Lecturer of Computer Science at Stanford University
Office: Gates Building, Room 193
Email: piech at cs.stanford.edu
Office Phone: (650) 736-6940
I was born and grew up in Nairobi, Kenya. When I was twelve I moved to Kuala Lumpur, Malaysia where I lived until I came to Stanford for university, liked it a lot and stayed. I love teaching and I'm into exploring our world (through both science and travelling). My research is in machine learning to understand human learning.
I was (well techincally still am) a PhD candidate advised by: Leo Guibas and Mehran Sahami and funded by a National Science Foundation Graduate Research Fellowship. Here is a copy of the latest draft of my dissertation and the slides from my defense.
C. Piech, J. Bassen, J. Huang, S. Ganguli, M. Sahami, L. Guibas, J. Sohl-Dickstein
Proceedings of the 29th Conference on Neural Information Processing Systems, Montreal, Canada, 2015
C. Piech, J. Huang, A. Nguyen, M. Phulsuksombati, M. Sahami, L. Guibas
Proceedings of the 43rd International Conference on Machine Learning, Lille, France 2015
C. Piech, M. Sahami, J. Huang, L. Guibas
Proceedings of the 2nd ACM Conference on Learning at Scale, Vancouver, Canada, 2015
A. Nguyen, C. Piech, J. Huang, L. Guibas
Proceedings of the 23rd international conference on World Wide Web, Seoul, Korea, 2014
J. Huang, C. Piech, A. Nguyen, L. Guibas
MOOC Shop, 11th International Conference on the Learning Sciences, Boulder, USA. 2014
C. Piech, J. Huang, Z. Chen, C. Do, A. Ng, D. Koller
Proceedings of the 6th International Conference on Educational Data Mining, Memphis, USA. 2013
R. Kizilcec, C. Piech, E. Schneider
Proceedings of the 3rd International Conference on Learning Analytics and Knowledge, Leuven, Belgium. 2013
C. Piech, M. Sahami, D. Koller, S. Cooper, P. Blikstein
Proceedings of the 43rd ACM Technical Symposium on Computer Science Education, Raleigh, USA. 2012
C. Piech, E. Roberts
Proceedings of the IFIP Conference on Informatics in a Globalised World of Education, Mombasa, Kenya. 2011
Code.org problem solving policy graph of learned policy for how to solve a single open ended programming assignment from over 1M users. Each node is a unique partial-solution (and node 0 is the correct answer)
CS106B: Programming Abstractions, Winter 2015.
CS221: Introduction to Artifcial Intelligence, Summer 2013.
CS106B: Programming Abstractions, Summer 2011.
CS106B: Programming Abstractions, Summer 2010.