• Short BIO

    Currently, I am a third-year Ph.D. candidate at Pennsylvania State University under the advisory of Prof. Zhenhui (Jessie) Li. I got my B.Eng. degree from the University of Electronic Science and Technology of China under the advisory of Prof. Defu Lian and Prof. Tao Zhou.

     

    My current research focuses on developing computational methods to improve the generalization capability of machine learning algorithms through the combination of knowledge transfer and relation learning. I am also passionate about applying these methods for solving social problems (i.e., AI for social good). Revolving around this goal, I mainly study the following topics:

    - Machine Learning: meta-learning, few-shot learning, transfer learning, multi-task learning, continual learning, AutoML, relation learning, graph neural network

    - Applications: AI for social goods (e.g., transportation, environment, education, healthcare, misinformation), language modeling

     

    My full CV pdf format is available. (Last update: Dec. 2019)

    News

    [2020.07] One paper was accepted by CIKM 2020

    [2020.05] Join Amazon A9 as an applied scientist intern

    [2020.05] Our tutorial "Learning with Small Data" was accepted by KDD 2020

    [2020.03] Our tutorial "Meta-learning and Automated Machine Learning: Approaches and Applications" was accepted by IJCAI 2020

    [2020.02] Honor to receive College of IST Ph.D. Award for Research Excellence

    [2020.02] Honor to attend and present our tutorial "Learning with Small Data" in WSDM 2020

    [2019.12] One paper was accepted by ICLR 2020

    [2019.11] Four papers were accepted by AAAI 2020

    [2019.10] Our tutorial "Learning with Small Data" was accepted by WSDM 2020

    [2019.10] One paper was accepted by WSDM 2020

    [2019.10] Two papers were accepted by NeurIPS 2019 GRL workshop and MetaLearn workshop, respectively

    [2019.09] Join Salesforce Research (MetaMind) as a research intern

    [2019.05] Join Alibaba DAMO Academy as a research intern

    [2019.04] One paper was accepted by ICML 2019

    [2019.01] Two papers were accepted by WWW 2019

    [2018.12] One paper was accepted by ACM TIST

    [2018.10] One paper was accepted by AAAI 2019

    [2018.05] Join machine learning group@Tencent AI Lab as a research intern

    [2018.05] One paper was accepted by KDD 2018

  • Education

    Pennsylvania State University

    2017 - 2021 (Expected)

    Ph.D. in Information Science and Technology

     

    Advisor: Prof. Zhenhui (Jessie) Li

    Thesis: Cost-efficient learning via Transferable Structured Experience

    Committee members: Prof. Zhenhui (Jessie) Li, Prof. Suhang Wang, Prof. Xiang Zhang, Prof. Lingzhou Xue

    University of Electronic Science and Technology of China

    2012 - 2016

    B.Eng. in Electronic Information Engineering

     

    Advisor: Prof. Defu Lian & Prof. Tao Zhou

    Thesis: Academic Performance Prediction and Social Network Analysis based on Campus Data

  • INDUSTRY EXPERIENCE

    Amazon A9

    Applied Scientist Intern

    May 2020 - now

    Mentor: Dr. Nikhil Rao, Dr. Sumeet Katariya, Prof. Sujay Sanghavi

    Topic: Large-scale multi-task/meta-learning, low-resource NLP

    Salesforce Research (MetaMind)

    Research Intern

    Sep. 2019 - Dec. 2019

    Mentor: Dr. Caiming Xiong, Dr. Yingbo Zhou, Manager: Dr. Richard Socher

    Topic: Continual learning

    Alibaba DAMO Academy, USA

    Research Intern

    May 2019 - Aug. 2019

    Data Analytics and Intelligence Lab

    Mentor & Manager: Dr. Bolin Ding

    Topic: Automated meta-learning

    Tencent AI Lab

    Research Intern

    May 2018 – Aug. 2018

    Machine Learning Group

    Mentor: Dr. Ying Wei, Manager: Prof. Junzhou Huang

    Topic: Gradient-based meta-learning

    Didi AI Labs

    Research Intern

    Mar. 2017 – Jun. 2017

    Mentor: Dr. Pinghua Gong, Manager: Prof. Jieping Ye

    Topic: Taxi demand prediction

  • Selected publicationS

    Full list of publications: Google Scholar, DBLP

    Tutorial

    [1] Huaxiu Yao, Xiaowei Jia, Vipin Kumar, Zhenhui Li, Learning with Small Data, Conference Tutorial at the 26th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2020), San Diego, CA, Aug. 2020. [Website] [Slides]

