A comfort corner
for science

A little hello from Yao Zhou

Without precise calculations we could fly right through a star or bounce too close to a supernova and that'd end your trip real quick, wouldn't it?

--Han Solo, Star Wars

About me


Current: Ph.D. candidate in CS @ Arizona State University
Advisor: Jingrui He
2nd M.S. in CS @ Oregon State University
Advisor: Sinisa Todorovic
1st M.S. in EE @ University of Rochester
Advisor: Jiebo Luo


Pattern Classification

Fundamentals of Numerical Computation

Convex Optimization

Pattern Recognition and Machine Learning


New York: Rochester, NYC, Buffalo, Niagara Fall

Washington: Seattle, Olympic National Forest, Tacoma.

Oregon: Portland, Corvallis, Albany, Salem, Eugene, Newport.


Everything is edible.


Running and Basketball.


Sci-fi Movies and PS4 Games.


    Currently I'm interest in Data Mining and Machine Learning problems. Specifically, my research topics are focused on Crowdsourcing, Heterogeneous Learning, Machine Teaching, and Medical Healthcare with Deep Learning. My former research was focused on video/image segmentation, surveillance video analysis (abnormal activity detection, pedestrain counting, etc.).


  1. Yao Zhou, Fenglong Ma, Jing Gao, Jingrui He. Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019). [ website ]


  1. Yao Zhou, Lei Ying, Jingrui He. Multi-task Crowdsourcing via an Optimization Framework. ACM Transactions on Knowledge Discovery from Data (TKDD 2019). To appear. [ pdf ]
  2. Yao Zhou, Arun Reddy Nelakurthi, Jingrui He. Unlearn What You Have Learned: Adaptive Crowd Teaching with Exponentially Decayed Memory Learners. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2018). (Long Presentation) [ pdf ] [ slides ] [ code & data ] [ video ]
  3. Yao Zhou, Jingrui He. Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching. International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction (SBP 2018). (Doctoral Consortium) [ pdf ]
  4. Pengfei Jiang, Weina Wang, Yao Zhou, Jingrui He, Lei Ying. A Winners-Take-All Incentive Mechanism for Crowd-Powered Systems. SIGMETRICS Workshop (NetEcon 2018).
  5. Yao Zhou, and Jingrui He. A Randomized Approach for Crowdsourcing in the Presence of Multiple Views. IEEE International Conference on Data Mining (ICDM 2017). (Long Paper) [ pdf ] [ slides ]
  6. Yao Zhou, Lei Ying, Jingrui He. MultiC2: an Optimization Framework for Learning from Task and Worker Dual Heterogeneity. SIAM International Conference on Data Mining (SDM 2017). (Oral & Poster) [ pdf ] [ slides ]
  7. Yao Zhou, Jingrui He. Crowdsourcing via Tensor Augmentation and Completion. 25th International Joint Conference on Artificial Intelligence (IJCAI 2016). (Oral & Poster) [ pdf ] [ slides ]
  8. Yao Zhou, Jiebo Luo. A Practical Method for Counting Arbitrary Target Objects in an Arbitrary Scene. IEEE International Conference on Multimedia & Expo (ICME 2013).

  9. [ Yao Zhou Google Scholar ]


    A Review of Unsupervised Video Segmentation. [ pdf ]

Professional Service

    Program Committees of
      ICML 19, NeurIPS (18,19), AAAI (18,19), IJCAI 19, SDM 19, PAKDD (18,19), BigData (18,19);
    Subreviewers for
      ICML 18, KDD (18, 19), IJCAI (16,17,18), AAAI 17, ICDM (16,17,18), UAI 19;
      WSDM (16,18, 19), WWW (17, 19), ACML 18, AISTATS (17,18,19), CIKM (16,17,18);
      ASONAM (16,17,18), DASFAA (17,18), DSAA 17, HIPC 18.
Get in Touch

Contact Me

699 S. Mill Ave. Tempe, AZ 85281

yzhou174 (at) asu (dot) edu

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web counter Last webpage update: Jan 8 2019