Hi, I'm Joey Bose,

About Me

Joey Bose is a third year PhD student at the RLLab at McGill/MILA under the supervision of Will Hamilton, Gauthier Gidel, and Prakash Panagaden. His research interests span Generative Modelling, Differential Geometry for Machine Learning, Adversarial Attacks and Graph Representation Learning with a current emphasis on understanding symmetries, equivariances and invariances in data. Previously, he completed his Bachelors and Master’s degrees from the University of Toronto working on adversarial attacks against face detection and is the President and CEO of FaceShield Inc an educational platform for digital privacy for facial data. His work has been featured in Forbes, CBC, VentureBeat and other media outlets and is generously supported by the IVADO PhD Fellowship.

Research Interests

Generative Models
Normalizing Flows
Equivariant Networks
Adversarial Attacks
Graph Neural Networks
KG Embeddings

Education & Experience

For more information, have a look at my curriculum vitae .

  • McGill and Mila Sept 2018 - Present
    Phd Student
    Geometric Deep Learning Normalizing Flows Equivariant Networks Adversarial Attacks
  • FaceShield.ai (acquired) August 2018 - June 2020
    Founder and CEO
    Adversarial Attacks on Face Detection
  • Uber AI May 2019 - Aug 2019
    Research Intern
    Graph Neural Networks Meta-Learning
  • University of Toronto Sept 2017 - Aug 2018
    Masters of Applied Science Computer Engineering
    Adversarial Attacks on Face Detection
  • Borealis AI May 2017 - Jul 2018
    Resarch Intern
    NLP Embeddings Negative Sampling
  • Bachelors of Applied Science in Computer Engineering and Minor in Mechatronics

Publications and Pre-Prints

Hallucination Dialogue Systems Knowledge Graphs

Adversarial Attacks Online Algorithms k-secretary

arxiv git blog
Adversarial Attacks Game Theory

arxiv git
Knowledge Graphs Negative Sampling

arxiv git blog
Normalizing Flows Hyperbolic Geometry

arxiv git
Link Prediction Graph Neural Networks Meta-Learning

arxiv git
Graph Neural Networks Fairness Node Embeddings

Policy Gradient off-policy actor-critic continuous relaxation

Coherence Modelling NLP

Adversarial Attacks Face Detection

Negative Sampling Embeddings Adversarial Sampling


If you like what I do and want to potentially collaborate feel free to reach out via email. Note for prospective students interested in joining Mila/McGill I cannot assess your candidacy and will not be able to help you get an interview with my advisor. But, I can offer general advice about applying to grad school and the culture of our lab.