Bio


Mehran Sahami is a Professor and Associate Chair for Education in the Computer Science department at Stanford University. He is also the Robert and Ruth Halperin University Fellow in Undergraduate Education. Prior to joining the Stanford faculty, he was a Senior Research Scientist at Google. His research interests include computer science education, artificial intelligence, and web search. He is co-chair of the ACM/IEEE-CS joint task force on Computer Science Curricula 2013, which is responsible for creating curricular guidelines for college programs in Computer Science at an international level.

Academic Appointments


Administrative Appointments


  • Associate Chair for Education, Computer Science Department (2007 - Present)

Boards, Advisory Committees, Professional Organizations


  • Advisory Board, Code.org (2013 - Present)
  • Co-Chair, ACM Education Board (2014 - Present)

Professional Education


  • PhD, Stanford University, Computer Science (1999)

2017-18 Courses


Stanford Advisees


All Publications


  • Programming Pluralism: Using Learning Analytics to Detect Patterns in the Learning of Computer Programming JOURNAL OF THE LEARNING SCIENCES Blikstein, P., Worsley, M., Piech, C., Sahami, M., Cooper, S., Koller, D. 2014; 23 (4): 561-599
  • Reflections on Stanford's MOOCs COMMUNICATIONS OF THE ACM Cooper, S., Sahami, M. 2013; 56 (2): 28-30
  • Special Session: The CS2013 Computer Science Curriculum Guidelines Project 43rd Annual Frontiers in Education Conference (FIE) Roach, S., Sahami, M., LeBlanc, R., Seker, R. IEEE. 2013
  • The 'Big Tent' of Computer Science: Curricula for the Coming Decade. Journal of Computing Sciences in Colleges Sahami, M. 2012; 27 (4)
  • Computer Science: Update on the Strawman Report from the ACM/IEEE-CS Task Force. Sahami, M., Roach, S., Cuadros-Vargas, E., Reed, D. 2012
  • Special Session -- The CS2013 Computer Science Curriculum Guidelines Project. Roach, S., Sahami, M., LeBlanc, R. 2012
  • EAAI-10: The First Symposium on Educational Advances in Artificial Intelligence AI MAGAZINE desJardins, M., Sahami, M., Wagstaff, K. 2011; 32 (1): 91-92
  • Computer Science Curriculum 2013: Reviewing the Strawman Report from the ACM/IEEE-CS Task Force 43rd ACM Technical Symposium on Computer Science Education (SIGCSE 2012) Sahami, M., Roach, S., Cuadros-Vargas, E., Reed, D. ASSOC COMPUTING MACHINERY. 2011: 3–4
  • Setting the Stage for Computing Curricula 2013: Computer Science - Report from the ACM/IEEE-CS Joint Task Force 42nd ACM Technical Symposium on Computer Science Education Sahami, M., Guzdial, M., McGettrick, A., Roach, S. ASSOC COMPUTING MACHINERY. 2011: 161–162
  • Special Session - The CS2013 Computer Science Curriculum Guidelines Project 41st Annual Frontiers in Education Conference (FIE) Sahami, M., Roach, S., LeBlanc, R. IEEE. 2011
  • Educational Advances in Artificial Intelligence 42nd ACM Technical Symposium on Computer Science Education Sahami, M., desJardins, M., Dodds, Z., Neller, T. ASSOC COMPUTING MACHINERY. 2011: 81–82
  • Modeling How Students Learn to Program 43rd ACM Technical Symposium on Computer Science Education (SIGCSE 2012) Piech, C., Sahami, M., Koller, D., Cooper, S., Blikstein, P. ASSOC COMPUTING MACHINERY. 2011: 153–158
  • A Course on Probability Theory for Computer Scientists 42nd ACM Technical Symposium on Computer Science Education Sahami, M. ASSOC COMPUTING MACHINERY. 2011: 263–268
  • Expanding the Frontiers of Computer Science: Designing a Curriculum to Reflect a Diverse Field 41st ACM Technical Symposium on Computer Science Education Sahami, M., Aiken, A., Zelenski, J. ASSOC COMPUTING MACHINERY. 2010: 47–51
  • Nifty assignments. Parlante, N., Murtagh, T., P., Sahami, M., Astrachan, O., L., Reed, D., Stone, C., A. 2009
  • Reports of the AAAI 2008 Spring Symposia AI MAGAZINE Balduccini, M., Baral, C., Brodaric, B., Colton, S., Fox, P., Gutelius, D., Hinkelmann, K., Horswill, I., Huberman, B., Hudlicka, E., Lerman, K., Lisetti, C., McGuinness, D., Maher, M. L., Musen, M. A., Sahami, M., Sleeman, D., Thoenssen, B., Velasquez, J., Ventura, D. 2008; 29 (3): 107-115
