Chao Zhang

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

Office: CODA 1309
Address: 756 W Peachtree St NW, Atlanta, GA 30308


My research aims to design machine learning and data-driven models to solve practical and challenging problems in science and engineering. I am particularly interested in the following topics:

  • Learning from limited supervision: weak supervision, self supervision, unsupervised learning
  • Uncertainty in ML: uncertainty quantification, active learning, decision making under uncertainty
  • Spatiotemporal modeling: spatial graphs, time series, dynamics, 3D molecular data
  • Generation and inverse design: learning for optimization, generative models, network design
  • Knowledge extraction and NLP: information extraction, multi-modal extraction, LM pre-training & fine-tuning

On the data side, I work a lot with spatiotemporal data, text data, and graph data. On the application side, I am passionate about interdisciplinary research and enjoy developing data-driven solutions to accelerate scientific discovery through close collaboration with domain experts. The techniques I develop are motivated by applications in material science, biomedical science, transportation, and public health.

Acknowledgment: My work has been generously supported by research funding/gift from NSF (IIS CAREER-2144338, IIS-2106961, IIS-2008334), ONR MURI , Kolon, HomeDepot, 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.


Below are the main research projects at my group and some recent representative works:


(* denotes equal contribution)









  • 2022 ML4H Outstanding Paper Award
  • 2022 NSF Career 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


  • SDE-Net: Efficient uncertainty estimation for deep neural networks
  • CHMM: BERT-conditional hidden Markov model for multi-source weakly-supervised learning
  • COSINE: Language model fine-tuning with weak supervision
  • BOND: Distantly-supervised named entity recognition
  • STEAM: Automatic taxonomy expansion
  • TaxoGen: Unsupervised topic taxonomy construction from text corpus
  • WestClass: Weakly-supervised text classification
  • GeoBurst: Unsupervised spatiotemporal event detection



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
  • 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
  • Binghong Chen: Ph.D. Student in CSE (co-advised with Prof. Le Song)
  • Pranav Shetty: Ph.D. Student in ML (JP Morgan AI Ph.D. Fellowship, co-advised with Prof. Rampi Ramprasad)
  • Yanbo Xu: Ph.D. Student in ML (co-advised with Prof. Alexey Tumanov)
  • Vidit Jain: M.S. Student in CS
  • Mukund Rungta: M.S. Student in CS
  • Junyang Zhang: B.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