Xin Qian
Ph.D. in Information Studies, University of Maryland
Email: xinq [at] terpmail [dot] umd [dot] edu
Hi! My name is Xin. As of summer 2025, I successfully defended my thesis Investigating Conversational Access to Historical Content, advised by Douglas W. Oard.
I interned ASAPP, inc. as a Machine Learning Research Intern, mentored by Ramya Ramakrishnan in summer 2022. I was also fortunate to intern at Adobe Research in 2019 and 2020, on ML techniques for visual analytics, mentored by Eunyee Koh, Ryan Rossi et al. Prior to candidacy, it was my pleasure contributing to computational sensemaking and creativity led by Joel Chan from HCIL.
Previously, I obtained a MS in Language Technologies from Carnegie Mellon University (2018) and a BEng in Software Engineering from Chu Kochen Honors College, Zhejiang University (2016).
Selected Papers
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Conversational Interaction with Historical Figures: What’s it good for?
Xin Qian, Douglas W. Oard, and Joel Chan.
iConference'22. [PDF] [Presentation]
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Learning to Recommend Visualizations from Data.
Xin Qian, Ryan Rossi, Fan Du, Sungchul Kim, Eunyee Koh, Sana Malik, Tak Yeon Lee, and Joel Chan.
KDD'21 (Research track). [PDF] [Presentation] [Corpus] [Arxiv, Sept 2020]
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Generating Accurate Caption Units For Figure Captioning.
Xin Qian, Eunyee Koh, Fan Du, Sungchul Kim, Joel Chan, Ryan Rossi, Sana Malik, and Tak Yeon Lee.
TheWebConf'21 (WWW). [PDF] [Presentation] [Corpus]
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Opening Up the Black Box of Scholarly Synthesis: Intermediate products, processes, and tools.
Xin Qian, Katrina Fenlon, Wayne Lutters, and Joel Chan.
ASIS&T'20. [Blogpost] [Presentation] [HCIL Symposium] -
A Formative Study on Designing Accurate and Natural Figure Captioning Systems.
Xin Qian, Eunyee Koh, Fan Du, Sungchul Kim, and Joel Chan.
CHI'20 (Late Breaking Works). [PDF] [Corpus]