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Shenyang Huang

Preferred name: Andy
Ph.D. student at School of Computer Science, McGill University
and Mila - Quebec Artificial Intelligence Institute
E-mail: shenyang.huang@mail.mcgill.ca
Google Scholar: link
Github: https://github.com/shenyangHuang
CV: Shenyang Huang [updated 4.12.2024]
Linkedin: https://www.linkedin.com/in/shenyang-huang
Twitter: shenyangHuang

Bio

I am a final year 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. 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!

  • [2024/06] Happy to share two preprints from our recent work, TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs and Towards Neural Scaling Laws for Foundation Models on Temporal Graphs. Thanks to all my amazing collaborators.

  • [2024/06] Excited to annouce our paper MiniMol: A Parameter-Efficient Foundation Model for Molecular Learning is selected as a spotlight poster at ICML 2024 Workshop on Efficient and Accessible Foundation Models for Biological Discovery. Thanks to my amazing collaborators at GraphCore, RWTH Aachen University, New Jersey Institute of Technology and Mila.

  • [2024/06] Excited to annouce our paper Temporal Graph Rewiring with Expander Graphs has been accepted to the ICML 2024 Workshop on Geometry-grounded Representation Learning and Generative Modeling. Thanks to my amazing co-authors Katarina Petrović, Farimah Poursafaei and Petar Veličković.

  • [2024/05] Excited to announce our paper Static graph approximations of dynamic contact networks for epidemic forecasting is accepted at Scientific Reports. Thanks to my amazing co-authors Razieh Shirzadkhani, Abby Leung and Reihaneh Rabbany.

  • [2024/02] Excited to announce our paper Temporal Graph Analysis with TGX is accepted at WSDM 2024 Demo Paper Track. Thanks to my amazing co-authors Razieh Shirzadkhani, Elahe Kooshafar, Reihaneh Rabbany and Farimah Poursafaei.

  • [2024/01] See our blog post providing an overview of trends and future directions in Temporal Graph Learning so far in 2024. Thanks to my amazing co-authors: Emanuele Rossi, Michael Galkin, Andrea Cini and Ingo Scholtes.

  • [2024/01] Excited to annouce two papers accepted at ICLR 2024, Graphpulse: Topological representations for temporal graph property prediction and Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets. Thanks to all my amazing collaborators!

  • Publications

    2024

  • Shirzadkhani, R.*, Ngo, T. G. B.*, Shamsi, K.* Huang, S., Poursafaei, F., Azad, P., Rabbany, R., Coskunuzer, B., Rabusseau, G., and Akcora, G. C. Towards Neural Scaling Laws for Foundation Models on Temporal Graphs (preprint)
  • Gastinger, J.* Huang, S.*, 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 (preprint)
  • 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

  • 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.

  • Teaching

  • 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
  • Scientific Reports Reviewer 2024, 2 papers
  • NeurIPS 2024 Reviewer
  • NeurIPS 2024 Datasets and Benchmarks Track Reviewer
  • NeurIPS 2023 Datasets and Benchmarks Track Reviewer
  • NeurIPS 2022 Datasets and Benchmarks Track Reviewer
  • 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