Chao Zhang

Chao Zhang is an Assistant Professor at the School of Computational Science and Engineering within the College of Computing at Georgia Institute of Technology. He holds affiliations with the Institute for Data Engineering and Science (IDEaS) and the Machine Learning Center (ML@GT). His research focuses on making it easier to build domain-customized foundation models and AI agents for task-solving and decision-making, with technical emphasis on data efficiency, computation efficiency, and model robustness.

Dr. Zhang's research interests span multiple cutting-edge areas of AI, with particular focus on four main themes: Data-Centric LLMs for adapting language models to target domains through data-efficient methods; LLM Agents & Reasoning for improving language model capabilities through environmental interaction; AI Alignment for ensuring responsible deployment through uncertainty quantification and factuality; and AI for Science for leveraging foundation models in material science, biomedical research, and urban science. His work emphasizes weakly-supervised learning, out-of-distribution generalization, and interpretable machine learning approaches.

His publication record shows consistent high-impact contributions across top AI conferences including NeurIPS, ICML, KDD, ACL, and ICLR, with recent work focusing on enhancing LLM capabilities, improving model robustness, and applying AI to scientific discovery. The trend in his publications demonstrates a clear trajectory from foundational machine learning techniques toward increasingly sophisticated applications of large language models and their integration with scientific domains.

• NSF CAREER Award (2022)
• Google Faculty Research Award (2020)
• Amazon AWS Machine Learning Research Award (2020)
• Facebook Faculty Research Award (2021)
• Kolon Faculty Fellowship (2021)
• ACM SIGKDD Dissertation Runner-up Award (2019)
• Georgia Tech CoC Outstanding Junior Faculty Award (2024)

Dr. Zhang actively mentors a large group of graduate students across computer science, machine learning, and computational science programs, with alumni securing positions at leading tech companies and research institutions. His research has been generously supported by NSF grants (IIS CAREER-2144338, IIS-2106961, IIS-2008334), ONR MURI funding, and industry partnerships with Kolon, HomeDepot, ADP, and Adobe. His lab focuses on developing practical AI systems that address real-world challenges while advancing fundamental understanding of machine learning principles.

Dr. Zhang leads research efforts in the Georgia Tech AI ecosystem that bridge theoretical advances with practical applications, particularly in scientific domains. His group develops methods for data-efficient LLM adaptation, LLM agent reasoning capabilities, uncertainty quantification, and domain-specific foundation models, with active collaborations across material science, biomedical research, and urban science domains.