Yibo Hu

I am a postdoctoral researcher in Computer Science at Georgia Institute of Technology, working with Professor Srijan Kumar. Prior to this, I earned my Ph.D. in Computer Science from the University of Texas at Dallas, under the guidance of Professor Latifur Khan.

I am broadly interested in data mining, artificial intelligence, natural language processing, computational social science, and uncertainty quantification. I am passionate about applying my knowledge to create efficient computational applications for real-world challenges and social good.

For example, I joined an interdisciplinary project on big data social science, which aims to extract usable data from massive texts to track, analyze and predict social events. My work utilized large-scale language models to alleviate the annotation bottleneck and developed state-of-the-art deep-learning models for social science research.

I am also interersted in uncertainty quantification to enhance the robustness of deep learning models in real-world scenarios with out-of-distribution examples and domain shifting. The goal is to "help models better know what they don't know."

My CV is here.

Yiqiao Jin, Mohit Chandra, Gaurav Verma, Yibo Hu, Munmun De Choudhury, Srijan Kumar. Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queries. In Proceedings of the ACM on Web Conference 2024 (WWW '24), 2024. [link]

Bing He, Yibo Hu, Yeon-Chang Lee, Soyoung Oh, Gaurav Verma, Srijan Kumar. A Survey on the Role of Crowds in Combating Online Misinformation: Annotators, Evaluators, and Creators. Preprint, 2023 [link]

Yibo Hu, Erick Skorupa Parolin, Latifur Khan, Patrick T. Brandt, Javier Osorio, Vito J. D'Orazio. Synthesizing Political Zero-Shot Relation Classification via Codebook Knowledge, NLI, and ChatGPT. Preprint, 2023. [link]

Sadaf Md Halim, Saquib Irtiza, Yibo Hu, Latifur Khan, and Bhavani Thuraisingham. WokeGPT: Improving Counterspeech Generation Against Online Hate Speech by Intelligently Augmenting Datasets Using a Novel Metric. In IEEE International Joint Conference on Neural Networks (IJCNN), 2023. [link]

Erick Skorupa Parolin, Yibo Hu, Latifur Khan, Javier Osorio, Patrick T. Brandt, and Vito D'Orazio. Confli-T5: An AutoPrompt Pipeline for Conflict Related Text Augmentation. In IEEE International Conference on Big Data (Big Data), 2022. [link]

Yibo Hu, Yu Lin, Erick Skorupa Parolin, Latifur Khan, and Kevin Hamlen. Controllable Fake Document Infilling for Cyber Deception. In Findings of the Association for Computational Linguistics: (EMNLP), 2022. [link]

Khandakar Ashrafi Akbar, Sadaf Md Halim, Yibo Hu, Anoop Singhal, Latifur Khan, and Bhavani Thuraisingham. Knowledge Mining in Cybersecurity: From Attack to Defense. In IFIP Annual Conference on Data and Applications Security and Privacy (DBSec), 2022. [link]

Zhuoyi Wang, Yibo Hu, Latifur Khan, Kevin Hamlen, and Bhavani Thuraisingham. CAPT: Contrastive Pre-Training based Semi-Supervised Open-Set Learning. In IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR), 2022. [link]

Erick Skorupa Parolin, MohammadSaleh Hosseini, Yibo Hu, Latifur Khan, Patrick T. Brandt, Javier Osorio, and Vito D'Orazio. Multi-CoPED: A Multilingual Multi-Task Approach for Coding Political Event Data on Conflict and Mediation Domain. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2022. [link]

Yibo Hu, MohammadSaleh Hosseini, Erick Skorupa Parolin, Javier Osorio, Latifur Khan, Patrick Brandt, and Vito D'Orazio. ConfliBERT: A Pre-trained Language Model for Political Conflict and Violence. In Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2022. [link]

Erick Skorupa Parolin, Yibo Hu, Latifur Khan, Javier Osorio, Patrick T. Brandt, and Vito D'Orazio. CoMe-KE: A New Transformers Based Approach for Knowledge Extraction in Conflict and Mediation Domain. In IEEE International Conference on Big Data (Big Data), 2021. [link]

Yibo Hu, and Latifur Khan. Uncertainty-aware reliable text classification. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD), 2021. [link]

Yibo Hu, Yuzhe Ou, Xujiang Zhao, Jin-Hee Cho, and Feng Chen. Multidimensional uncertainty-aware evidential neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2021. [link]

Instructor, Georgia Institute of Technology
CSE 8803 DSN: Data Science for Social Networks (Graduate), Fall 2023

Teaching Assistant, The University of Texas at Dallas
CS6375 Machine Learning (Graduate), Summer 2018, Spring 2019, Spring 2020
CS6364 Artificial Intelligence (Graduate), Spring 2018, Fall 2020
CS6350 Big Data Management and Analytics (Graduate), Spring 2020
CS4395 Human Language Technologies (Undergraduate), Fall 2019
CS4347 Database Systems (Undergraduate), Fall 2019
CS4375 Introduction to Machine Learning (Undergraduate), Fall 2018
CS6320 Natural Language Processing (Graduate), Fall 2018
CS3345 Data Structures and Algorithmic Analysis (Undergraduate), Spring 2018

Program committee or reviewers
North American Chapter of the Association for Computational Linguistics (NAACL) 2024
The International Joint Conference on Neural Networks (IJCNN) 2024
The Web Conference 2024
AAAI Conference on Artificial Intelligence (AAAI) 2024
The Annual Meeting of the Association for Computational Linguistics (ACL) 2023
International Joint Conferences on Artificial Intelligence (IJCAI) 2023 (DISTINGUISHED PC MEMBER AWARD)
SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2020, 2021, 2023
The International Conference on Database Systems for Advanced Applications (DASFAA) 2023
The Conference on Empirical Methods in Natural Language Processing (EMNLP) 2022, 2023
SIAM International Conference on Data Mining (SDM) 2022, 2024
The 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021
ACM Transactions on Internet Technology (TOIT) 2021