I am Head of Data Science at Etsy Inc., managing a group of data scientists to deliver cutting-edge scientific solutions for:
- Search and Discovery
- Personalization and Recommendation
- Computational Advertising
by utilizing a wide range of technologies including deep learning, probabilistic modeling, image understanding (computer vision), user profiling, query understanding, text mining and others.
Learn more about us and we are hiring:
- Data scientists (at all levels), specializing in machine learning and AI
- Data scientists (at all levels), specializing in statistics and analytics
- 2017-09-07 (Upcoming) Talk at Big Data Innovation Summit Boston 2017, Boston, MA.
- 2017-08-10 Talk at Insights Data Science, New York about “AI in E-Commerce at Etsy“. [Slides]
- 2017-06-28 Talk at Machine Learning Summit, Beijing, China about “AI in E-Commerce at Etsy“. [Slides]
- 2017-06-23 Talk at Chinese Academy of Sciences, Beijing, China about “GB-CENT“. [Slides]
- 2017-06-14 Talk at Data Science Pop-up, New York City, NY about “GB-CENT“. [Slides]
- 2017-06-06 Talk at Machine Learning Innovation Summit, San Francisco, CA about “GB-CENT“. [Slides]
- 2017-04-12 Talk at CSE Dept. at Lehigh University, Bethlehem, PA about “GB-CENT“. [Slides]
- 2017-03-28 Talk at Global Predictive Analytics Conference, Santa Clara, CA about “GB-CENT“. [Slides]
- 2017-02-22 Talk at Predictive Analytics Innovation Summit, San Diego, CA about “GB-CENT“. [Slides]
- 2016-12-05 Talk at Department of Statistics at Columbia University, New York, NY about “Data Science at Etsy“.
- 2017-08 “Returning is Believing: Optimizing Long-term User Engagement in Recommender Systems” in CIKM 2017.
- 2017-06 “An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy” in AdKDD and TargetAd 2017 workshop at KDD 2017.
- 2017-06 “A Gradient-based Adaptive Learning Framework for Efficient Personal Recommendation” in RecSys 2017. [PDF]
- 2017-06 “Joint Text Embedding for Personalized Content-based Recommendation” in ArXiv. [PDF]
- 2017-05 “On Sampling Strategies for Neural Network-based Collaborative Filtering” in KDD 2017. [PDF] [Code]
- 2016-12 “GB-CENT: Gradient Boosted Categorical Embedding and Numerical Trees” in WWW 2017. [PDF]
Previously, I was Senior Manager of Research at Yahoo Research from 2013 to 2016, leading science efforts for Personalization and Search Sciences. Our team helped to drive science solutions for a wide range of products including Yahoo Homepage News Streams, Yahoo Aviate App Recommendation, Yahoo Tumblr Blog Recommendation, Yahoo Video Recommendation, Yahoo Assistant/Bot Platform and Yahoo Mobile Search. I have published papers in all major international conferences in data mining, machine learning and information retrieval, such as SIGIR, WWW, KDD, CIKM, AAAI, WSDM, RecSys and ICML with more than 2,000 citations (H-index: 17), winning WWW 2011 Best Poster Paper Award, WSDM 2013 Best Paper Nominated and RecSys 2014 Best Paper Award, serving as program committee members in KDD, WWW, SIGIR, WSDM, AAAI, EMNLP, ICWSM, ACL, CIKM, IJCAI as well as several workshops. In addition, I constantly review articles in most prestige journals such as DMKD, TKDD, TIST, TIS, and TKDE. I have co-founded User Engagement Optimization Workshop, which has been held in conjunction with CIKM 2013 and with KDD 2014. Prior to Yahoo Research, I was a research assistant in Department of Computer Science and Engineering at Lehigh University, where I was a member of WUME lab from 2008 to 2013, working with Brian D. Davison. I obtained my Ph.D. (2013), M.S. (2010) from Lehigh University and B.S. (2007) from Beijing University of Chemical Technology, all in Computer Science.