Welcome to Xuan’s Homepage!

I am an Assistant Professor in the Computer Science Department at Virginia Tech (VT). I am also a faculty member of the Sanghani Center for Artificial Intelligence and Data Analytics at VT.

I received my Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign (UIUC) under the supervision of Dr. Jiawei Han. During my Ph.D. study, I also worked at IBM Research as a summer intern. I received my M.S. in Statistics and my M.S. in Biochemistry from UIUC. I received my B.S. in Biological Science from Tsinghua University, China.

Research Interests

My primary research interests are in the fields of Natural Language Processing (NLP) and Data Mining (DM). I am specifically interested in developing principled data-driven approaches with light human effort for effective and scalable model learning.

• Natural Language Processing (NLP):
  • Information extraction, knowledge graph construction, text classification, …
  • Weakly-supervised/few-shot/zero-shot learning
• Fact-checking and Trustworthy NLP:
  • Fact-checking for generative large language models
  • Trustworthy large language models: groundedness, confidence, explainability, …
• AI for Sciences; AI for Healthcare:
  • Large language models for sciences and healthcare
  • Brain large language models
  • Multi-omics large language models

News and Highlights

• (3/26/24) Check out our new survey paper on LLMs for diverse biomedical data! We explored three critical categories of biomedical data: 1) textual data (biomedical literature and health records), 2) biological sequences (DNA/RNA/protein sequences and multi-omics sequencing data), and 3) brain signals (time-series EEG data).
• (3/3/24) We are grateful to receive a new grant from the Virginia Tech Brain Tech One Health Initiative to support our research on open-vocabulary brain-to-text translation with large language models.
• (1/18/24) We are grateful to receive a new grant from the Children’s National Hospital + Virginia Tech Initiative to support our research on weakly-supervised clinical variable extraction with large language models.
• (10/10/23) We are grateful to receive a new grant from the Virginia Tech Institute for Critical Technology and Applied Science (ICTAS) to support our research on multi-omics encoding with LLMs for disease progression prediction.
• (10/7/23) Three papers are accepted by EMNLP 2023. Congratulations to all the authors!
• (10/2/23) We are excited to present our tutorial of “AI for Science in the Era of Large Language Models” at ACL 2024.
• (9/6/23) We are grateful to receive a new grant from the Amazon + VT Center for Efficient and Robust ML to support our research on fact-checking in LLMs.
• (6/7/23) We are grateful to receive a new grant from the Center for Health Behaviors Research at Fralin Biomedical Research Institute to support our research on AI-guided behavioral health modifier prediction for fetal growth disorder detection.
• (5/11/23) We are grateful to receive a new grant from the Commonwealth Cyber Initiative (CCI) to support our research on trustworthy multimodal machine learning in healthcare.
• (5/1/23) Two papers are accepted by ACL 2023. Congratulations to all the authors!

My Schedule

You can find my schedule here. The “Week” view will present you the details of slots.

Prospective Students

My lab at Virginia Tech has openings for PhD students and research internship opportunities for master/undergrad students. We are looking for students with a strong background in NLP, data mining, deep learning, or bioinformatics.

Please email me if you’re interested in working with me. In your email, please include the following items:

• Title as “Prospective Student: YourName - YourAffliation”
• Briefly introduce what research problem you are interested in.
• Briefly introduce yourself, including education background, research experiences, and programming skills.
• Briefly explain your motivations and expectations of working with me.
• Include a PDF version of your CV.

Due to the high volume of emails, I may not be able to respond to every one I receive. For prospective PhD/MS students interested in joining our group, please include my name as one of your interested faculty members in your graduate application. This will make it easier for me to locate your application during the review process. For undergraduates and MS students at VT, the best way to learn more about our research is to attend my classes, visit my office hours, or engage with my graduate students in DDS 362 and 364.