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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
Linkedin: https://www.linkedin.com/in/shenyang-huang
Twitter: shenyangHuang

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

  • [2023/05] 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

  • 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 (pre-print)
  • 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.

  • Teaching

  • Guest Lecturer, Anomaly Detection for Dynamic Graphs (updated slides), Fall 2022 COMP 599, Network Science
  • Guest Lecturer, Anomaly Detection for Dynamic Graphs, Fall 2021 COMP 599, Network Science
  • TA, Fall 2021 COMP 599, Network Science
  • TA, Winter 2020 COMP 250, Introduction to Computer Science
  • TA, Fall 2019 COMP 202, Introduction to Programming

  • Services

  • Organization Chair for Temporal Graph Learning Workshop@NeurIPS 2022
  • Organizer of the weekly Temporal Graph Reading Group
  • Organizer of the Temporal Graph Learning Community Slack
  • Reviewer for Transactions on Machine Learning Research (TMLR) journal 2023
  • NeurIPS 2022 Datasets and Benchmarks Track Reviewer
  • 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
  • McGill Graduate Research Enhancement and Travel Awards (GREAT awards), 2023
  • 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