Yang Liu

 

Yang Liu
Postdoctoral fellow
SEAS, CRCS, Harvard University
yangl(at)seas.harvard.edu
Office: MD-110


About me

    Hello. I'm currently a postdoctoral fellow at Harvard University. I am very fortunate to be hosted by Professor Yiling Chen. I also affiliate with the Center for Research on Computation and Society. Prior to joining Harvard, I obtained my Ph.D degree from the University of Michigan, Ann Arbor in 2015, where I was happily advised by Professor Mingyan Liu. Before that, I got my Bachelor degree from Shanghai Jiao Tong University, China in 2010; then I went to Ann Arbor and obtained my Master of Science in EE:Systems (OR) and Mathematics, in 2012 and 2014 respectively, both from the University of Michigan.

New.

  • Attended BayesianCrowd and presented our results on "Machine Learning aided Peer Prediction" (July 3-4).
  • Our paper "Calibrated Fairness in Bandits" (with Goran Radanovic, Christos Dimitrakakis, Debmalya Mandal and David Parkes) is going to appear at FATML'17 (oral presentation, 8/48)!
  • Maintaining fairness in a decision making process: my paper "Fair Optimal Stopping Policy for Sequential Matching" is going to appear at UAI'17.
  • New papers to appear: Machine Learning aided Peer Prediction (for data elicitation) at EC'17 (with Yiling), and Crowd Learning: Improving Online Decision Making Using Crowdsourced Data at IJCAI'17 (with Mingyan).
  • Our data breach prediction work (USENIX SEC15: Cloudy with a Chance of Breach ..) is featured in a WSJ Article.

Research

I enjoy researching on leveraging AI/ML approaches to address EconCS and social computation type of questions. Examples of topics of interest: machine learning aided incentive design, information elicitation without verification, crowdsourcing, fairness in AI/ML, forecasting cyber security events.

Selected / Recent Publications

<Click for the Full List>

Machine learning aided information elicitation (learning to incentivize)

Yang Liu and Yiling Chen
Machine Learning aided Peer Prediction.
ACM EC 2017, Cambridge, United States.

Yang Liu and Yiling Chen
A Bandit Framework for Strategic Regression.
NIPS 2016, Barcelona, Spain.

Learning the wisdom of crowd

Yang Liu and Mingyan Liu
An Online Learning Approach to Improving the Quality of Crowd-Sourcing.
ACM SIGMETRICS 2015, Portland, United States.
Extended version at ACM/IEEE Transaction on Networkings, 2017.

Yang Liu and Mingyan Liu
Crowd Learning: Improving Online Decision Making Using Crowdsourced Data.
IJCAI 2017, Melbourne, Australia.

Fairness in AI/ML

Yang Liu
Fair Optimal Stopping Policy for Sequential Matching.
UAI 2017, Sydney, Australia.

Yang Liu, Goran Radanovic, Christos Dimitrakakis, Debmalya Mandal and David Parkes
Calibrated Fairness in Bandits.
FATML 2017, Halifax, Canada.

Data driven Cyber Security

Yang Liu, Armin Sarabi, Jing Zhang, Parinaz Ardabili, Manish Karir, Michael Bailey and Mingyan Liu
Cloudy with a Chance of Breach: Forecasting Cyber Security Incidents.
USENIX Security 2015, Washington, D.C., United States.

Patent

Mingyan Liu, Manish Karir, Michael Bailey, Yang Liu and Jing Zhang
Rating Network Maliciousness and Comparing Network Maliciousness Through Similarity Analyses.
Patent No. 62/026,349.