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Qian Zhao
Senior Research Engineer
Bloomberg L.P.
Email: qzhao2018@gmail.com
Employment
  • July 2018 - now, Full-time in Bloomberg L.P., New York, United States
    • Senior Research Engineer in Machine Learning Group
  • October 2012 - July 2013, Full-time in Baidu Inc., Beijing, China
    • R&D Engineer in Sponsored Ads Model Group
    • Worked on feature engineering for Click-Through Rate prediction
  • July 2010 - August 2012, Full-time in Funshion Online Technologies Co., Ltd, Beijing, China
    • R&D Engineer in Data Mining Group working on Funshion Video Recommendation System
Education
  • August 2013 - May 2018, University of Minnesota, Twin Cities, United States
    • Research Assistant in GroupLens lab advised by Prof. Joe Konstan
    • Teaching Assistant for CSci 2033: Elementary Computational Linear Algebra, Fall 2013
  • Sept. 2006 - July 2010, Nankai University, Tianjin, China
Internships
  • August 2017 - November 2017, Google Research, Mountain View, United States
    • Recommender system research and engineering intern
  • May 2017 - August 2017, Microsoft Research, Redmond, United States
    • Recommender system research intern
  • May 2016 - August 2016, Yahoo Research, Sunnyvale, United States
    • Recommender system intern scientist
  • May 2014 - July 2014, Wise Inc., Berkeley, United States
    • Data Science and Machine Learning Consultant
Award
  • Doctoral Dissertation Fellowship. 2016-17. University of Minnesota. United States.
Research Interest
  • My research interest lies at the intersection of recommender systems, human-computer interaction and machine learning. My research goal is to bring human factors of attention, perception, decision-making, goal-directed and contextualized information needs into the statistical or computational models of information recommendation systems. My research employs mixed-method approaches, by understanding prominent user factors from theory, lab experiments and field studies; developing algorithms that take account of those factors; and evaluating their effects on user experience through field experiments.