William L. Hamilton

I am an Assistant Professor of Computer Science at McGill University and a Canada CIFAR Chair in AI. I develop machine learning models that can reason about our complex, interconnected world.

Broadly, my research interests lie at the intersection of machine learning, network science, and natural language processing, with a current emphasis on the fast-growing subjects of graph representation learning and graph neural networks.

Recent news
  • June 2019 Our paper on using neural transfer learning to detect perinatal asphyxia was accepted at Interspeech 2019!
  • June 2019 Invited talk on "Graph Neural Networks and Graph Isomorphism" at the ICML 2019 Workshop on Learning and Reasoning with Graph-structured Representations.
  • May 2019 Our paper on "Compositional Fairness Constraints for Graph Embeddings" was accepted at ICML 2019.
  • April 2019 I was awarded a Canada CIFAR Chair in AI!
  • January 2019: AAAI 2019 Tutorial on Graph Representation Learning was a great success! Slides are available here.
2019
Neural Transfer Learning for Cry-based Diagnosis of Perinatal Asphyxia
Charles C. Onu, Jonathan Lebensold, William L. Hamilton, and Doina Precup
Interspeech. 2019.
Compositional Fairness Constraints for Graph Embeddings
Avishek Joey Bose and William L. Hamilton
Proceedings of ICML. 2019.
pdf (arxiv)
Discrete Off-policy Policy Gradients Using Continuous Relaxations
Andre Cianflone, Zafarali Ahmed, Riashat Islam, Avishek Joey Bose, and William L. Hamilton
Proceedings of RLDM. 2019.
Neural Transfer Learning for Cry-based Diagnosis of Perinatal Asphyxia
Charles C. Onu, Jonathan Lebensold, William L. Hamilton, and Doina Precup
ICLR AI for Social Good Workshop. 2019.
Tutorial on Graph Representation Learning
William L. Hamilton and Jian Tang
AAAI Tutorial Forum. 2019.
slides (zip)
Deep Graph Infomax
Petar Velickovic, William Fedus, William L. Hamilton, Pietro Lio, Yoshua Bengio, and R Devon Hjelm.  
Proceedings of ICLR. 2019.
pdf (arxiv)
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks
Christoper Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe.  
Proceedings of AAAI. 2019.
pdf (arxiv)
2018
Hierarchical Graph Representation Learning with Differentiable Pooling
Jiaxuan You, Rex Ying, Christopher Morris, Xiang Ren, William L. Hamilton, Jure Leskovec.  
Proceedings of NeurIPS. 2018.
pdf (arxiv)
Embedding Logical Queries on Knowledge Graphs
William L. Hamilton, Marinka Zitnik, Payal Bajaj, Dan Jurafsky, Jure Leskovec.  
Proceedings of NeurIPS. 2018.
pdf (arxiv)
Compositional Fairness Constraints for Graph Embeddings
Joey Bose and William L. Hamilton.
NeurIPS Relational Representation Learning Workshop. 2018.
Compositional Language Understanding with Text-based Relational Reasoning
Koustuv Sinha, Shagun Sodhani, William L. Hamilton, and Joelle Pineau.  
NeurIPS Relational Representation Learning Workshop. 2018.
Deep Graph Infomax
Petar Velickovic, William Fedus, William L. Hamilton, Pietro Lio, Yoshua Bengio, and R Devon Hjelm.  
NeurIPS Relational Representation Learning Workshop. 2018.
GraphRNN: A Deep Generative Model for Graphs
Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, Jure Leskovec.  
Proceedings of ICML. 2018.
pdf (arxiv)
Graph Convolutional Neural Networks for Web-scale Recommender Systems
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai,
William L. Hamilton, Jure Leskovec.  
Proceedings of KDD. 2018.
pdf (arxiv)
Community Interaction and Conflict on the Web
Srijan Kumar, William L. Hamilton, Jure Leskovec, Dan Jurafsky.      
Proceedings of The Web Conference (WWW). 2018.
pdf (arxiv)   project website (code + data)
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 NeurIPS. 2017.
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
COMP 551 --- Applied Machine Learning

I am teaching a course on Applied Machine Learning at McGill. I taught the course in the winter semester of 2019 and will teach it again in the fall of 2019, Check out the course website for more information.


COMP 766 --- Graph Representation Learning

In winter 2020, I will teach a graduate-level course on Graph Representation Learning. Check out the course website for more information.

William (Will) Hamilton is an Assistant Professor of Computer Science in the School of Computer Science at McGill University, a Canada CIFAR Chair in AI, and a member of the Mila AI Institute of Quebec. Will completed his PhD in Computer Science at Stanford University in 2018. He received the 2018 Arthur Samuel Thesis Award for best Computer Science PhD Thesis from Stanford University, the 2014 CAIAC MSc Thesis Award for best AI-themed MSc thesis in Canada, as well as an honorable mention for the 2013 ACM Undergraduate Researcher of the Year. His interests lie at the intersection of machine learning, network science, and natural language processing, with a current emphasis on the fast-growing subject of graph representation learning. Will was the SAP Stanford Graduate Fellow (2014-2018), received the Cozzarelli Best Paper Award from the Proceedings of the National Academy of Sciences (PNAS) in 2017, and his work has been featured in numerous media outlets, including Wired, The New York Times, and The BBC.


Stanford University Stanford NLP

Assistant Professor
McGill University, Mila
Office: McConnell 309

wlh@cs.mcgill.ca


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Many thanks to David Jurgens for the site template/inspiration