Queenie Luo

Ph.D. Candidate in Religion
Unofficial EALC logo: a line drawing of a Fu lion in profile, facing left, against a background of deep crimson
queenieluo@g.harvard.edu
Website

Queenie Luo graduated summa cum laude from Columbia University with a B.A. in Religion and Physics in 2018, completed her M.T.S in Buddhist Studies at Harvard Divinity School in 2020, and her M.S degree in Data Science at Harvard John A. Paulson School of Engineering and Applied Sciences in 2023. (Visit Queenie’s scholarly website for more information.)

Queenie’s research explores the making of early Chinese states during the Spring and Autumn and Warring States periods. Bridging ancient history and cutting-edge AI, she develops agentic AI tools to uncover patterns of state-building from fragmented historical evidence, including oracle bone scripts, bronze inscriptions, bamboo strips, tomb materials, ancient sites, landscape features, and transmitted texts. She also collaborates with faculty in computer science, statistics, and the social sciences to advance research on large language models, with a focus on fairness, ethics, and their broader societal implications.

Before coming to Columbia, Queenie spent three years studying Fashion Design at Parsons School of Design in New York. In 2018, Queenie received the Phi Beta Kappa Prize, which is awarded to the candidate who best represents the ideals of the society—intellectual integrity, tolerance for other views, and a broad range of academic interests. Queenie led the Norbu Ketaka project to clean 1,000,000 pages of Classical Tibetan manuscripts using NLP and Computer Vision methods, and donated the database to the Buddhist Database Resource Center (BDRC) upon completion. Queenie also works at the Institute for Quantitative Social Science (IQSS) on the China Biographical Database project (CBDB), where she developed the Lepton, Kraft, and TITO models, which have successfully assisted CBDB in expanding its database repository by employing NLP techniques.

Articles:

Queenie Luo, Michael J. Puett, and Michael D. Smith. 2024. A Perspectival Mirror of the Elephant. Communications of the ACM 67, 8 (August 2024).

Queenie Luo and Michael Puett. 2024. Anglo-American bias could make generative AI an invisible intellectual cage. Nature 629 (8014).

Queenie Luo and Yung-Sung Chuang. 2024. Cleansing Jewel: A Neural Spelling Correction Model Built On Google OCR-ed Tibetan Manuscripts. ACM Transactions on Asian and Low-Resource Language Information Processing 23, 5, Article 73 (May 2024).