Chao Zhang

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Assistant Professor
School of Computational Science and Engineering
College of Computing
Georgia Institute of Technology

Office: CODA E1358B
Address: 756 W Peachtree St NW, Atlanta, GA 30308


I develop data science and machine learning models to assist with task solving and decision-making. My research focuses on rapidly developing AI systems that are more data-efficient, trustworthy, and customized for specific domains.

Currently, I am working on the following themes:

  1. Data-Centric LLM Adaptation – Adapting Large Language Models for target domains by addressing data scarcity challenges through data-efficient methods such as feedback-based adaptation, weak supervision, and active learning.
  2. Trustworthy AI – Making AI models more reliable and aligned with human values through uncertainty quantification, robust fine-tuning, scalable alignment, and decision-focused learning.
  3. AI for Science – Leveraging data science and AI to accelerate scientific discovery in fields like material science, biomedical and life sciences, and urban science through close collaboration with domain experts.

Acknowledgment: My work has been generously supported by research funding/gift from NSF (IIS CAREER-2144338, IIS-2106961, IIS-2008334), ONR MURI , Kolon, HomeDepot, ADP, and Adobe. My work has also been recognized by an NSF CAREER Award, a Facebook Faculty Award, an Amazon AWS Machine Learning Research Award, a Google Faculty Research Award, a Kolon Faculty Fellowship, an ACM SIGKDD Dissertation Runner-up Award, and several paper awards from IMWUT (UbiComp), ECML/PKDD, and ML4H.

I. Data-Centric LLM

We aim to adapt LLMs to various domains and complex tasks. A key bottleneck when adapting LLMs is data scarcity – the lack of high-quality, representative data for the target domain. We are tackling this bottleneck by developing methods for data-efficient LLM adaptation, including:

II. Trustworthy AI

We aim to develop AI systems that are not only capable but also trustworthy for deployment in various domains. We focus on several aspects of trustworthy AI: uncertainty quantification for deep learning, robust LLM fine-tuning and alignment, and decision-focused learning.

III. AI for Science


  • 2024 GaTech CoC Outstanding Junior Faculty Award
  • 2022 NSF Career Award
  • 2022 ML4H Outstanding Paper Award
  • 2021 Facebook Faculty Research Award
  • 2021 Kolon Faculty Fellowship
  • 2020 Amazon AWS Machine Learning Research Award
  • 2020 Google Faculty Research Award
  • 2019 ACM SIGKDD Dissertation Award Runner-up
  • 2018 ACM IMWUT Distinguished Paper Award
  • 2015 ECML/PKDD Best Student Paper Runner-up Award
  • 2013 Chiang Chen Overseas Graduate Fellowship


(* denotes equal contribution)











Prospective students: I am always looking for strong and motivated students to join our group. If you are interested in working with me, you can either email me or fill out this form.


  • Rui Feng: Ph.D. Student in CS
  • Lingkai Kong: Ph.D. Student in CSE
  • Yinghao Li: Ph.D. Student in ML
  • Agam A. Shah: Ph.D. Student in ML (co-advised with Sudheer Chava)
  • Haotian Sun: Ph.D. Student in ML (co-advised with Bo Dai)
  • Haorui Wang: Ph.D. Student in CSE
  • Kuan Wang: Ph.D. Student in CSE
  • Yue Yu: Ph.D. Student in CSE
  • Rongzhi Zhang: Ph.D. Student in ML
  • Yuchen Zhuang: Ph.D. Student in ML
  • Jacob Wessell: M.S. student in CS
  • Wenhao Mu: M.S. student in CS
  • Shangqing Xu: M.S. student in CS


  • Yanbo Xu: Ph.D., 2023 (First Employment: Microsoft Research)
  • Binghong Chen: Ph.D., 2023 (co-advised with Prof. Le Song, First Employment: Citadel Capital)
  • Pranav Shetty: Ph.D., 2023 (JP Morgan AI Ph.D. Fellowship, co-advised with Prof. Rampi Ramprasad, First Employment: JP Morgan Chase)
  • Vidit Jain: M.S. Student in CS
  • Mukund Rungta: M.S. Student in CS
  • Junyang Zhang: M.S. Student in CS
  • Piyush Patil: M.S. Student in CS
  • Mengyang Liu: M.S. Student in CSE
  • Isaac Rehg: M.S. in CS
  • Wendi Ren: M.S. in CSE
  • Ruijia Wang: M.S. in CSE
  • Yi Rong: Visiting Ph.D. Student