Hi, I'm Joey Bose,

About Me

Joey Bose is a Post-Doctoral Fellow at University of Oxford working with Michael Bronstein, a Distinguished Research scientist at Dreamfold, and an Affiliate member of Mila. He completed his PhD 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 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
Pytorch
Pytorch-Geometric
Fall 2022 COMP760 Geometry and Generative Models

Education & Experience

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

  • Mila Sept 2024 - Present
    Affiliate Member
  • University of Oxford Jan 2024 - Present
    Post-doc
    Geometric Deep Learning Generative Models
  • Dreamfold July 2023 - Present
    Distinguished Research Scientist
    Proteins Generative models and Sampling
  • McGill and Mila Sept 2018 - Sept 2023
    Phd Student
    Geometric Deep Learning Generative Models Equivariant Networks Adversarial Attacks
  • Qualcomm AI Research Oct 2022 - Feb 2023
    Research Intern
    Generative Models Geometry
  • Co-Instructor at McGill Sept 2022 - Dec 2022
    Teaching COMP760
    Generative Models Geometry
  • Facebook AI Research May 2021 - Dec 2021
    Research Intern
    Generative Models Causality
  • 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

arxiv

arxiv

arxiv

arxiv

arxiv

arxiv

arxiv

arxiv

arxiv
Generative Models Equivariance

arxiv
Generative Models

arxiv
Strong Lottery Tickets Equivariant Networks Pruning

arxiv
Diffusion Models Riemmanian Geometry Generative Models

arxiv
Causality Generative Models

Equivariant Normalizing Flows Generative Models

arxiv
Continuous Normalizing Flows Generative Models

arxiv
Adversarial Attacks Online Algorithms k-secretary

arxiv
Hallucination Dialogue Systems Knowledge Graphs

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

arxiv
Policy Gradient off-policy actor-critic continuous relaxation

arxiv
Coherence Modelling NLP

arxiv
Adversarial Attacks Face Detection

arxiv
Negative Sampling Embeddings Adversarial Sampling

Contact

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/Oxford 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.