Hello!

I am an Applied Scientist at Amazon Search. My research interests are data mining, machine learning, and graph-structured data.

I obtained my PhD degree from College of IST, the Pennsylvania State University. I was very fortunate to have been advised by Dr. Prasenjit Mitra and Dr. Suhang Wang. Prior to this, I received my Bachelor of Engineering degree from Computer Science Department, University of Science and Technology of China.

I am hiring a research intern to work on cost-effective machine learning. If you are a PhD student and are interested in the topic, please send me your CV.

Interests

  • Machine Learning
  • Data Mining
  • Graph-structured Data

Education

  • PhD in College of IST, 2016 - 2020

    Pennsylvania State University

  • BEng in Computer Science, 2012 - 2016

    University of Science and Technology of China

News & Updates

  • 01/2021: One paper is accepted to WWW 2021
  • 07/2020: Two papers are accepted to CIKM 2020
  • 05/2020: Two papers are accepted to KDD 2020
  • Website setup

Experiences

 
 
 
 
 

Applied Scientist II

Amazon

Jan 2021 – Present Palo Alto
 
 
 
 
 

Research Intern

Snap Inc.

Sep 2020 – Dec 2019 Santa Monica
 
 
 
 
 

Research Intern

Pinterest

May 2020 – Aug 2020 San Francisco
 
 
 
 
 

Research Intern

Snap Inc.

Sep 2019 – Dec 2019 Santa Monica
 
 
 
 
 

Research Intern

Bytedance

May 2019 – Aug 2019 Palo Alto
 
 
 
 
 

Research Intern

Intellifusion

May 2018 – Aug 2018 Shenzhen
 
 
 
 
 

Research Intern

Microsoft Research Asia

Jul 2015 – May 2016 Beijing

Publications

(2020). Investigating and Mitigating Degree-Related Biases in Graph Convoltuional Networks. Proceedings of the 2020 ACM on Conference on Information and Knowledge Management.

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(2020). Semi-Supervised Graph-to-Graph Translation. Proceedings of the 2020 ACM on Conference on Information and Knowledge Management.

(2020). Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.

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(2020). Graph Structure Learning for Robust Graph Neural Networks. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.

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(2020). Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach. Proceedings of The Web Conference 2020.

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(2020). Find you if you drive: Inferring home locations for vehicles with surveillance camera data. Knowledge-Based Systems.

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(2020). Layer-constrained variational autoencoding kernel density estimation model for anomaly detection. Knowledge-Based Systems.

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(2020). Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values. AAAI Conference on Artificial Intelligence (AAAI).

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(2020). Transferring Robustness for Graph Neural Network Against Poisoning Attacks. ACM International Conference on Web Search and Data Mining (WSDM).

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(2019). A simple baseline for travel time estimation using large-scale trip data. ACM Transactions on Intelligent Systems and Technology (TIST).

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(2019). Citywide Traffic Volume Inference with Surveillance Camera Records. IEEE Transactions on Big Data.

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(2019). Joint modeling of dense and incomplete trajectories for citywide traffic volume inference. The World Wide Web Conference.

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(2019). Learning from multiple cities: A meta-learning approach for spatial-temporal prediction. The World Wide Web Conference.

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(2019). MEGAN: a generative adversarial network for multi-view network embedding. IJCAI'19 Proceedings of the 28th International Joint Conference on Artificial Intelligence.

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(2019). Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction. AAAI Conference on Artificial Intelligence, 2019.

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(2018). Deep multi-view spatial-temporal network for taxi demand prediction. Thirty-Second AAAI Conference on Artificial Intelligence.

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(2018). Representation learning for large-scale dynamic networks. International Conference on Database Systems for Advanced Applications.

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