William L. Hamilton

I am an Assistant Professor of Computer Science at McGill University and a Canada CIFAR AI Chair at the Mila AI Institute of Quebec. 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.

I am recruiting postdoctoral researchers and graduate students! See the open positions tab below details.

Recent news
  • August 2020 Our workshop on Deep Learning meets Differential Geometry will be co-located with NeurIPS 2020!
  • August 2020 A pre-publication draft of my book on Graph Representation Learning is now available! See the website for details.
  • March 2020 Excited to announce the next Graph Representation Learning Workshop will take place at ICML 2020! Details to follow in the coming weeks, as the workshop may be virtual-only given the evolving COVID-19 pandemic.
  • January 2020 Invited talk at the AAAI Deep Learning on Graphs Workshop. I discussed my lab's recent work on "Meta Learning and Logical Induction on Graphs." Check out the slides at here.

2020
Graph Representation Learning
William L. Hamilton
Morgan and Claypool Publishers. 2020.
pdf
Distilling Structured Knowledge for Text-Based Relational Reasoning
Jin Dong, Marc-Antoine Rondeau, and William L. Hamilton
Proceedings of EMNLP. 2020.
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.
Structure Aware Negative Sampling in Knowledge Graphs
Kian Ahrabian, Aarash Feizi, Yasmin Salehi, William L. Hamilton, and Avishek Joey Bose
Proceedings of EMNLP. 2020.
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)
Inductive Relation Prediction by Subgraph Reasoning
Komal K. Teru, Etienne Denis, and William L. Hamilton
Proceedings of ICML. 2020.
pdf
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)
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)
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.
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)
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)
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)
2019
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)
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)
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)
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 451 --- Fundamentals of Machine Learning

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.


COMP 551 --- Applied Machine Learning

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.


COMP 766 --- Graph Representation Learning

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.

PhD Students
Joey Bose
Personal Website
Research areas: Generative models, adversarial learning, graph representation learning
Devendra Sachan Singh
Google Scholar Profile
Research areas: Natural language processing, graph representation learning
Koustuv Sinha
Personal Website
Research areas: Natural language processing, logical reasoning, graph representation learning
Priyesh Vijayan
Personal Website
Research areas: Graph representation learning
Zichao Yan
Personal Website
Research areas: Graph representation learning, computational biology
Carlos Gonzalez Oliver
Personal Website
Research areas: Graph representation learning, computational biology
MSc Students
Dora Jambor
Personal Website
Research areas: Graph representation learning, natural language processing
Jiapeng (Paul) Wu
Personal Website
Research areas: Knowledge graphs, natural language processing
Alumni
Jin Dong
Now a Machine Learning Engineer at Microsoft
Komal Teru Kumar
Now a Research Scientist at Vanguard

Postdoctoral Research Positions

If you are interested in any of these positions, please send your full CV and a short (i.e., one page) cover letter detailing your research background and interests to wlh@cs.mcgill.ca with the subject line “POSTDOC APPLICANT”.


Graph Neural Networks, Logical Reasoning, and Knowledge Graphs

Supervisor: William L. Hamilton
Summary: I am looking for a postdoctoral researcher to investigate questions at the intersection of graph representation learning, logical reasoning, and knowledge graphs. The goal of the postdoc will be to advance fundamental methods that combine graph representation learning and logical reasoning (e.g., graph neural networks, Markov logic networks, and inductive logic programming). The primary application domain will be reasoning over knowledge graphs (e.g., biomedical knowledge graphs). Expected outputs of the postdoc include the development of new methods, as well as the release of open-source code and data to advance knowledge graph research.
Experience Required: PhD in Computer Science or a closely related field. Prior experience publishing on topics related to graph representation learning and/or knowledge graph analysis.
Start Date and Duration: January 2021 start-date and a two-year duration are preferred but negotiable.
Salary: Salary is negotiable, competitive, and conditioned on the applicant’s prior experience.

PhD Research Positions

In the coming application cycle (i.e., for PhDs to begin in fall 2021), I will be prioritizing PhD applicants with an interest in working on the research topics outlined below. All PhD research assistants receive competitive stipends and tuition support.

If you are interested in any of these positions, please submit an official application to McGill and also indicate your interest to work with me via the Mila Research Institute. I am not able to respond to direct email inquiries.


Graph Representation Learning Beyond Neural Message Passing

Supervisor: William L. Hamilton
Summary: I am looking for PhD students who want to develop the next generation of graph representation learning methods. Recent research on graph representation learning has largely focused on the so-called “message-passing” paradigm to develop graph neural networks (e.g., GraphSAGE, GCNs, GINs; see Chapter 5 of my book on the subject). However, these message-passing graph neural networks have serious limitations, both in terms of theoretical expressiveness and empirical power. The goal of this PhD research area is to work towards the next generation of methods for deep learning on graphs, which go beyond the current message-passing paradigm and lead to meaningful real-world improvements.
Experience Required: MSc in Computer Science or a closely related field. Priority will be given to applicants with prior research on graph representation learning (e.g., publications).

Graph Representation Learning, Signal Processing, and Logic

Supervisor: William L. Hamilton
Summary: I am looking for PhD students who want to improve our fundamental theoretical understanding of graph-based machine learning, while also developing new methods and algorithms. The goal of this research area is to uncover new relationships between the fields of graph representation learning, spectral graph theory, graph signal processing, and neuro-symbolic reasoning. This research area will prioritize theoretical developments and theoretically-driven algorithm design.
Experience Required: MSc in Computer Science or a closely related field. Priority will be given to applicants with prior research experience (e.g., publications) on graph representation learning, graph signal processing, or related areas.



MSc Research Positions

In the coming application cycle (i.e., for MSc students to begin in fall 2021), I will be prioritizing MSc applicants with an interest in working on the research topics outlined below. All MSc research assistants receive competitive stipends and tuition support.

If you are interested in any of these positions, please submit an official application to McGill and also indicate your interest to work with me via the Mila Research Institute. I am not able to respond to direct email inquiries.


Graph Representation Learning for Wireless Networks

Supervisor: William L. Hamilton (primary), with Reihaneh Rabbany and Georges Kaddoum
Summary: I am looking for MSc research students who are interested in applying graph representation learning methods (e.g., graph neural networks) for problems in wireless network design. The goal of this research area is to develop methods that can have immediate impact in real-world problems related to wireless network design and management. This project will involve close collaboration with subject-domain experts on wireless networks.
Experience Required:BSc in Computer Science or a closely related field. Coursework or other prior experience on machine learning and/or wireless systems.

Graph Representation Learning for Computational Drug Design

Supervisor: William L. Hamilton (primary), in collaboration with the LambdaZero Mila Initiative
Summary: I am looking for MSc research students who are interested in applying graph representation learning methods (e.g., graph neural networks) to problems related to computational drug design. This includes using graph neural networks to model molecule structures as well as applying graph learning techniques to biomedical knowledge graphs. Research in this area will prioritize real-world applications, including the development of treatments for various cancers, COVID-19, and other infectious diseases. This project will involve collaboration with ongoing initiatives at Mila and McGill related to computational drug design.
Experience Required: BSc in Computer Science or a closely related field. Coursework or other prior experience on machine learning and computational biology.



Stanford University Stanford NLP

Assistant Professor
McGill University, Mila
Office: McConnell 309

wlh@cs.mcgill.ca


Google Scholar


Many thanks to David Jurgens for the site template/inspiration