     

    [2] Xin Wang, Huaxiu Yao, Ying Wei, Zhenhui Li, Wenwu Zhu, Wenpeng Zhang, Meta-learning and Automated Machine Learning: Approaches and Applications​, Conference Tutorial at the 29th International Joint Conference on Artificial Intelligence (IJCAI 2020), Yokohama, Japan, July 2020. [Website]

     

    [3] Zhenhui Li, Huaxiu Yao, Fenglong Ma, Learning with Small Data, Conference Tutorial at the 13th ACM International Conference on Web Search and Data Mining (WSDM 2020), Houston, TX, Feb. 2020. [Website]

    Preprint

    [1] Huaxiu Yao, Longkai Huang, Ying Wei, Li Tian, Junzhou Huang, Zhenhui Li, Don't Overlook the Support Set: Towards Improving Generalization in Meta-learning, arXiv 2007.13040. [arXiv]

     

    [2] Jiatu Shi*, Huaxiu Yao*, Xian Wu, Tong Li, Zedong Lin, Tengfei Wang, Binqiang Zhao, Zhenhui Li, Relation-aware Meta-learning for Market Segment Demand Prediction with Limited Records, arXiv 2008.00181. [arXiv] (*: equal contribution)

     

    [3] Ying Wei, Peilin Zhao, Huaxiu Yao, Junzhou Huang, Transferable Neural Processes for Hyperparameter Optimization, arXiv 1909.03209 (the short version is published in NeurIPS 2019 Workshop on Meta-Learning). [arXiv]

    2020

    [1] Huaxiu Yao, Xian Wu, Zhiqiang Tao, Yaliang Li, Bolin Ding, Ruirui Li, Zhenhui Li, Automated Relational Meta-learning, in Proceeding of the Eighth International Conference on Learning Representations (ICLR 2020), Addis Ababa, Ethiopia, Apr. 2020. [Openreview] [PDF] [Code] [Slides] [arXiv]

     

    [2] Huaxiu Yao, Chuxu Zhang, Ying Wei, Meng Jiang, Suhang Wang, Junzhou Huang, Nitesh V. Chawla, Zhenhui Li, Graph Few-shot Learning via Knowledge Transfer, in Proceeding of the Thirty-fourth AAAI Conference on Artificial Intelligence (AAAI 2020), New York, NY, Feb. 2020 (the short version is published in NeurIPS 2019 Graph Representation Learning Workshop). [PDF] [Full Paper] [Code] [arXiv]

     

    [3] Chuxu Zhang, Huaxiu Yao, Chao Huang, Meng Jiang, Zhenhui Li, Nitesh V. Chawla, Few-Shot Knowledge Graph Completion, in Proceeding of the Thirty-fourth AAAI Conference on Artificial Intelligence (AAAI 2020), New York, NY, Feb. 2020. [PDF] [Code] [Poster] [arXiv]

     

    [4] Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Charu Aggarwal, Prasenjit Mitra, Suhang Wang, Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values, in Proceeding of the Thirty-fourth AAAI Conference on Artificial Intelligence (AAAI 2020), New York, NY, Feb. 2020. [PDF] [arXiv]

     

    [5] Xinshi Zang, Huaxiu Yao, Guanjie Zheng, Nan Xu, Kai Xu, Zhenhui Li, MetaLight: Value-based Meta-reinforcement Learning for Online Universal Traffic Signal Control, in Proceeding of the Thirty-fourth AAAI Conference on Artificial Intelligence (AAAI 2020), New York, NY, Feb. 2020. [PDF] [Code] [Poster]

     

    [6] Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Yiqi Wang, Jiliang Tang, Charu Aggarwal, Prasenjit Mitra, Suhang Wang, Investigating and Mitigating Degree-Related Biases in Graph Convolutional Networks, in Proceeding of the 29th ACM International Conference on Information and Knowledge Management (CIKM 2020), Galway, Ireland, Oct. 2020. [arXiv]

     

