About Me
Joey Bose is an incoming Assistant Professor of Computing at Imperial College London starting January 2026, and an Affiliate member Mila. Previously, he was a Post-Doctoral Fellow at University of Oxford working with Michael Bronstein. 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 geometric generative models for scientific applications. 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, New York Times, CBC, VentureBeat and other media outlets and was generously supported by the IVADO PhD Fellowship, and NSERC Post-doc Fellowship.
Research Interests, Students, Teaching & Prospective Students
- Sampling from Unnormalized Densities
- Boltzmann Generators
- Generative models such as Diffusion and Flow Matching
- Discrete Generative models, e.g., Discrete Diffusion
- Molecular Dynamics
- Neural Net Potentials
- Protein design with generative models
- Adversarial machine learning for generative models
- Red teaming LLMs and Discrete Diffusion
- Deep Learning theory for synthetic data
- Posterior sampling and Bayesian Inference
Education & Experience
For more information, have a look at my curriculum vitae .
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Jan 2026 - PresentAssistant Professor of Computing
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Sept 2024 - PresentAffiliate Member
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Jan 2024 - Dec 2025Post-docGeometric Deep Learning Generative Models
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July 2023 - Jan 2025Distinguished Research ScientistProteins Generative models and Sampling
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Sept 2018 - Sept 2023Phd StudentGeometric Deep Learning Generative Models Equivariant Networks Adversarial Attacks
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Oct 2022 - Feb 2023Research InternGenerative Models Geometry
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Sept 2022 - Dec 2022Teaching COMP760Generative Models Geometry
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May 2021 - Dec 2021Research InternGenerative Models Causality
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August 2018 - June 2020Founder and CEOAdversarial Attacks on Face Detection
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May 2019 - Aug 2019Research InternGraph Neural Networks Meta-Learning
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Sept 2017 - Aug 2018Masters of Applied Science Computer EngineeringAdversarial Attacks on Face Detection
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May 2017 - Jul 2018Resarch InternNLP Embeddings Negative Sampling
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2012 - 2017Bachelors of Applied Science in Computer Engineering and Minor in Mechatronics
Publications and Pre-Prints
Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities
NeurIPS 2025 (Spotlight)
T. Akhound-Sadegh*, J. Lee*, A. J. Bose, V. De Bortoli, A. Doucet, M. Bronstein, D. Beaini, S. Ravanbakhsh, K. Neklyudov†, A. Tong†
arxiv
Path Planning for Masked Diffusion Models with Applications to Biological Sequence Generation
Preprint 2025
F. Z. Peng*, Z. Bezemek*, S. Patel, J. Rector-Brooks, A. J. Bose, A. Tong†, P Chatterjee†
arxiv
RETRO SYNFLOW, Discrete Flow-Matching for Accurate and Diverse Single-Step Retrosynthesis
NeurIPS 2025
R. Yadav, Q. Yan, G. Wolf, A. J. Bose, R. Liao
arxiv
FORT, Forward-Only Regression Training of Normalizing Flows
ICML GenBio Workshop 2025 (Best Paper)
D. Rehman, O. Davis, J. Lu, J. Tang, M. Bronstein, Y. Bengio, A. Tong†, A. J. Bose†
arxiv
Curly Flow Matching For Learning Non-Gradient Field Dynamics
NeurIPS 2025
K. Petrović, L. Atanackovic, V. Morro, K. Kapusniak, M. Bronstein, A. J. Bose†, A Tong†
arxiv
Scalable Equilibrium Sampling with Sequential Boltzmann Generators
ICML 2025
C. B. Tan*, A. J. Bose*, C. Lin, L. Klein, M. Bronstein, A. Tong
arxiv
The Superposition of Diffusion Models
ICLR 2025 Spotlight
M. Skreta*, L. Atanackovic*, A. J. Bose, A. Tong, K. Neklyudov
arxiv
Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction
ICLR 2025
J. Rector-Brooks, M. Hasan, Z. Peng, Z. Quinn, C. Liu, S. Mittal, N. Dziri, M. Bronstein, Y. Bengio, P. Chatterjee, A. Tong†, A. J. Bose†
arxiv
Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation
NeurIPS 2024
G. Huguet*, J. Vuckovic*, K. Fatras, E. Thibodeau-Laufer, P. Lemos, R. Islam, C. Liu, J. Rector-Brooks, T. Akhound-Sadegh, M. Bronstein, A. Tong†, A. J. Bose†
