About Me

I am Jinghan Zhang, a Ph.D. candidate in Computer Science at Clemson University (expected 2027), advised by Dr. Kunpeng Liu in the CLAIM Lab.

My Research Interests

My research interests span Large Language Models (LLMs), Human-Inspired AI, Collaborative AI, Inclusive and Adaptive AI, and Data-Centric AI. I study how AI systems can learn generalizable principles from human intelligence, improve continually through collaborative experience, and adapt their knowledge, behavior, and accessibility across users, domains, and environments.

My Current Work

My current work is organized around three connected directions:

1. Human-Inspired AI: I develop methods that translate human principles—such as evidence-guided search, exploration, and evaluative judgment—into computational mechanisms for reliable complex reasoning and behavior optimization.
2. Collaborative AI: I study how AI systems can learn continuously from limited feedback, diverse interaction signals, and collaborative experience with people and other systems.
3. Inclusive and Adaptive AI: I build methods that acquire domain expertise, evaluate evidence and risk, and transfer existing AI capabilities across domains, users, and accessible representations.

Together, these directions support my broader vision of Inspired AI: systems that proactively discover useful knowledge, learn from experience, respond responsibly to new evidence, and adapt their capabilities for scientific discovery and broad access.

I welcome collaborations and interdisciplinary conversations that advance reliable, inclusive, and human-centered AI. Please feel free to contact me.