I am an Adjunct Professor of Computer Science at McGill University and a Senior Quantitative Researcher at Citadel LLC.
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.
Note that I am no longer accepting new students, as I have shifted away from my full-time role at McGill University to an adjunct position.
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Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs
Dora Jambor, Komal Teru, Joelle Pineau, and William L. Hamilton Proceedings of EACL. 2021. pdf (arxiv) |
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Do Syntax Trees Help Pre-trained Transformers Extract Information?
Devendra Singh Sachan, Yuhao Zhang, Peng Qi, and William Hamilton Proceedings of EACL. 2021. pdf (arxiv) |
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Neural Representation and Generation for RNA Secondary Structures
Zichao Yan, William L. Hamilton, and Mathieu Blanchette Proceedings of ICLR. 2021. pdf (openreview) |
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A Universal Representation Transformer Layer for Few-Shot Image Classification
Lu Liu, William L. Hamilton, Guodong Long, Jing Jiang, and Hugo Larochelle Proceedings of ICLR. 2021. pdf (openreview) |
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Adversarial Example Games
Avishek Joey Bose, Gauthier Gidel, Hugo Berard, Andre Cianflone, Pascal Vincent, Simon Lacoste-Julien, and William L. Hamilton Proceedings of NeurIPS. 2020. pdf (arxiv) |
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Learning Dynamic Belief Graphs to Generalize on Text-Based Games
Ashutosh Adhikari, Xingdi Yuan, Marc-Alexandre Côté, Mikuláš Zelinka, Marc-Antoine Rondeau, Romain Laroche, Pascal Poupart, Jian Tang, Adam Trischler, and William L. Hamilton Proceedings of NeurIPS. 2020. pdf (arxiv) |
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Distilling Structured Knowledge for Text-Based Relational Reasoning
Jin Dong, Marc-Antoine Rondeau, and William L. Hamilton Proceedings of EMNLP. 2020. |
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TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion
Jiapeng Wu, Meng Cao, Jackie Chi Kit Cheung, and William L. Hamilton Proceedings of EMNLP. 2020. |
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Structure Aware Negative Sampling in Knowledge Graphs
Kian Ahrabian, Aarash Feizi, Yasmin Salehi, William L. Hamilton, and Avishek Joey Bose Proceedings of EMNLP. 2020. |
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Latent Variable Modelling with Hyperbolic Normalizing Flows
Avishek Joey Bose, Ariella Smofsky, Renjie Liao, Prakash Panangaden, and William L. Hamilton Proceedings of ICML. 2020. pdf (arxiv) |
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Inductive Relation Prediction by Subgraph Reasoning
Komal K. Teru, Etienne Denis, and William L. Hamilton Proceedings of ICML. 2020. |
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Augmented base pairing networks encode RNA-small molecule binding preferences
Carlos G. Oliver, Roman Gendron, Nicolas Moitessier, Vincent Mallet, Vladimir Reinharz, William L. Hamilton, Nicolas Moitessier, and Jérôme Waldispühl Nucleic Acids Research (NAR). 2020. pdf (biorxiv) |
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Learning an Unreferenced Metric for Online Dialogue Evaluation
Koustuv Sinha, Prasanna Parthasarathi, Jasmine Wang, Ryan Lowe, William L. Hamilton, and Joelle Pineau Proceedings of ACL. 2020. pdf (arxiv) |
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Exploring the Limits of Simple Learners in Knowledge Distillation for Document Classification
Ashutosh Adhikari, Achyudh Ram, Raphael Tang, William L. Hamilton, and Jimmy Lin ACL Workshop on Representation Learning for NLP. 2020. |
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Exploring Structural Inductive Biases in Emergent Communication
Agnieszka Słowik, Abhinav Gupta, William L. Hamilton, Mateja Jamnik, Sean B. Holden, and Christopher Pal AAMAS Workshop on Adaptive and Learning Agents. 2020. pdf (arxiv) |
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Graph Neural Representational Learning of RNA Secondary Structures for Predicting RNA-Protein Interactions
Zichao Yan, William L. Hamilton, and Mathieu Blanchette Proceedings of ISMB. 2020. pdf (bioarxiv) |
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Towards Graph Representation Learning in Emergent Communication
Agnieszka Słowik, Abhinav Gupta, William L. Hamilton, Mateja Jamnik, and Sean B. Holden AAAI Workshop on Reinforcement Learning in Games. 2020. pdf (arxiv) |
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Meta-Graph: Few shot Link Prediction via Meta-Learning
Avishek Joey Bose, Ankit Jain, Piero Molino, and William L. Hamilton NeurIPS Graph Representation Learning Workshop 2019. pdf (arxiv) |
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CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text
Koustuv Sinha, Shagun Sodhani, Jin Dong, Joelle Pineau, and William L. Hamilton Proceedings of EMNLP. 2019. pdf (arxiv) |
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Efficient Graph Generation with Graph Recurrent Attention Networks
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, ,William L. Hamilton, David Duvenaud, Raquel Urtasun, and Richard S Zemel Proceedings of NeurIPS. 2019. pdf (arxiv) |
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Neural Transfer Learning for Cry-based Diagnosis of Perinatal Asphyxia
Charles C. Onu, Jonathan Lebensold, William L. Hamilton, and Doina Precup Interspeech. 2019. |
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Compositional Fairness Constraints for Graph Embeddings
Avishek Joey Bose and William L. Hamilton Proceedings of ICML. 2019. pdf (arxiv) |
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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. |
