• Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan, Hanna Wallach. Manipulating and Measuring Model Interpretability. Transparent and Interpretable Machine Learning in Safety Critical Environments Workshop at NIPS, 2017. [pdf]
  • Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan, Hanna Wallach. Manipulating and Measuring Model Interpretability. Women in Machine Learning Workshop at NIPS, 2017. [pdf]
  • Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Leah Findlater, Kevin Seppi. ALTO: Active Learning with Topic Overviews for Speeding Label Induction and Document Labeling . Association for Computational Linguistics, 2016. [pdf]
  • Alison Smith, Tak Yeon Lee, Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Niklas Elmqvist, Kevin Seppi, Leah Findlater. Human-Centered and Interactive: Expanding the Impact of Topic Models .Proc. HCML Workshop at CHI, 2016. [pdf]
  • Alison Smith, Tak Yeon Lee, Forough Poursabzi-Sangdeh, Leah Findlater, Jordan Boyd-Graber, and Niklas Elmqvist. Evaluating Visual Representations for Topic Understanding and Their Effects on Manually Generated Labels. Transactions of the Association for Computational Linguistics, 2016.
  • Forough Poursabzi-Sangdeh and Jordan Boyd-Graber. Speeding Document Annotation with Topic Models. NAACL Student Research Workshop, 2015. [pdf]
  • Jason Chuang, John D. Wilkerson, Rebecca Weiss, Dustin Tingley, Brandon M. Stewart, Margaret E. Roberts, Forough Poursabzi-Sangdeh, Justin Grimmer, Leah Findlater, Jordan Boyd-Graber, and Jeffrey Heer. Computer-Assisted Content Analysis: Topic Models for Exploring Multiple Subjective Interpretations. NIPS Workshop on Human-Propelled Machine Learning, 2014. [pdf]
  • Forough Poursabzi-Sangdeh, and Ananth Kalyanaraman. On clustering heterogeneous networks. In SIAM Workshop on Network Science, 2013. [pdf]