I am a third year doctoral student at UC Berkeley in computer science (specialization: artificial intelligence). I mainly work in the Computational Cognitive Science lab, and I'm advised by Tom Griffiths and Dan Klein. My research focuses on Bayesian modeling of psychological and linguistic processes. My current focus is on models of pedagogy: how can we model teaching and learning in order to best predict what examples and problems will result in the most learning? Models of pedagogy are also relevant for intelligent tutoring systems, and we hope to apply our techniques to improve these systems. Additionally, we look at how our models of pedagogy compare to human performance on teaching and learning tasks. My other work at Berkeley has primarily involved experimental and theoretical work on language evolution and iterated learning.
I completed my undergraduate and masters' work at Stanford in Symbolic Systems, and following graduation, I worked in Stanford's Natural Language Processing Group as a research programmer for Professor Chris Manning. My master's thesis involved creating computational models of students' learning in intelligent tutoring systems, and I was advised by Professor Dan Schwartz.
My other interests include feminist activism and improving access to science and math education for women and girls. I teach science once a week at Girls Inc., working with girls ages 6-14. I am originally from northern Minnesota and miss having a lake in my backyard - but the lack of snow in the Bay Area is a plus! Other interests include jigsaw puzzles, rollerblading and hiking, sci-fi books, and SET. Education: Ph.D. in progress in Computer Science at UC Berkeley, August 2008-present M.S., Symbolic Systems, Stanford University, June 2007. B.S., Symbolic Systems, Focus in Artificial Intelligence, Stanford University, June 2007, with Distinction. B.A., Feminist Studies, Focus in Women's Sexuality, Stanford University, June 2007, with Distinction. Relevant Work Experience: Research Programmer. Natural Language Processing Group, Computer Science, Stanford University, July 2007-August 2008. Publications: Rafferty, Anna N. and Thomas L. Griffiths. "Optimal language learning: The importance of starting representative." Proceedings of CogSci 2010. [PDF] Rafferty, Anna N., Thomas L. Griffiths, and Dan Klein. "Convergence Bounds for Language Evolution by Iterated Learning." Proceedings of CogSci 2009. [PDF] Ramage, Daniel, Anna N. Rafferty, and Christopher D. Manning. "Random Walks for Text Semantic Similarity." Proceedings of ACL-IJCNLP TextGraphs-4 Workshop 2009. [PDF] Rafferty, Anna N. and Christopher D. Manning. "Parsing Three German Treebanks: Lexicalized and Unlexicalized Baselines." Proceedings of Workshop on Parsing German, ACL-HLT 2008. [PDF] de Marneffe, Marie-Catherine, Anna N. Rafferty, and Christopher D. Manning. "Finding Contradictions in Text." Proceedings of ACL-HLT 2008. [PDF] Rafferty, Anna N. and Michael Yudelson. "Applying Learning Factors Analysis to Build Stereotypic Student Models." Proceedings of Artificial Intelligence in Education, 2007. Winner of Best Paper Award for the Young Researcher Track. [PDF] Marie-Catherine de Marneffe, Bill MacCartney, Trond Grenager, Daniel Cer, Anna Rafferty and Christopher D. Manning. "Learning to distinguish valid textual entailments." Proceedings of The Second PASCAL Challenges Workshop. 2006. [PDF] Slides: From presentation of above AIED 2007 paper. [PDF] From Symbolic Systems forum presentation of my master's research. [PDF] Master's Thesis: "Using FACT to Challenge Assumptions: Frequency, Accuracy, Choice, and Timing in Machine Learning." Symbolic Systems Department, Stanford University, June 2007. Advised by Professor Dan Schwartz, with second reader Professor Ken Koedinger. [RaffertyMSThesis.pdf] Resume [Resume] Contact: rafferty AT eecs DOT berkeley DOT edu
