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 anomaly detection in dynamic graph using spectral techniques. I also have a broad interest in representation learning for dynamic graphs, graph neural networks, disease modelling and continual learning.

News!

  • 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!
  • The TGL workshop Submission Portal is now available. Follow us on Twitter for updates!
  • 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

    2022

  • Poursafaei, F.*, Huang, S.*, Pelrine, K., Rabbany, R. Towards Better Evaluation for Dynamic Link Prediction (To appear at 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, 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
  • 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

  • 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