Selected Publications

Papers are listed in reverse chronological order.

• M. Sahami and C. Piech. 2016. As CS Enrollments Grow, Are We Attracting Weaker Students?: A Statistical Analysis of Student Performance in Introductory Programming Courses Over Time. In Proceedings of the 47th ACM Technical Symposium on Computer Science Education (SIGCSE), 2016.
• M. Sahami, S. Roach, E. Cuadros-Vargas, and D. Reed. 2012. Computing Curricula 2013: Computer Science: Update on the Strawman Report from the ACM/IEEE-CS Task Force. In Proceedings of the 43rd ACM Technical Symposium on Computer Science Education (SIGCSE), 2012.
• C. Piech, M. Sahami, D. Koller, S. Cooper, and P. Blikstein. 2012. Modeling How Students Learn to Program. In Proceedings of the 43rd ACM Technical Symposium on Computer Science Education (SIGCSE), 2012.
• S. Roach, M. Sahami, and R. LeBlanc. 2012. Special Session -- The CS2013 Computer Science Curriculum Guidelines Project. In Proceedings of the 42nd ASEE/IEEE Frontiers in Education Conference (FIE), 2012.
• M Sahami. 2012. The 'Big Tent' of Computer Science: Curricula for the Coming Decade. Journal of Computing Sciences in Colleges, Volume 27, No. 4, April 2012.
• M. Sahami, M. Guzdial, A. McGettrick, and S. Roach. 2011. Setting the Stage for Computing Curricula 2013: Computer Science - Report from the ACM/IEEE-CS Joint Task Force. In Proceedings of the 42nd ACM Technical Symposium on Computer Science Education (SIGCSE), 2011.
• M. Sahami, M. desJardins, Z. Dodds, and T. Neller. 2011. Educational Advances in Artificial Intelligence. In Proceedings of the 42nd ACM Technical Symposium on Computer Science Education (SIGCSE), 2011.
• M. Sahami. 2011. A Course on Probability Theory for Computer Scientists. In Proceedings of the 42nd ACM Technical Symposium on Computer Science Education (SIGCSE), 2011.
• M. Sahami, S. Roach, and R. LeBlanc. 2011. Special Session - The CS2013 Computer Science Curriculum Guidelines Project. In Proceedings of the 41st ASEE/IEEE Frontiers in Education Conference (FIE), 2011.
• M. Sahami, A. Aiken, and J. Zelenski. 2010. Expanding the Frontiers of Computer Science: Designing a Curriculum to Reflect a Diverse Field. In Proceedings of the 41st ACM Technical Symposium on Computer Science Education (SIGCSE 2010). 2010.

• N. Parlante, T. P. Murtagh, M. Sahami, O. L. Astrachan, D. Reed, C. A. Stone, B. Heeringa, and K. L. Reid. 2009. Nifty assignments. In Proceedings of the 40th ACM Technical Symposium on Computer Science Education (SIGCSE 2009). 2009. pp. 483-484.

• M. Patel, R. Gossweiler, M. Sahami, J. Blackburn, D. Brown, and A. Knight. 2008. Google TV search: dual-wielding search and discovery in a large-scale product. In Proceedings of the First International Conference on Designing Interactive User Experiences for TV and Video (UXTV 2008), 2008, pp. 95-104.

• R. Bekkerman, M. Sahami, and E. Learned-Miller. 2006. Combinatorial Markov Random Fields. In Proceedings of 17th European Conference on Machine Learning (ECML), 2006.

• M. Sahami and T. D. Heilman. 2006. A Web-based Kernel Function for Measuring the Similarity of Short Text Snippets. In Proceedings of the 15th International World Wide Web Conference (WWW), 2006.

• R. Bekkerman and M. Sahami. 2006. Semi-supervised Clustering using Combinatorial MRFs. In Proceedings of ICML Workshop on Learning in Structured Output Spaces, 2006.

• M. Sahami. 2006. Mining the Web to Determine Similarity Between Words, Objects, and Communities. In Proceedings of the 19th International FLAIRS Conference (FLAIRS), 2006. (Invited paper)

• M. Sahami. 2006. Scaling Computer Science Education to Education on Scaling in Computer Science. White Paper presented at Workshop on Integrative Computing Education & Research (ICER): Preparing IT Graduates for 2010 and Beyond, 2006.

• M. Bilenko, S. Basu, and M. Sahami. 2005. Adaptive Product Normalization: Using Online Learning for Record Linkage in Comparison Shopping. In Proceedings of the 5th IEEE International Conference on Data Mining (ICDM), 2005.

• E. Spertus, M. Sahami, and O. Buyukkokten. 2005. Evaluating Similarity Measures: A Large-Scale Study in the Orkut Social Network. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2005.

• S. Baluja, M. Sahami, and H. Rowley. 2004. Efficient Face Orientation Discrimination. In Proceedings of the IEEE International Conference on Image Processing (ICIP), 2004.

• M. Sahami, V. Mittal, S. Baluja, and H. Rowley. 2004. The Happy Searcher: Challenges in Web Information Retrieval. In Proceedings of the Eighth Pacific Rim Conference on Artificial Intelligence (PRICAI), 2004. (Invited paper)

• L. Gravano, P. Ipeirotis, and M. Sahami. 2003. QProber: A System for Automatic Classification of Hidden-Web Databases. In ACM Transactions on Information Systems (TOIS). 21(1): 1-41.

• L. Gravano, P. Ipeirotis, and M. Sahami. 2002. Query- vs. Crawling-based Classification of Searchable Web Databases. In IEEE Data Engineering Bulletin, 25(1), March 2002.

