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
Email: chaozhang@gatech.edu

Hi! I am an Assistant Professor at the School of CSE at Georgia Tech. Before joining Georgia Tech, I received my Ph.D. in Computer Science from UIUC in 2018, where I worked with Prof. Jiawei Han.

Research

Interests

The goal of my research is to develop machine learning methods that model large-scale data to solve practical and challenging problems in science and engineering. I work a lot with spatiotemporal data, text data, and graph data. In particular, I have been focused on the following topics: (1) knowledge extraction and text mining; (2) spatiotemporal data analysis; (3) uncertainty quantification for deep learning; (4) learning from limited supervision; (5) generation and optimization. More broadly, I am interested in the following topics:

  • Data Mining: Spatiotemporal Data Mining, Text Mining, Graph Mining
  • Machine Learning: Self-Supervised Learning, Weakly-Supervised Learning, Uncertainty Quantification, Decision Making Under Uncertainty
  • Natural Language Processing: Information Extraction

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.

Thrusts

News

  • [Pinned] Check out my book Multidimensional Mining of Massive Text Data published by Morgan & Claypool, also available on Amazon.
  • Grateful to receive the NSF Career award!
  • Three papers accepted by NAACL'22, discussing semi-structured session graph pretraining, uncertainty-based active self-training, and differentiable self-training.
  • Our paper on interactive weakly-supervised learning is accepted by ACL'22.
  • Our paper on uncertainty-aware multi-view time series forecasting is accepted by WWW'22.
  • Two paper accepted by NeurIPS 2021 on graph pre-training and uncertainty quantification for time series forecasting.
  • Grateful to receive an NSF IIS grant on uncertainty quantification for time series forecasting.
  • Grateful to receive 2021 Facebook Faculty Research Award.
  • Multiple papers accepted by KDD'20 and ICML'20.
  • Multiple papers accepted by EMNLP'20, and NAACL'21.
  • Congrats to my student Wendi Ren for winning the Marshall D. Williamson Fellowship.
  • Grateful to receive 2020 Amazon Faculty Award.
  • Thanks to NSF IIS for supporting our research on using transformers for sequential data.
  • Grateful to receive 2020 Google Faculty Research Award.
  • Two papers accepted by the Web Conference 2020.
  • Honored to receive the ACM SIGKDD 2019 Dissertation Runner-up Award.

Publications

(* denotes equal contribution)

2022

2021

2020

2019

2018

2017

Earlier

Awards

  • 2022 NSF Career Award
  • 2021 Facebook Faculty Research Award
  • 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

Software

  • 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

Teaching

Students

Prospective students: I am always looking for strong and motivated students to join our group. But due to the large volume of emails I receive, I am unable to respond to every email. If you are interested in working with me, please fill out this form, for which I will review the responses regularly. You can also apply to GT's related Ph.D. Programs (CSE, CS, ML) and specify my name in your applications.

Current:

  • Rui Feng: Ph.D. Student in CS
  • Lingkai Kong: Ph.D. Student in CSE
  • Yinghao Li: Ph.D. Student in ML
  • 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 (co-advised with Prof. Rampi Ramprasad)
  • Kevin Tynes: Ph.D. Student in ML (co-advised with Prof. Peng Qiu)
  • Yanbo Xu: Ph.D. Student in ML (co-advised with Prof. Alexey Tumanov)
  • Piyush Patil: M.S. Student in CS
  • Mengyang Liu: M.S. Student in CSE
  • Vidit Jain: M.S. Student in CS
  • Mukund Rungta: M.S. Student in CS
  • Junyang Zhang: B.S. Student in CS

Alumni:

  • Isaac Rehg: M.S. in CS
  • Wendi Ren: M.S. in CSE
  • Ruijia Wang: M.S. in CSE
  • Yi Rong: Visiting Ph.D. Student