accurate radiology reports.
I am a proud 👐🏟🧀 Badger and I got my BS in CS and Math there in 2019.
Email : [first_name]_[last_name] [at] brown [dot] edu
news
Feb 27, 2023 | We released Alfred, a toolkit for integrating Large Pretrained Models into Weak Supervision |
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selected publications
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NeurIPS-ENLSPStructure Discovery in Prompted Weak SupervisionIn The 3rd NeurIPS Workshop on Efficient Natural Language and Speech Processing 2023
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IEEE Big DataLeveraging Large Language Models for Structure Learning in Prompted Weak SupervisionIn IEEE International Conference on Big Data 2023