Assistant Professor

Assistant Professor, School of Computing[Picture of [YourName]
Postdoc Scholar, Computational Science and Engineering, Georgia Institute of Technology
Ph.D., Computer Science, University of Texas at Dallas

Office: IIT Galvin Tower #15E3-3, 10 West 35th Street, Chicago, IL 60616, USA
(Google Map Location)
Office Hour: Thursday 2-4pm
Telephone: Click to show
Email: yhu89 [at] illinoistech [dot] edu
LinkedIn: View [Your Name]'s profile on LinkedIn

👉 Prospective Students Research openings are available for Illinois Tech master’s students and for select external or remote students. I do not have funded RA or TA positions, and PhD admissions for the current cycle are closed. See more details →

About

Dr. Yibo Hu is an Assistant Professor in the School of Computing at Illinois Institute of Technology. Before joining IIT, he was a Postdoctoral Scholar in the School of Computational Science and Engineering at the Georgia Institute of Technology.

He works on the safety and reliability of AI systems: when large language models and multi-agent LLM systems can be trusted, and how they fail. His research shows that confidence, agreement, and statistical guarantees stop being reliable safety signals once a model's decisions are shaped by social influence, distribution shift, and multi-agent interaction. He builds methods and benchmarks to measure and contain these failures, with applications in health, political measurement, and content moderation.

News

  • Recruiting: I recruit motivated students year round for publication-oriented research in trustworthy AI and multi-agent LLM reliability. [details] [group]
  • Jun 2026: Our recent work Easier to Mislead Than to Correct: Harmful and Beneficial Revision in LLM Conformity is now available as a preprint. [link]
  • May 2026: Students working with my group completed several manuscripts within their first semester of research involvement, including projects on health-related question answering, political event coding, and content moderation workflows. [recent manuscripts]

Research Areas

Reliability and the limits of a model’s self-knowledge

  • Calibration and selective prediction
  • Robustness under distribution and subpopulation shift
  • Self-knowledge and confidence estimation

Safety of multi-agent LLM systems

  • Social influence, conformity, and sycophancy
  • Monitoring and accountability in agent pipelines
  • Aggregation and coverage guarantees under adversarial agents

AI for public-interest and high-stakes domains

  • Political and social-event measurement
  • Health decision-making and content moderation

Selected Publications

Recent Manuscripts and Preprints

Social Influence and Collective Decision-Making

Alicia Guerra and Yibo Hu. The Evaluator Is Part of the Experiment: Measuring Open-Ended LLM Conformity. Preprint, 2026. [link]

Yibo Hu and Jiaming Qu. Social Pressure Breaks Majority Voting in LLM Safety Panels. Preprint, 2026. [link]

Yibo Hu and Jiaming Qu. Most LLM Conformity Needs No Speaker: Measuring the Speaker-Free Floor in Peer-Pressure Benchmarks. Preprint, 2026. [link]

Jiaming Qu, Lucheng Fu, and Yibo Hu. Easier to Mislead Than to Correct: Harmful and Beneficial Revision in LLM Conformity. Preprint, 2026. [link]

Monitoring and Security in Multi-Agent Systems

Yibo Hu. Silence Is Endorsement: Verification-Status Laundering in LLM Agent Pipelines. Preprint, 2026. [link]

Diego Fernandez Arias, Dev Prashant Mistry, Ren Wang, and Yibo Hu. Early Detection of Distributed Backdoors in Multi-Agent LLM Systems: A Characterization Study. Preprint, 2026. [link]

Yibo Hu and Ren Wang. When Local Monitors Miss Compositional Harm: Diagnosing Distributed Backdoors in Multi-Agent Systems. Preprint, 2026. [link]

Reliability under Uncertainty and Distribution Shift

Yibo Hu. One Axis, No Brake: Self-Knowledge Limits the Filtering of Harmful Peer Conformity in LLMs. Preprint, 2026. [link]

Yibo Hu. Safety-Flag: A Unified Benchmark for the Reliability and Calibration of LLM Content Moderators. Preprint, 2026. [link]

Hanyu Su, Carlota Julbe i Juanola, and Yibo Hu. Subtype Robustness Is Not Just Accuracy: Calibration Under Unseen Subtype Shift. Preprint, 2026. [link]

AI for Health and Public-Interest Domains

Zixian He, Bharath Raahul Murugesan, Patrick T. Brandt, and Yibo Hu. When Better Codebooks Are Not Enough: Predictive Performance and Behavioral Reliability in LLM Political Event Coding. Preprint, 2026. [link]

Suraj Babu Thimma Krishnaram, Yibo Hu, and Karthikeyan Saravanan. When Surface Form Changes Moderation Decisions: A Paired Study of Code-Mixed Workflow Instability. Preprint, 2026. [link]

Maroof Kousar and Yibo Hu. Can I Take Another Dose? Evaluating LLM Decision-Making Under Temporal Uncertainty in OTC Dosing. Preprint, 2026. [link]

Peer-Reviewed Publications

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. WWW 2024. [link]

Yibo Hu, Erick Skorupa Parolin, Latifur Khan, Patrick T. Brandt, Javier Osorio, Vito J. D'Orazio. Leveraging Codebook Knowledge with NLI and ChatGPT for Zero-Shot Political Relation Classification. ACL 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. ACM TKDD 19, no. 1 (2024): 1-30 [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. NAACL 2022. [link]

Yibo Hu, and Latifur Khan. Uncertainty-aware reliable text classification. KDD 2021. [link]

Yibo Hu, Yuzhe Ou, Xujiang Zhao, Jin-Hee Cho, and Feng Chen. Multidimensional Uncertainty-Aware Evidential Neural Networks. AAAI 2021. [link]

Group

I work with students on trustworthy AI, multi-agent LLM systems, and AI safety. Please see the Group page for current students and group activities, or the Prospective Students page for information about research opportunities.

Teaching

  • ITMD 526: Data Warehousing (Graduate), Illinois Tech, Fall 2026
  • ITMD 524: Applied Artificial Intelligence and Deep Learning (Graduate), Illinois Tech, Fall 2025, Spring 2026
  • ITMD 523: Advanced Topics in Data Management (Graduate), Illinois Tech, Spring 2026
  • CSE 8803 DSN: Data Science for Social Networks (Graduate), Georgia Tech, Fall 2023

Service

Editorial Roles

  • Co-Editor, Graph-Based Retrieval-Augmented Generation Systems, Frontiers in Big Data (2025-2026) [link]

Program Committee & Reviewing

  • ACL (2023-), EMNLP (2022-), NAACL (2024-), WWW (2024-)
  • IJCAI (Distinguished PC Member, 2023), KDD (2020-)
  • SDM (2022-), DASFAA (2023), IJCNN (2024), DSAA (2025)
  • Data4SoftSec 2026 (Workshop on Datasets for Software Security @ IEEE S&P)