Chong Wang

Chong Wang's Homepage

I am a Project Scientist in Eric Xing's group, Machine Learning Department, Carnegie Mellon University.
My PhD advisor was David M. Blei from Princeton University.

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Carnegie Mellon University
Machine Learning Department, GHC 8110
5000 Forbes Avenue, Pittsburgh, PA 15213

Curriculum Vitae

Research interests:
- Probabilistic graphical models
- Large scale (approximate) inference for latent variable models
- Recommender systems, topic modeling and knowledge discovery and dissemination.
Awards:
- ACM Doctoral Dissertation Award Nomination, 2012
- Siebel Scholar, class of 2012
- Best student paper award at KDD 2011
- Notable paper award at AISTATS 2011
- Google PhD fellowship in machine learning 2010-2012
- Outstanding student paper honorable mention at NIPS 2009
Past working experiences:
- Research intern, Google Research New York, summer 2011
- Research intern, Microsoft Research Redmond, summer 2008 and 2009
- Assistant researcher, Microsoft Research Asia, 2006-2007
Recent work (More):
- Rajesh Ranganath, Chong Wang, David Blei and Eric Xing.
  An adaptive learning rate for stochastic variational inference.
  In ICML 2013.
- Chong Wang and David M. Blei.
  Variational inference in nonconjugate models.
  To appear in JMLR. [code]
- Matt Hoffman, David M. Blei, Chong Wang and John Paisley.
  Stochastic variational inference.
  To appear in JMLR.
- Chong Wang and David M. Blei.
  Truncation-free stochastic variational inference for Bayesian nonparametric models.
  To appear in NIPS 2012.
- Jason Weston, Chong Wang, Ron Weiss and Adam Berenzweig.
  Latent Collaborative Retrieval.
  In ICML 2012. Oral presentation.
- Chong Wang and David M. Blei.
  Collaborative topic modeling for recommending scientific articles.
  In KDD 2011. Oral presentation and best student paper award. [demo]
- Chong Wang, John Paisley and David M. Blei.
  Online variational inference for the hierarchical Dirichlet process.
  In AISTATS 2011. Oral presentation.
- John Paisley, Chong Wang and David M. Blei.
  The discrete infinite logistic normal distribution for mixed-membership modeling.
  In AISTATS 2011. Oral presentation and notable paper award.
List of software (Email me for those not listed; sorry for slow responses sometimes.)