Shenyang Huang

Bio

I am a Ph.D. student at Mila and McGill University , supervised by Professor Reihaneh Rabbany and Professor Guillaume Rabusseau . Previously I obtained an Honours in Computer Science from McGill University in 2019. My current research focus on temporal graph learning, specifically link prediction and anomaly detection on dynamic graphs. I also have a broad interest in graph transformers, machine learning for molecules, graph neural networks, disease modelling and continual learning.

News!

2023

[2023/09] Excited to be organizing the Temporal Graph Learning Workshop @ NeurIPS 2023 along with many great co-organizers! The submission portal is now open on OpenReview and call for paper on the workshop website. The submission deadline is Oct. 3rd, 2023, AoE and hope to see your work there!

[2023/08] The pre-print for our work Towards Temporal Edge Regression: A Case Study on Agriculture Trade Between Nations is now available. Thanks to my amazing collaborators at Mila and University of Cambridge!

[2023/08] Our work "GPS++: Reviving the Art of Message Passing for Molecular Property Prediction" is accepted at TMLR. Thanks to all my amazing collaborators at Valence, graphcore and Mila.

[2023/07] Excited to see our Graphium Library being released : a powerful and flexible python library for training molecular GNNs at scale. See Dominique Beaini's excellent blog post for more details on the library.

[2023/07] The pre-print for our work "Temporal Graph Benchmark for Machine Learning on Temporal Graphs" is now available. Check out our project on

and our website

. Thanks to all my amazing collaborators from Mila, McGill, Kumo.AI, Imperial College London, University of Montreal, Stanford and Oxford.

[2023/05] Travelling to PAKDD 2023 to present our accepted work Fast and Attributed Change Detection on Dynamic Graphs with Density of States . Thanks to my amazing co-authors Jacob Danovitch, Guillaume Rabusseau and Reihaneh Rabbany

[2023/02] Happy to annouce the weekly Temporal Graph Reading Group organized by Farimah Poursafaei, Julia Gastinger and me. Join us weekly at 11am EDT on Thursdays!

[2023/01] See our blog post providing an overview of trends and future directions in Temporal Graph Learning so far in 2023. Thanks to my amazing co-authors: Emanuele Rossi, Michael Galkin and Kellin Pelrine. Also thanks to Farimah Poursafaei for suggestions.

Publications

2023

Jiang, L., Zhang, C., Poursafaei, F., Huang, S., Towards Temporal Edge Regression: A Case Study on Agriculture Trade Between Nations (preprint)

Huang, S.*,, Poursafaei, F.*, Danovitch, J., Fey, M., Hu, W., Rossi, E., Leskovec, J., Bronstein, M., Rabusseau G. and Rabbany R. Temporal Graph Benchmark for Machine Learning on Temporal Graphs (preprint)

Huang, S.,, Danovitch, J., Rabusseau G., Rabbany R. Fast and Attributed Change Detection on Dynamic Graphs with Density of States (PAKDD 2023)

Masters, D., Dean, J. Klaser, K., Li, Z., Mander, S., Sanders, A., Helal, H., Beker, D., Fitzgibbon, A., Huang, S., Rampášek, L., Beaini, D. GPS++: Reviving the Art of Message Passing for Molecular Property Prediction (TMLR)

Huang, S., Coulombe, S., Hitti, Y., Rabbany, R., Rabusseau, G. Laplacian Change Point Detection for Single and Multi-view Dynamic Graphs (pre-print)

2022

Poursafaei, F.*, Huang, S.*, Pelrine, K., Rabbany, R. Towards Better Evaluation for Dynamic Link Prediction (NeurIPS 2022 Datasets and Benchmarks Track)

2021

Huang, S., Wang, K., Rabusseau, G., & Makhzani, A. Few Shot Image Generation via Implicit Autoencoding of Support Sets 5th Workshop on Meta-Learning at NeurIPS 2021

Huang, S., Rabusseau, G. & Rabbany, R. Scalable Change Point Detection for Dynamic Graphs 6th Outlier Detection and Description Workshop at KDD 2021

Huang, S., François-Lavet, V., & Rabusseau, G. Understanding Capacity Saturation in Incremental Learning. Canadian Conference on Artificial Intelligence 2021

Ding, X., Huang, S., Leung, A., Rabbany, R. Incorporating dynamic flight network in SEIR to model mobility between populations. Applied Network Science, Special issue on Epidemics Dynamics & Control on Networks

2020

Huang, S., Hitti, Y., Rabusseau, G. & Rabbany, R. Laplacian Change Point Detection for Dynamic Graphs. (KDD 2020)

Leung, A., Ding, X., Huang, S., Rabbany, R. Contact Graph Epidemic Modelling of COVID-19 for Transmission and Intervention Strategies.

Alletto, S., Huang, S., François-Lavet, V., Nakata, Y., & Rabusseau, G. RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning. AAAI 2020 Meta-Eval Workshop

2019

Huang, S., François-Lavet, V., & Rabusseau, G. Neural Architecture Search for Class-incremental Learning

(previous version of "Understanding Capacity Saturation in Incremental Learning")

2018

Huang, S., François-Lavet, V., Rabusseau, G. & Pineau, J. Exploring Continual Learning Using Incremental Architecture Search NeuIPS Continual Learning Workshop 2018.