profile picture
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, graph neural networks, disease modelling and continual learning.

News!

2023

  • [2023/02] The arXiv pre-print for our work "Laplacian Change Point Detection for Single and Multi-view Dynamic Graphs" is now available. In this work, we extend our prior work LAD to multi-view dynamic graphs. Thanks to my co-authors: Samy Coulombe, Yasmeen Hitti, Reihaneh Rabbany, Guillaume Rabusseau.
  • [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.
  • 2022

  • [2022/12] The Temporal Graph Learning (TGL) Workshop was hosted at NeurIPS 2022. Happy to see many in person and online attendees interested in TGL. You can find the list of accepted papers and talk recordings on the NeurIPS virtual site and more information on the workshop website
  • [2022/11] I was invited to give a talk at the LoGaG reading group about our recent work. The video is now available youtube.
  • [2022/10] The companion blog post for our NeurIPS 2022 Datasets and Benchmarks Track paper is now available on medium and our lab website
  • [2022/9] Our submission Towards Better Evaluation for Dynamic Link Prediction has been accepted to the Datasets and Benchmarks Track at NeurIPS 2022! Huge thanks to my co-authors!
  • [2022/6] I am co-organizing the Temporal Graph Learning Workshop@NeurIPS 2022. Thanks to my amazing co-organizers: Farimah Poursafaei, Kellin Pelrine, Aarash Feizi, Jianan Zhao, Meng Qu, Reihaneh Rabbany, Jian Tang, Michael Bronstein.
  • Publications

    2023

  • 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
  • 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
  • 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