Yibo Hu
- Assistant Professor of Information Technology and Management
Yibo Hu's research focuses on developing efficient and trustworthy AI methods for socially important domains where predictions influence public perception, policy, or safety. His recent work includes NLP applications in global political events, misinformation and counterspeech, cybersecurity defense and deception. He also investigates the foundations of trustworthy AI, including fairness in large language models and robustness under distribution shifts.
Education
- Ph.D. , Computer Science, The University of Texas at Dallas
- M.S., Computer Science, The University of Texas at Dallas
- M.S., Electronics & Communication Engineering, Xiamen University
- B.E., Communication Engineering, Xiamen University
Research Interests
- Natural language processing and data mining
- Trustworthy and robust AI
- Computational social science
- Cybersecurity and cyber deception
Publications
NLP for Social and Political Applications
Yibo Hu, Erick Skorupa Parolin, Latifur Khan, Patrick T. Brandt, Javier Osorio, Vito J. D'Orazio; ACL 2024.
Yibo Hu, MohammadSaleh Hosseini, Erick Skorupa Parolin, Javier Osorio, Latifur Khan, Patrick Brandt, and Vito D'Orazio; NAACL 2022.
Bing He, Yibo Hu, Yeon-Chang Lee, Soyoung Oh, Gaurav Verma, Srijan Kumar; ACM TKDD 19, no. 1 (2024): 1-30.
Trustworthy & Robust AI
Yibo Hu, and Latifur Khan; KDD 2021.
Yibo Hu, Yuzhe Ou, Xujiang Zhao, Jin-Hee Cho, and Feng Chen; AAAI 2021.
"Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queries;" Yiqiao Jin, Mohit Chandra, Gaurav Verma, Yibo Hu, Munmun De Choudhury, Srijan Kumar; WWW 2024.
AI for Cybersecurity
Yibo Hu, Yu Lin, Erick Skorupa Parolin, Latifur Khan, and Kevin Hamlen; EMNLP 2022 (Findings).