  • Google TV search: dual-wielding search and discovery in a large-scale product. Patel, M., Gossweiler, R., Sahami, M., Blackburn, J., Brown, D., Knight, A. 2008
  • A Web-based Kernel Function for Measuring the Similarity of Short Text Snippets. Sahami, M., Heilman, T., D. 2006
  • Combinatorial Markov Random Fields. Bekkerman, R., Sahami, M., Learned-Miller, E. 2006
  • Semi-supervised Clustering using Combinatorial MRFs. Bekkerman, R., Sahami, M. 2006
  • Mining the Web to Determine Similarity Between Words, Objects, and Communities. Sahami, M. 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 Sahami, M. 2006
  • Evaluating Similarity Measures: A Large-Scale Study in the Orkut Social Network. Spertus, E., Sahami, M., Buyukkokten, O. 2005
  • Adaptive Product Normalization: Using Online Learning for Record Linkage in Comparison Shopping. Bilenko, M., Basu, S., Sahami, M. 2005
  • Efficient Face Orientation Discrimination. Baluja, S., Sahami, M., Rowley, H. 2004
  • The Happy Searcher: Challenges in Web Information Retrieval. Sahami, M., Mittal, V., Baluja, S., Rowley, H. 2004
  • QProber: A system for automatic classification of hidden-Web databases ACM TRANSACTIONS ON INFORMATION SYSTEMS Gravano, L., Ipeirotis, P. G., Sahami, M. 2003; 21 (1): 1-41
  • Query- vs. Crawling-based Classification of Searchable Web Databases. In IEEE Data Engineering Bulletin Gravano, L., Ipeirotis, P., Sahami, M. 2002; 1 (25)
  • Probe, count, and classify: Categorizing hidden-web databases ACM SIGMOD International Conference on Management of Data Ipeirotis, P. G., Gravano, L., Sahami, M. ASSOC COMPUTING MACHINERY. 2001: 67–78
  • PERSIVAL: Categorizing Hidden-Web Resources. Ipeirotis, P., Gravano, L., Sahami, M. 2001
  • Automatic Classification of Text Databases Through Query Probing. Ipeirotis, P., Gravano, L., Sahami, M. 2000
  • Integrating Data Mining into Vertical Solutions. In SIGKDD Explorations Kohavi, R., Sahami, M. 2000; 2 (1): 55-58
  • A Bayesian Approach to Filtering Junk E-Mail. In Learning for Text Categorization: Papers from the 1998 Workshop. AAAI Technical Report WS-98-05 Sahami, M., Dumais, S., Heckerman, D., Horvitz, E. 1998
  • SONIA: A Service for Organizing Networked Information Autonomously. Sahami, M., Yusufali, S., Baldonado, M., W.Q. 1998
  • Inductive learning algorithms and representations for text categorization. Dumais, S., T., Platt, J., Heckerman, D., Sahami, M. 1998
  • A Probabilistic Approach to Full-Text Document Clustering. Technical Report ITAD-433-MS-98-044, SRI International. Goldszmidt, M., Sahami, M. 1998
  • Lazy acquisition of place knowledge ARTIFICIAL INTELLIGENCE REVIEW Langley, P., Pfleger, K., Sahami, M. 1997; 11 (1-5): 315-342
  • Hierarchically Classifying Documents Using Very Few Words. Koller, D., Sahami, M. 1997
  • Real-time Full-text Clustering of Networked Documents (Abstract). Sahami, M., Yusufali, S., Baldonado, M., W.Q. 1997
  • Applications of Machine Learning to Information Access. Sahami, M. 1997
  • Generating neural networks through the induction of threshold logic unit trees ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE Sahami, M. 1996; 9 (2): 129-136
  • Applying the Multiple Cause Mixture Model to Text Categorization. Sahami, M., Hearst, M., Saund, E. 1996
  • Toward Optimal Feature Selection. Koller, D., Sahami, M. 1996
  • Learning Limited Dependence Bayesian Classifiers. Sahami, M. 1996
  • Error-Based and Entropy-Based Discretization of Continuous Features. Kohavi, R., Sahami, M. 1996
  • Generating Neural Networks Through the Induction of Threshold Logic Unit Trees (Extended Abstract). Sahami, M. 1995
  • Supervised and Unsupervised Discretization of Continuous Features. Dougherty, J., Kohavi, R., Sahami, M. 1995
  • Learning classification rules using lattices 8th European Conference on Machine Learning Sahami, M. SPRINGER-VERLAG BERLIN. 1995: 343–346
  • Generating neural networks through the induction of threshold logic unit trees 8th European Conference on Machine Learning Sahami, M. SPRINGER-VERLAG BERLIN. 1995: 339–342
  • LEARNING NONLINEARLY SEPARABLE BOOLEAN FUNCTIONS WITH LINEAR THRESHOLD UNIT TREES AND MADALINE-STYLE NETWORKS 11th National Conference on Artificial Intelligence (AAAI-93) Sahami, M. M I T PRESS. 1993: 335–341