arxiv
Self-Consuming Generative Models with Curated Data Provably Optimize Human Preferences
NeurIPS 2024 (Spotlight)
D. Ferbach, Q. Bertrand, A. J. Bose, G. Gidel
arxiv
Metric Flow Matching for Smooth Interpolations on the Data Manifold
NeurIPS 2024
K. Kapusniak, P. Potaptchik, T. Reu, L. Zhang, A. Tong, M. Bronstein, A. J. Bose, F. D. Giovanni
arxiv
Fisher Flow Matching for Generative Modeling over Discrete Data
NeurIPS 2024
O. Davis, S. Kessler, M. Petrache, I. I. Ceylan, M. Bronstein, A. J. Bose
arxiv
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities
ICML 2024
T. Akhound-Sadegh*, J. Rector-Brooks*, A.J. Bose*, S. Mittal, P. Lemos, C.H. Liu, M. Sendera, S. Ravanbaksh, G. Gidel, Y. Bengio, N. Malkin, A. Tong
arxiv
SE(3) Stochastic Flow Matching for Protein For Protein Backbone Generation
ICLR 2024 (Spotlight)
A. J. Bose*, T. Akhound-Sadegh*, K. Fatras, G. Huguet, J. Rector-Brooks, C.H. Liu, A.C. Nica, M. Korablyov, M. Bronstein, A. Tong
arxiv
On the Stability of Iterative Retraining of Generative Models on their own Data
ICLR 2024 (Spotlight)
Q. Bertrand, A. J. Bose, A. Duplessis, M. Jiralerspong, G. Gidel
arxiv
EDGI, Equivariant diffusion for planning with embodied agents
NeurIPS 2023
J. Bremer*, A. J. Bose*, P. de Haan, T. Cohen
arxiv
Generative Models
Equivariance
Feature Likelihood Score, Evaluating Generalization of Generative Models Using Samples
NeurIPS 2023
M. Jiralerspong, A. J. Bose, G. Gidel
arxiv
Generative Models
A General Framework For Proving The Equivariant Strong Lottery Ticket Hypothesis
ICLR 2023
D. Ferbach*, C. Tsirigotis*, G. Gidel, A.J. Bose
arxiv
Strong Lottery Tickets
Equivariant Networks
Pruning
Riemannian Diffusion Models
NeurIPS 2022
C.W. Huang*, M. Aghajohari*, A.J. Bose, P. Panangaden, A. Courville
arxiv
Diffusion Models
Riemmanian Geometry
Generative Models
Controllable Generative Modeling via Causal Reasoning
TMLR 2022
A.J. Bose, R. Monti, A. Grover
arxiv
Causality
Generative Models
Equivariant Finite Normalizing Flows
Preprint 2022
A.J. Bose, I. Kobyzev, M. Brubaker
Equivariant Normalizing Flows
Generative Models
Matching Normalizing Flows and Probablity Paths on Manifolds
ICML 2022
H. Ben-Hamu*, S. Cohen*, A.J. Bose, B. Amos, M. Nickel, A. Grover, R. Chen, Y. Lipman
arxiv
Continuous Normalizing Flows
Generative Models
Online Adversarial Attacks
ICLR 2022
A. Mladenović*, A.J. Bose*, H. Berard*, W.L. Hamilton, S. Lacoste-Julien, P. Vincent, G.Gidel
arxiv
Adversarial Attacks
Online Algorithms
k-secretary
Neural Path Hunter, Reducing Hallucination in Dialogue Sytems via Path Grounding
EMNLP 2021
N. Dziri, A. Madotto, O. Zaiane, A.J. Bose
arxiv
Hallucination
Dialogue Systems
Knowledge Graphs
Adversarial Example Games
NeurIPS 2020
A.J. Bose*, G. Gidel*, H. Berard*, A. Cianflone, P. Vincent, S. Lacoste-Julien, W.L. Hamilton
arxiv
git
blog
Adversarial Attacks
Game Theory
Structure Aware Negative Sampling in Knowledge Graphs
EMNLP 2020
K. Ahrabian, A. Feizi, Y. Salehi, W. L. Hamilton, A. J. Bose
arxiv
git
Knowledge Graphs
Negative Sampling
Latent Variable Modeling with Hyperbolic Normalizing Flows
ICML 2020
A. J. Bose, A. Smofsky, R. Liao, P. Panangaden, W. L. Hamilton
arxiv
git
blog
Normalizing Flows
Hyperbolic Geometry
Meta-Graph Few Shot Link Prediction Via Meta-Learning
Preprint (Unpublished)
A. J. Bose, A. Jain, P. Molino, W. L. Hamilton
arxiv
git
Link Prediction
Graph Neural Networks
Meta-Learning
Compositional Fairness Constraints for Graph Embeddings
ICML 2019
A. J. Bose, W. L. Hamilton
arxiv
git
Graph Neural Networks
Fairness
Node Embeddings
Discrete off-policy policy gradient using continuous relaxations
RLDM 2019
A. Cianflone, Z. Ahmed, R. Islam, A. J. Bose, W. L. Hamilton
arxiv
Policy Gradient
off-policy actor-critic
continuous relaxation
A Cross-Domain Transferable Neural Coherence Model
ACL 2019
X. Peng, H. Saghir, J. Kang, T. Long, A. J. Bose, Y. Cao, J. Cheung
arxiv
Coherence Modelling
NLP
Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization
IEEE MMSP (Best Paper Runner-up)
A. J. Bose, P. Aarabi
arxiv
Adversarial Attacks
Face Detection
Adversarial Contrastive Estimation
ACL 2018 (ORAL)
A. J. Bose*, Y. Cao*, H. Ling*
arxiv
Negative Sampling
Embeddings
Adversarial Sampling
Contact
I am actively looking for new Phd students, Post-docs, and academic visitors at Imperial College London. In addition, I am open to industrial collaborations/consultancy on topics that overlap with my research interests. Please do reach out via email.