Bio

I am a postdoc at University of Oxford working with Prof. Michael Bronstein. Previously, I obtained my Ph.D. at McGill University and affiliated with Mila , supervised by Prof. Reihaneh Rabbany and Prof. Guillaume Rabusseau . I also obtained an Honours in Computer Science from McGill University in 2019. I have a broad interest in temporal graph neural networks, graph transformers, graph neural networks and spectral methods. My research focuses on machine learning models for complex and evolving networks in the real world, referred to as Temporal Graph Learning (TGL). I also actively engage in building the TGL community by organizing the TGL reading group and two editions of the TGL workshop @ NeurIPS 2022 / 2023. Through my research, I aim to answer the following questions:

• How to design more scalable, expressive and powerful temporal graph methods?
• How to properly evaluate temporal graph methods based on real world considerations?
• How to deploy TGL methods for applications such as disease modeling, anomaly detection and forecasting?

News!

[2025/07] Excited to annouce that our tutorial: Relational Deep Learning: Challenges, Foundations and Next-Generation Architectures will be presented at KDD 2025. Thanks to my amazing co-organizers at Stanford: Vijay Prakash Dwivedi, Charilaos I. Kanatsoulis and Jure Leskovec. See you at KDD 2025 and here is the tutorial website and pre-print of our survey paper .

[2025/06] Excited to share our preprint Are Large Language Models Good Temporal Graph Learners? Thanks to my amazing co-authors Ali Parviz, Emma Kondrup, Zachary Yang, Zifeng Ding, Michael Bronstein, Reihaneh Rabbany, Guillaume Rabusseau to make this possible.

[2025/06] I am thrilled to share that the third edition of the TGL workshop will be held at KDD 2025 in Toronto. here is the TGL workshop website for details and hope to see you at KDD.

[2025/04] I have officially started my postdoc at University of Oxford working with prof. Michael Bronstein. Excited for this new journey.

[2025/02] Delievered three lectures on temporal graph learning at COMP 511: Network Science. In this lectures, I cover An introduction to temporal graph, Temporal Graph Neural Networks as well as Frontiers of Temporal Graph Research. You can find the slides on the resource section of this website.

[2025/01] I have successfully defended my thesis, my PhD journey will not be possible without my amazing supervisors prof. Reihaneh Rabbany and prof. Guillaume Rabusseau. I would also like to thank my family, friends and collaborators for their support and advice.

Publications

2025

Dwivedi, V. P., Kanatsoulis, C., Huang, S., Leskovec, J. Relational Deep Learning: Challenges, Foundations and Next-Generation Architectures (KDD 2025)

Huang, S., Parviz, A., Kondrup, E., Yang, Z., Ding, Z., Bronstein, M., Rabbany, R., Rabusseau, G. Are Large Language Models Good Temporal Graph Learners? (preprint)

Yuan, Y., Zhang, Z., He, X., Nitta, A., Hu, W., Shah, M., Stojanovič, B., Huang, S., Lenssen, J., Leskovec, J., Fey, M. ContextGNN: Beyond Two-Tower Recommendation Systems (ICLR 2025)

Shirzadkhani, R.*, Ngo, T. G. B.*, Shamsi, K.* Huang, S., Poursafaei, F., Azad, P., Rabbany, R., Coskunuzer, B., Rabusseau, G., and Akcora, G. C. MiNT: Multi-Network Training for Transfer Learning on Temporal Graphs (preprint)

2024

Huang, S.*, Gastinger, J.* Galkin, M., Loghmani, E., Parviz, A., Poursafaei, F., Danovitch, J., Rossi, E., Koutis, I., Stuckenschmidt, H., Rabbany, R., and Rabusseau, G. TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs (NeurIPS 2024 Datasets and Benchmark Track)

Huang, S.*, Poursafaei, F.*, Rabbany, R., Rabusseau, G. and Rossi, E. UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs (LOG 2024)

Petrović., K., Huang, S., Poursafaei, F., and Veličković, P. Temporal Graph Rewiring with Expander Graphs (ICML 2024 Workshop on Geometry-grounded Representation Learning and Generative Modeling)

Kläser, K., Banaszewski, B., Maddrell-Mander, S., McLean, C., Müller, L., Parviz, A., Huang, S. and Fitzgibbon, A. MiniMol: A Parameter-Efficient Foundation Model for Molecular Learning (Spotlight Poster at ICML 2024 Workshop on Efficient and Accessible Foundation Models for Biological Discovery)

Shirzadkhani, R., Huang, S., Leung, A. and Rabbany, R. Static graph approximations of dynamic contact networks for epidemic forecasting (Scientific Reports)

Shamsi, K., Poursafaei, F., Huang, S., Ngo, B., Coskunuzer, B. and Akcora, C. Graphpulse: Topological representations for temporal graph property prediction (ICLR 2024)

