Chao Zhang

My research has been centered around how to make machine learning more label-efficient, uncertainty-aware, and explainable. I work a lot with both unstructured data (e.g., text, spatiotemporal data) and structured data (e.g., graphs). And lately, I become very interested in using data mining and machine learning to analyze such data arising from scientific domains to accelerate scientific discovery, especially for material science and biomedical science. More broadly, I am interested in any intelligent systems that can augment human expertise for completing tasks and making decisions. Below are some research projects I do/did:

• Knowledge extraction and text mining: Extract information about entities, relations, taxonomies, and events from unstructured data such as social media and scientific literature. We are particularly interested in low-resource information extraction and multi-modal information extraction:
  • BOND: Bert-Assisted Open-Domain Named Entity Recognition with Distant Supervision, KDD 2020
  • STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths, KDD 2020
  • TaxoGen: Unsupervised Topic Taxonomy Construction by Adaptive Term Embedding and Clustering, KDD 2018
  • TrioVecEvent: Embedding-Based Online Local Event Detection in Geo-Tagged Tweet Streams, KDD 2017
  • GeoBurst: Real-Time Local Event Detection in Geo-Tagged Tweet Streams, SIGIR 2016
• Learning predictive models from limited data: The lack of labeled training data is a major bottleneck for training deep learning models. We aim to learn neural predictive models from a small amount of labeled data, or weakly-labeled data derived from domain knowledge. Our recent works combine self-supervised learning with self-training as a generic approach to learning from limited supervision.
  • Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach, NAACL 2021
  • Text Classification Using Label Names Only: A Language Model Self-Training Approach, EMNLP 2020
  • Denoising Multi-Source Weak Supervision for Neural Text Classification, EMNLP 2020
  • SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup, EMNLP 2020
  • Weakly-Supervised Neural Text Classification, CIKM 2018
• Uncertainty estimation and out-of-distribution robustness: Current deep learning models typically assume the training and test distributions are i.i.d., which is rarely the case in practice. As a result, deep learning models can fail miserably in open-world settings. We want machines to go beyond the training distribution: How can we quantify uncertainties for deep learning models and enable them say "I don't know" for out-of-distribution samples? How can we make them to generalize to different domains and make robust predictions? How can we make them fast adapt to tasks that were never seen before?
  • SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates, ICML 2020
  • Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data, EMNLP 2020
  • RDeepSense: Reliable Deep Mobile Computing Models with Uncertainty Estimations, UbiComp 2018
• Guided Generative Models: We study how to guide deep generative models to generate samples that satisfy pre-determined properties.
  • Transformer-Based Neural Text Generation with Syntactic Guidance, preprint
  • A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization, ICML 2019
• Spatiotemporal data mining: I have worked on spatiotemporal data mining along two lines: 1) Deep learning for predictive spatiotemporal data analytics (human mobility modeling, location recommendation); 2) Multi-modal spatiotemporal analytics for semantics-rich spatiotemporal understanding:
  • A Linear Time Approach to Computing Time Series Similarity based on Deep Metric Learning, TKDE 2020
  • DeepMove: Predicting Human Mobility with Attentional Recurrent Networks, WWW 2018
  • Regions, Periods, Activities: Uncovering Urban Dynamics via Cross-Modal Representation Learning, WWW 2017
  • Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation, KDD 2017
  • GMove: Group-Level Mobility Modeling Using Geo-Tagged Social Media, KDD 2016

• [Pinned] Check out my book Multidimensional Mining of Massive Text Data published by Morgan & Claypool, also available on Amazon.
• 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.
• Honored to receive the 2020 Amazon Faculty Award.
• Thanks to NSF for supporting our research on using transformers for sequential data.
• Honored to receive the 2020 Google Faculty Research Award.
• Two papers accepted by the Web Conference 2020.
• Honored to receive the ACM SIGKDD 2019 Dissertation Runner-up Award.

(* denotes equal contribution)

• 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
• 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 from social media streams

• 2021 Spring: CX4240: Introduction to Computational Data Analysis
• 2020 Fall: CSE8803-DLT: Deep Learning for Text Data
• 2020 Spring: CX4240: Introduction to Computational Data Analysis
• 2019 Fall: CSE8803-DLT: Deep Learning for Text Data
• 2019 Spring: CX4240: Introduction to Computational Data Analysis

• Ph.D. Students:
  • Lingkai Kong
  • Rui Feng
  • Yue Yu
  • Yinghao Li
  • Rongzhi Zhang
  • Yuchen Zhuang
  • Yanbo Xu
• Master Students:
  • Isaac Rehg
  • Ruijia Wang
• Alumni:
  • Wendi Ren
  • Yi Rong