    [7] Xianfeng Tang, Yandong Li, Yiwei Sun, Huaxiu Yao, Prasenjit Mitra, Suhang Wang, Transferring Robustness for Graph Neural Network Against Poisoning Attacks, in Proceeding of the 13th ACM International Conference on Web Search and Data Mining (WSDM 2020), Houston, TX, Feb. 2020. [PDF] [Code] [arXiv]

    2019

    [8] Huaxiu Yao, Ying Wei, Junzhou Huang, Zhenhui Li, Hierarchically Structured Meta-learning, in Proceeding of the 36th International Conference on Machine Learning (ICML 2019), Long Beach, CA, June 2019. [PDF] [Supplementary] [Slides] [Poster] [Code] [arXiv]

     

    [9] Huaxiu Yao, Yiding Liu, Ying Wei, Xianfeng Tang, Zhenhui Li, Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction, in Proceeding of The Web Conference 2019 (WWW 2019), San Francisco, CA, May 2019 (Research Track, Long Paper). [PDF] [Slides] [Poster] [Code] [arXiv]

     

    [10] Xianfeng Tang, Boqing Gong, Yanwei Yu, Huaxiu Yao, Yandong Li, Haiyong Xie, Xiaoyu Wang, Joint Modeling of Dense and Incomplete Trajectories for Citywide Traffic Volume Inference, in Proceeding of The Web Conference 2019 (WWW 2019), San Francisco, CA, May 2019 (Research Track, Long Paper). [PDF] [arXiv]

     

    [11] Huaxiu Yao*, Xianfeng Tang*, Hua Wei, Guanjie Zheng, Zhenhui Li, Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction, in Proceeding of the Thirty-third AAAI Conference on Artificial Intelligence (AAAI 2019), Honolulu, Hawaii, Jan. 2019. [PDF] [Poster] [Code] [Spotlight] [arXiv]​ (*: equal contribution)

     

    [12] Huaxiu Yao, Defu Lian, Yi Cao, Yifan Wu, Tao Zhou, Predicting Academic Performance for College Students: A Campus Behavior Perspective, ACM Transactions on Intelligent Systems and Technology (TIST) 10.3 (2019): 24. [PDF] [arXiv]

    2018

    [13] Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, Zhenhui Li, Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction, in Proceeding of the Thirty-second AAAI Conference on Artificial Intelligence (AAAI 2018), New Orleans, LA, Feb. 2018. [PDF] [Poster] [Code] [Spotlight] [Penn State News] [arXiv]

     

    [14] Hua Wei, Guanjie Zheng, Huaxiu Yao, and Zhenhui Li, IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light Control, in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018), London, UK, Aug. 2018 (Research Track). [PDF] [Code] [Demo]

  • miscellaneous

    Honors and Awards

    • College of IST Ph.D. Award for Research Excellence, Penn State University, 2020
    • AAAI Travel Award, 2020
    • NeurIPS Travel Award, 2019
    • ICML Travel Award, 2019
    • PSU IST Travel Award, 2017, 2018
    • Excellent Bachelor Thesis​ in UESTC, 2016
    • Excellent Graduates in UESTC, 2016
    • Meritorious Winner in MCM/ICM, 2016 [Report]

    Professional Services

    Conference Program Committee Member/Reviewer:

    • International Conference on Machine Learning (ICML), 2020
    • Annual Conference on Neural Information Processing Systems (NeurIPS), 2020
    • International Conference on Learning Representations (ICLR), 2020, 2021
    • ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020
    • AAAI Conference on Artificial Intelligence (AAAI), 2020
    • International Joint Conferences on Artificial Intelligence (IJCAI), 2020
    • International Conference on Information and Knowledge Management (CIKM), 2019

    Journal Invited Reviewer:

    • IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI)
    • Journal of Artificial Intelligence (AIJ)
    • ACM Transactions on Knowledge Discovery from Data (TKDD)
    • IEEE Transactions on Knowledge and Data Engineering (TKDE)
    • ACM Transaction on Intelligent System and Technology (TIST)
    • IEEE Transaction on Big Data (TBD)
    • IEEE Transactions on Intelligent Transportation Systems (TITS)
    • Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)
    • Knowledge and Information Systems (KAIS)

    Teaching Experience

    Teaching Assistant, IST 597: Reinforcement Learning, Instructor: Dr. Zihan Zhou, Fall 2019

  • contact

    Address: Room E342, Westgate Bldg, Penn State University, State College, PA, USA