Beaini, D., Huang, S., Cunha, J.A., Li, Z., Moisescu-Pareja, G., Dymov, O., Maddrell-Mander, S., McLean, C., Wenkel, F., Müller, L., Mohamud, J., Parviz, A., Craig, M., Koziarski, M., Lu, J., Zhu, Z., Gabellini, C., Klaser, K., Dean, J., Wognum, C., Sypetkowski, M., Rabusseau, G., Rabbany, R., Tang, J., Morris, C., Koutis, I., Ravanelli, M., Wolf, G., Tossou, P., Mary, H., Bois, T., Fitzgibbon, A., Banaszewski, B., Martin, C., and Masters, D. Towards Foundational Models For Molecular Learning on Large-scale Multi-task Datasets (ICLR 2024)

Shirzadkhani, R., Huang, S.,, Kooshafar, E., Rabbany, R., and Poursafaei, F. Temporal Graph Analysis with TGX (WSDM 2024 Demo Track)

2023

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 (NeurIPS 2023 Datasets and Benchmarks Track)

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

Huang, S., Coulombe, S., Hitti, Y., Rabbany, R., Rabusseau, G. Laplacian Change Point Detection for Single and Multi-view Dynamic Graphs (ACM Transactions on Knowledge Discovery from Data, TKDD)

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)

Pupneja, Y., Zou, J., Lévy, S. Huang, S., Understanding Opinions Towards Climate Change on Social Media (NeurIPS 2023 Workshop: Tackling Climate Change with Machine Learning)

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

2022

Huang, S.*, Poursafaei, F.*, 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.

Blogs

Lecture Slides

Lectures on Temporal Graph Learning, COMP 511 Network Science McGill University Winter 2025

Introduction to Temporal Graph Learning Temporal Graph Neural Networks Frontiers of Temporal Graph Learning

Teaching

Winter 2025, Course Lecturer for Advanced Topic on Temporal Graph Learning, 3 Lectures on TGL, COMP 511 Network Science, McGill University

Winter 2025, Mentor, Geometric Deep Learning L65, University of Cambridge

Winter 2024, Mentor, Geometric Deep Learning L65, University of Cambridge

Winter 2023, Mentor, Representation Learning on Graphs and Networks L45, University of Cambridge

Fall 2022, Guest Lecturer, Anomaly Detection for Dynamic Graphs (updated slides), COMP 599 Network Science, McGill University

Fall 2021, Guest Lecturer, Anomaly Detection for Dynamic Graphs, COMP 599 Network Science, McGill University

Fall 2021, TA, COMP 599 Network Science, McGill University

Winter 2020, TA, COMP 250, Introduction to Computer Science, McGill University

Fall 2019, TA, COMP 202, Introduction to Programming, McGill University

Services

Organizer of the weekly Temporal Graph Reading Group

Organizer of the Temporal Graph Learning Community Slack, see here to join

Organization Chair for Temporal Graph Learning Workshop@NeurIPS 2023

Organization Chair for Temporal Graph Learning Workshop@NeurIPS 2022

ICLR 2025 Reviewer

NeurIPS 2024 Reviewer

NeurIPS 2024 Datasets and Benchmarks Track Reviewer

NeurIPS 2023 Datasets and Benchmarks Track Reviewer

NeurIPS 2022 Datasets and Benchmarks Track Reviewer

Scientific Reports Reviewer 2024, 2 papers

Reviewer for Transactions on Machine Learning Research (TMLR) journal 2023

KDD 2021 External Reviewer

IEEE Transactions on Neural Networks and Learning Systems Reviewer 2021

ECML PKDD 2020 Program Committee Member

Awards and Scholarships

NSERC Postgraduate Scholarships-Doctoral (PGS D) Award, 2022-2025

Fonds de recherche du Québec – Nature et Technologies (FRQNT) Doctoral Award, 2022-2026

Scientist in Residence Program, Valence Labs and Mila, 2023

McGill Graduate Research Enhancement and Travel Awards (GREAT awards), 2023

Mitacs Accelerate Award, 2022

NSERC Undergraduate Student Research Awards, 2018

McGill Undergraduate Computer Science Research Award, 2nd Place Winner, 2018

McGill Physics Hackathon, 2nd Place Winner, 2017

NSERC Undergraduate Student Research Awards, 2016

Mentorship

2024 April-May, Research Intern, Julia Gastinger, TGB 2.0

2024, Research Assistant, Razieh Shirzadkhani

2024, Project Mentor, Qianyi Liu and Zak Buzzard, Better time encoding models in temporal GNNs.

2024, Project Mentor, Dobromir Marinov and Lauren R. Wilkes, Adversarial Attacks on continuous-time financial networks

2024, Project Mentor, Kiril Bikov and Riccardo Conci, Looking into the past: exploring time dependencies beyond exponential decay

2023-2024, Research Intern, Shahrad Mohammadzadeh, better temporal graph learning methods.

2022-2023, Research Assistant, Razieh Shirzadkhani, Disease Modeling with Dynamic Graphs & TGX package

2023, Project Mentor, Lekang Jiang and Caiqi Zhang, Towards Temporal Edge Regression: A Case Study on Agriculture Trade Between Nations

2023, Project Mentor, Xiangjian Jiang and Yanyi Pu, Exploring Time Granularity on Temporal Graphs for Dynamic Link Prediction in Real-world Networks

2022, Research Assistant, Abu bakar Daud, pypi package for Towards better dynamic link prediction