I'm an AI researcher studying how people and computers can work together more effectively. I'm primarily interested in program synthesis, probabilistic programming, and concept learning. In the past, I studied cognitive psychology as a PhD student and then postdoc at Stanford.

Publications

• webppl-oed: A practical optimal experiment design system
  Ouyang, L., Tessler, M.H., Ly, D., & Goodman, N. D.
  CogSci 2018
• Pedagogical learning
  Ouyang, L. & Frank, M.C.
  NIPS 2017 Teaching Machines workshop
  
• Support and influence analysis for visualizing posteriors of probabilistic programs
  Ouyang, L.
  POPL 2017 Probabilistic Programming Systems workshop
  
• Semantic coherence facilitates distributional learning
  Ouyang, L., Boroditsky, L., & Frank, M. C.
  Cognitive Science
  Code & data
• Fabular: Regression formulas as probabilistic programming
  Borgstrom, J. D., Gordon, A. D., Ouyang, L., Russio, C., Scibior A., & Szymczak, M. POPL 2016
• Semantic coherence facilitates distributional learning of word meanings
  Ouyang, L., Boroditsky, L., & Frank, M. C.
  CogSci 2012

Presentations

• Probabilistic programming tutorial
  Stuhlmüller, A., & Ouyang, L.
  Next.ML San Francisco 2015
• Transfer in human concept learning
  Ouyang, L. & Goodman, N. D.
  UAI 2012 Natural Intelligence workshop

Manuscripts

• Bayesian Inference of Regular Expressions from Human-Generated Example Strings
  Ouyang, L.
  
• Learning to learn by teaching: generating helpful examples facilitates learning from helpful examples
  Ouyang, L., MacPherson A., & Goodman N. D