• P. Ipeirotis, L. Gravano, and M. Sahami. 2001. Probe, Count, and Classify: Categorizing Hidden Web Databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), 2001.

• P. Ipeirotis, L. Gravano, and M. Sahami. 2001. PERSIVAL: Categorizing Hidden-Web Resources. In Proceedings of the 1st ACM/IEEE Joint Conference on Digital Libraries (JCDL), 2001.

• P. Ipeirotis, L. Gravano, and M. Sahami, 2000. Automatic Classification of Text Databases Through Query Probing. In Selected Papers from the ACM SIGMOD Workshop on the Web and Databases (WebDB), 2000.

• R. Kohavi and M. Sahami. 2000. Integrating Data Mining into Vertical Solutions. In SIGKDD Explorations 1(2): 55-58.

• M. Sahami. 1998. Using Machine Learning to Improve Information Access. PhD Thesis, Stanford University, Computer Science Department. STAN-CS-TR-98-1615.

• S. T. Dumais, J. Platt, D. Heckerman, and M. Sahami. 1998. Inductive learning algorithms and representations for text categorization. In CIKM-98: Proceedings of the Seventh International Conference on Information and Knowledge Management, 1998.

• M. Sahami, S. Dumais, D. Heckerman, and E. Horvitz. 1998. A Bayesian Approach to Filtering Junk E-Mail. In Learning for Text Categorization: Papers from the 1998 Workshop. AAAI Technical Report WS-98-05.

• M. Sahami, S. Yusufali, and M. Q. W. Baldonado. 1998. SONIA: A Service for Organizing Networked Information Autonomously. In Digital Libraries 98: Proceedings of the Third ACM Conference on Digital Libraries, pp. 200-209, New York, NY: ACM.

• M. Goldszmidt and M. Sahami. 1998. A Probabilistic Approach to Full-Text Document Clustering. Technical Report ITAD-433-MS-98-044, SRI International.

• D. Koller and M. Sahami. 1997. Hierarchically Classifying Documents Using Very Few Words. In ICML-97: Proceedings of the Fourteenth International Conference on Machine Learning, pp. 170-178, San Francisco, CA: Morgan Kaufmann.

• P. Langley, K. Pfleger, and M. Sahami. 1997. Lazy acquisition of place knowledge. In Artificial Intelligence Review. Special issue on "Lazy Learning", Vol. 11, pp. 315-342, Kluwer.

• M. Sahami. 1997. Applications of Machine Learning to Information Access. In AAAI-97: Proceedings of the Fourteenth National Conference on Artificial Intelligence, p. 816, Menlo Park, CA: AAAI Press. (AAAI-97 Doctoral Consortium).

• M. Sahami, S. Yusufali, and M. Q. W. Baldonado. 1997. Real-time Full-text Clustering of Networked Documents (Abstract). In AAAI-97: Proceedings of the Fourteenth National Conference on Artificial Intelligence, p. 845, Menlo Park, CA: AAAI Press.

• D. Koller and M. Sahami. 1996. Toward Optimal Feature Selection. In ICML-96: Proceedings of the Thirteenth International Conference on Machine Learning, pp. 284-292, San Francisco, CA: Morgan Kaufmann.

• M. Sahami, M. Hearst, and E. Saund. 1996. Applying the Multiple Cause Mixture Model to Text Categorization. In ICML-96: Proceedings of the Thirteenth International Conference on Machine Learning, pp. 435-443, San Francisco, CA: Morgan Kaufmann.

• R. Kohavi and M. Sahami. 1996. Error-Based and Entropy-Based Discretization of Continuous Features. In KDD-96: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 114-119, Menlo Park, CA: AAAI Press.

• M. Sahami. 1996. Learning Limited Dependence Bayesian Classifiers. In KDD-96: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 335-338, Menlo Park, CA: AAAI Press.

• M. Sahami. 1996. Generating Neural Networks Through the Induction of Threshold Logic Unit Trees. In Engineering Applications of Artificial Intelligence, Vol. 9, No. 2, pp. 129-136, Elsevier Science Ltd.

• J. Dougherty, R. Kohavi, and M. Sahami. 1995. Supervised and Unsupervised Discretization of Continuous Features. In ML-95: Proceedings of the Twelfth International Conference on Machine Learning, pp. 194-202, San Francisco, CA: Morgan Kaufmann.

• M. Sahami. 1995. Learning Classification Rules Using Lattices (Extended Abstract). In ECML-95: Proceedings of the Eighth European Conference on Machine Learning, pp. 343-346. Berlin, Germany: Springer-Verlag.

• M. Sahami. 1995. Generating Neural Networks Through the Induction of Threshold Logic Unit Trees (Extended Abstract). In ECML-95: Proceedings of the Eighth European Conference on Machine Learning, pp. 339-342. Berlin, Germany: Springer-Verlag.

• M. Sahami. 1995. Generating Neural Networks Through the Induction of Threshold Logic Unit Trees. In Proceedings of the First International IEEE Symposium on Intelligence in Neural and Biological Systems, pp. 108-115, Washington, DC: IEEE Press.
  This paper has more results and discussion than the ECML-95 paper above.

• M. Sahami. 1993. Learning Non-Linearly Separable Boolean Functions With Linear Threshold Unit Trees and Madaline-Style Networks. In AAAI-93: Proceedings of the Eleventh National Conference on Artificial Intelligence, pp. 335-341. Menlo Park, CA: AAAI Press.