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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. |
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Tutorial on Graph Representation Learning
William L. Hamilton and Jian Tang AAAI Tutorial Forum. 2019. slides (zip) |
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Deep Graph Infomax
Petar Velickovic, William Fedus, William L. Hamilton, Pietro Lio, Yoshua Bengio, and R Devon Hjelm. Proceedings of ICLR. 2019. pdf (arxiv) |
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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) |
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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) |
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Embedding Logical Queries on Knowledge Graphs
William L. Hamilton, Marinka Zitnik, Payal Bajaj, Dan Jurafsky, Jure Leskovec. Proceedings of NeurIPS. 2018. pdf (arxiv) |
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Compositional Fairness Constraints for Graph Embeddings
Joey Bose and William L. Hamilton. NeurIPS Relational Representation Learning Workshop. 2018. |
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Compositional Language Understanding with Text-based Relational Reasoning
Koustuv Sinha, Shagun Sodhani, William L. Hamilton, and Joelle Pineau. NeurIPS Relational Representation Learning Workshop. 2018. |
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Deep Graph Infomax
Petar Velickovic, William Fedus, William L. Hamilton, Pietro Lio, Yoshua Bengio, and R Devon Hjelm. NeurIPS Relational Representation Learning Workshop. 2018. |
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GraphRNN: A Deep Generative Model for Graphs
Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, Jure Leskovec. Proceedings of ICML. 2018. pdf (arxiv) |
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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) |
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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) |
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Representation Learning on Graphs: Methods and Applications
William L. Hamilton, Rex Ying, Jure Leskovec. IEEE Data Engineering Bulletin. 2017. |
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Inductive Representation Learning on Large Graphs
William L. Hamilton*, Rex Ying*, Jure Leskovec. Proceedings of NeurIPS. 2017. pdf project website (code+data) |
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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. |
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Loyalty in Online Communities
William L. Hamilton*, Justine Zhang*, Cristian Danescu-Niculescu-Mizil, Jure Leskovec, Dan Jurafsky. Proceedings of ICWSM (short paper). 2017. |
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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. |
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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) |
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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) |
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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. |
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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) |
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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. |
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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 |
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Methods of Moments for Learning Stochastic Languages: Unified Presentation and Empirical Comparison
Borja Balle*, William L. Hamilton*, Joelle Pineau   Proceedings of ICML. 2014. |
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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 |
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Modelling Sparse Dynamical Systems with Compressed Predictive State Representations
William L. Hamilton, Mahdi Milani Fard, Joelle Pineau. Proceedings of ICML. 2013. pdf code |
In the winter semester of 2021, I will teach a course on the Fundamentals of Machine Learning at McGill. Check out the course website for more information.
I teach a course on Applied Machine Learning at McGill. I taught the course in the winter semester of 2019 as well as in the fall of 2019. Check out the course website for more information.
In winter 2020, I taught a graduate-level course on Graph Representation Learning. Check out the course website for more information.
William (Will) Hamilton is an Assistant Professor in the School of Computer Science at McGill University, a Canada CIFAR AI Chair, 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.
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Joey Bose
Personal Website Research areas: Generative models, adversarial learning, graph representation learning |
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Devendra Sachan Singh
Google Scholar Profile Research areas: Natural language processing, graph representation learning |
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Koustuv Sinha
Personal Website Research areas: Natural language processing, logical reasoning, graph representation learning |
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Carlos Gonzalez Oliver
Personal Website Research areas: Graph representation learning, computational biology |
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Dora Jambor
Personal Website Research areas: Graph representation learning, natural language processing |
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Jin Dong
Now a Machine Learning Engineer at Microsoft |
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Komal Teru Kumar
Now a Research Scientist at Vanguard |
Many thanks to David Jurgens for the site template/inspiration