William L. Hamilton

I use computational models to analyze large social systems and develop machine learning approaches to learn over complex social and information networks.

Recent news
  • December 2017: Tutorial on "Representation Learning on Graphs and Networks" accepted at WWW 2018. See you in Lyon!
  • December 2017: One main-track poster presentation at NIPS 2017 and an invited talk at the NIPS MLTrain Workshop. Both on the GraphSAGE framework.
  • November 2017: I am on the job market!
  • September 2017: Review paper on "Representation Learning on Graphs" published in the IEEE Data Engineering Bulletin.
  • August 2017: Invited talks on using word embeddings to model language change at the University of Edinburgh, Cambridge University, and the Alan Turing Institute.
2017
Representation Learning on Graphs: Methods and Applications
William L. Hamilton, Rex Ying, Jure Leskovec.                  
IEEE Data Engineering Bulletin. 2017.
pdf
Inductive Representation Learning on Large Graphs
William L. Hamilton*, Rex Ying*, Jure Leskovec.                
Proceedings of NIPS. 2017 (to appear).
pdf     project website (code+data)
Community Identity and User Engagement in a Multi-Community Landscape
Justine Zhang*, William L. Hamilton*, Cristian Danescu-Niculescu-Mizil, Jure Leskovec, Dan Jurafsky.  
Proceedings of ICWSM. 2017.
pdf
Loyalty in Online Communities
William L. Hamilton*, Justine Zhang*, Cristian Danescu-Niculescu-Mizil, Jure Leskovec, Dan Jurafsky.
Proceedings of ICWSM (short paper). 2017.
pdf
Language from Police Body Camera Footage Shows Racial Disparities in Officer Respect
Rob Voigt, Nicholas P. Camp, Vinod Prabhakaran, William L. Hamilton, Rebecca C. Hetey, Camilla M. Griffiths, David Jurgens, Dan Jurafsky, and Jennifer L. Eberhardt.
Proceedings of the National Academy of Science (PNAS). 2017.
pdf
2016
Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora
William L. Hamilton, Kevin Clark, Jure Leskovec, Dan Jurafsky.
Proceedings of EMNLP. 2016.
pdf     project website (code+data)
Cultural Shift or Linguistic Drift? Comparing Two Computational Models of Semantic Change
William L. Hamilton, Jure Leskovec, Dan Jurafsky.
Proceedings of EMNLP. 2016.
pdf     project website (code+data)
Learning Linguistic Descriptors of User Roles in Online Communities
Alex Wang, William L. Hamilton, Jure Leskovec.
EMNLP Workshop on Computational Social Science (NLP+CSS). 2016.
pdf
Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change
William L. Hamilton, Jure Leskovec, Dan Jurafsky.
Proceedings of ACL. 2016.
pdf     project website (code+data)
Predicting the Rise and Fall of Scientific Topics from Trends in their Rhetorical Framing
Vinodkumar Prabhakaran, William L. Hamilton, Dan McFarland, Dan Jurafsky.
Proceedings of ACL. 2016.
pdf
2014
Compressed Predictive State Representation: An Efficient Moment-Method for Sequence Prediction and Sequential Decision Making
William L. Hamilton
MSc Thesis. McGill University.
Canadian AI Association (CAIAC) 2014 MSc Thesis Award
pdf
Methods of Moments for Learning Stochastic Languages: Unified Presentation and Empirical Comparison
Borja Balle*, William L. Hamilton*, Joelle Pineau  
Proceedings of ICML. 2014.
pdf
Efficient Learning and Planning with Compressed Predictive States  
William L. Hamilton, Mahdi Milani Fard, Joelle Pineau.
Journal of Machine Learning Research (JMLR). 2014.
pdf  code
2013
Modelling Sparse Dynamical Systems with Compressed Predictive State Representations
William L. Hamilton, Mahdi Milani Fard, Joelle Pineau.
Proceedings of ICML. 2013.
pdf  code

William (Will) Hamilton is a PhD Candidate in computer science at Stanford University, working jointly in the NLP and SNAP groups, which he joined in 2014. His interests lie at the intersection of machine learning, network science, natural language processing, and computational social science. He is co-advised by Dan Jurafsky and Jure Leskovec. Will's PhD is supported by the SAP Stanford Graduate Fellowship and a Canadian NSERC PGS-D Grant.

Prior to coming to Stanford, Will completed a BSc and MSc at McGill University, where he studied computer science (with an emphasis on theoretical machine learning) in the Reasoning and Learning Lab under the supervision of Joelle Pineau. Will was awarded the Canadian AI MSc Thesis Award for his work at McGill and received an honorable mention for the CRA Undergraduate Researcher of the Year.

During the summers of 2013 and 2014, Will interned at Amazon as a software development engineer and research scientist, where he designed and implemented new time-series prediction algorithms.


Stanford University Stanford NLP SNAP

PhD Candidate
Computer Science, Stanford University

wleif(at)stanford.edu

Gates 450
Stanford CA 94305


CV
Google Scholar


Many thanks to David Jurgens for the site template/inspiration