Yongkang Du

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duyongka[at]gmail[dot]com

Yongkang Du is a third-year PhD student at Penn State University, College of IST, advised by Prof. Lu Lin. He received his M.S. in computer science from University of Southern California advised by Prof. Jieyu Zhao and B.E. in computer science and technology from North China University of Technology. His cv is available here.

His research is driven by building capable and reliable AI systems. Specifically, two fundamental questions:

  • How can we bridge the gap between human and AI reasoning? Investigating advanced cognitive capabilities in multimodal and agentic systems, including compositional logic, analogical reasoning, and long-horizon memory.
  • How can we ensure the trustworthiness of AI in the real world? Auditing and optimizing models for fairness, alignment, and safety, particularly in the context of decision-making.

Open to collaboration and internship opportunities!

News

Aug 2026 One papers accepted to the EMNLP 2026! From Narrative to Auditable Forecasts: A Structured Scaffold for Agentic Forecasting
Jul 2026 One paper accepted to the COLM 2026! CARV: A Diagnostic Benchmark for Compositional Analogical Reasoning in Multimodal LLMs
Jun 2026 Two papers accepted to the ICML Workshop on Forecasting! From Narrative to Auditable Forecasts: A Structured Scaffold for Agentic Forecasting and ForecastCompass: Guiding Agentic Forecasting with Adaptive Factor Memory (🌟 Spotlight)
May 2026 One paper accepted to the ACL TrustNLP workshop! Controllable Pareto Trade-off between Fairness and Accuracy

Selected publications

  1. COLM’26
    CARV: A Diagnostic Benchmark for Compositional Analogical Reasoning in Multimodal LLMs
    Yongkang Du, Xiaohan Zou, Minhao Cheng, and Lu Lin
    The Third Annual Conference on Language Modeling, 2026
  2. EMNLP’26
    From Narrative to Auditable Forecasts: A Structured Scaffold for Agentic Forecasting
    Yuanpu Cao, Yongkang Du, Yurui Chang, Lu Lin, and Jinghui Chen
    The 2026 Conference on Empirical Methods in Natural Language Processing, 2026
  3. ICML’26
    ForecastCompass: Guiding Agentic Forecasting with Adaptive Factor Memory
    Yurui Chang, Yongkang Du, Yuanpu Cao, Jinghui Chen, and Lu Lin
    ICML Workshop on Forecasting, 2026
    Spotlight
  4. KDD’26
    FairCoder: Probing LLM Bias in High-Stakes Decision Making via Coding Tasks
    Yongkang Du, Jen-tse Huang, Jieyu Zhao, and Lu Lin
    KDD SeT-LLM Workshop, 2026
  5. ACL’26
    Controllable Pareto Trade-off between Fairness and Accuracy
    Yongkang Du, Jieyu Zhao, Yijun Yang, and Tianyi Zhou
    ACL TrustNLP Workshop, 2026
  6. EMNLP
    Self-Contradictory Reasoning Evaluation and Detection
    Ziyi Liu, Isabelle Lee, Yongkang Du, Soumya Sanyal, and Jieyu Zhao
    The 2024 Conference on Empirical Methods in Natural Language Processing, 2024