Pengtao Xie

PhD student, Machine Learning Department,
School of Computer Science, Carnegie Mellon University.
Office: GHC 8011
Email: pengtaoxie2008@gmail.com,  pengtaox@cs.cmu.edu

Research Interests: latent variable models (LVMs). Specifically, I am interested in (1) designing LVMs to address various complex modeling issues presented in big data; (2) developing diversity-promoting regularization approaches of LVMs to capture infrequent patterns and reduce model complexity; (3) designing distributed systems to facilitate large scale inference and learning of LVMs; (4) developing privacy preservation techniques to protect users' privacy in cloud machine learning. Currently I am focusing on the second and third aspect.

Bio: I am working with Prof. Eric Xing in SAILING lab. My research interests lie in latent variable models, in particular developing diversity-promoting regularization approaches to capture infrequent patterns and reduce model complexity, and building distributed systems to facilitate large scale latent variable modeling.. Before coming to CMU, I obtained a M.E. from Tsinghua University in 2013 and a B.E. from Sichuan University in 2010. I am the recipient of Siebel Scholarship, Goldman Sachs Global Leader Scholarship and National Scholarship of China.

Misc: I am a fan of symphonies and ballet performances. I am also fond of traditional Chinese poems. Occasionally, I write some pseudo poems. In addition, I love traveling.

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Archived News

Publications


Unpublished Manuscripts


Projects


Teaching

Teaching assistant at Carnegie Mellon University for:

Student Mentoring

  • Yuntian Deng (MS student in Language Technologies Institute, CMU)
  • Yuping Gong (MS student in Language Technologies Institute, CMU)
  • Wanchao Liang (MS student in Information Networking Institute, CMU)
  • Hakim Sidahmed (MS student in Language Technologies Institute, CMU)
  • Yuan Xie (PhD student at Indiana University Bloomington)

Talks

  • Database Seminar, Carnegie Mellon University, Mar 2016. Sufficient Factor Broadcasting for Distributed Machine Learning.
  • 11th CSL Student Conference, University of Illinois Urbana-Champaign, Feb 2016. Latent Variable Modeling with Diversity-Inducing Mutual Angular Regularization. [Slides]
  • Artificial Intelligence Seminar, Carnegie Mellon University, Feb 2016. Diversity-Inducing Learning of Latent Variable Models: Frequentist and Bayesian Perspectives. [Slides]
  • VALSE Webinar, Oct 2015. Diversity Regularization of Latent Variable Models: Theory, Algorithm and Applications. [Slides]
  • Machine Learning Lunch Seminar, Carnegie Mellon University, Sept 2015. Mutual Angular Regularization of Latent Variable Models: Thoery, Algorithm and Applications. [Slides]
  • Beihang University, Aug 2015. Diversifying Restricted Boltzmann Machine for Document Modeling.
  • ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Sydney, Aug 2015. Diversifying Restricted Boltzmann Machine for Document Modeling.
  • VASC Seminar, Carnegie Mellon University, April 2015. Integrating Image Clustering and Codebook Learning.
  • Machine Learning Lunch Seminar, Carnegie Mellon University, April 2015. Integrating Data Clustering and Representation Learning.
  • SCS Student Seminar, Carnegie Mellon University, April 2015. Incorporating Word Correlation Knowledge into Topic Modeling.
  • CL+NLP Seminar, Carnegie Mellon University, Apr 2015. Incorporating Word Correlation Knowledge into Topic Modeling.
  • Database Seminar, Carnegie Mellon University, Mar 2015. Mining User Interests from Personal Photos.
  • 29th AAAI Conference on Artificial Intelligence, Austin, Jan 2015. Integrating Image Clustering and Codebook Learning.
  • Database Seminar, Carnegie Mellon University, Sept 2014. CryptGraph: Privacy Preserving Graph Analytics on Encrypted Graph.
  • Machine Learning Lunch Seminar, Carnegie Mellon University, Sept 2014. Privacy Preserving Neural Network Prediction on Encrypted Data.[Video]
  • Cylab Student Seminar, Carnegie Mellon Unversity, Sep 2014. Privacy-Preserving Neural Network Prediction on Encrypted Data.
  • SDI/ISTC Seminar, Carnegie Mellon Unversity and Intel, Sep 2014. Privacy-Preserving Neural Network Prediction on Encrypted Data.
  • Cloud Machine Learning Team and Machine Learning Group, Microsoft Research, Aug 2014. Privacy-Preserving Neural Network Prediction on Encrypted Data.
  • Cryptography Group, Microsoft Research, Jul 2014. Privacy-Preserving Neural Network Prediction on Encrypted Data.
  • 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013), Beijing, Aug 2013. Multi-Modal Distance Metric Learning.

Professional Activities

  • Reviewer for Neural Information Processing Systems (NIPS), 2016  
  • Program Committee Member for Asian Conference on Computer Vision (ACCV), 2016  
  • Reviewer for European Conference on Computer Vision (ECCV), 2016  
  • Program Committee Member for Annual Meeting of the Association for Computational Linguistics (ACL), 2016  
  • Program Committee Member for IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016  
  • Reviewer for Journal of the American Statistical Association (JASA)  
  • Program Committee Member for Annual Meeting of the Association for Computational Linguistics (ACL), 2015  
  • Reviewer for Conference on Empirical Methods in Natural Language Processing (EMNLP), 2015  
  • Reviewer for International Conference on Computer Vision (ICCV), 2015  
  • Reviewer for ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2015  
  • Reviewer for International Conference on Machine Learning (ICML), 2014  
  • Reviewer for IEEE Transactions on Multimedia (TMM)  
  • Reviewer for IEEE Transactions on Knowledge and Data Engineering (TKDE)  
  • Reviewer for IEEE Transactions on Neural Networks and Learning Systems (TNNLS)  
  • Membership: ACM, IEEE  

Industry Experience

  • Cloud Machine Learning Team and Machine Learning Group, Microsoft Research, Redmond, May 2014 -- Aug 2014.
    Mentors: Thomas Finley, Misha Bilenko, Ran Gilad-Bachrach. I also work closely with Kristin Lauter and Michael Naehrig.
  • Web Search and Mining Group, Microsoft Research Asia, Aug 2009 -- Jun 2010. Mentor: Rong Xiao.

Awards&Honors

  • Siebel Scholarship, 2014. Press Release: [CMU SCS] [CMU] [CMU LTI]
  • KDD Travel Award, 2015
  • CMU Research Fellowship, 2013-2018
  • Excellent Master Graduate Award, Dept. of CS, Tsinghua University, 2013
  • UAI Travel Award, 2013
  • Excellent Bachelor Thesis Award,2010
  • National Scholarship,2009
  • National First Prize in China Undergraduate Mathematical Contest of Modeling (CUMCM),2008
  • Goldman Sachs Global Leader Scholarship,2008