Joey Bose
Assistant Professor of Computing
Leads the Bose Lab building generative models for science, including geometric deep learning and flow-based models for molecules and proteins.
Joey Bose's lab at Imperial works on generative & foundation models, graph learning and geometry processing & modeling. 28 papers since 2023, 24 at top venues, mostly NeurIPS and ICLR. 7 PhD students. 2 alumni. Applications close .
At a glance
- papers, 2023–2026
- 7/ year
- at T1 venues
- 86%
- PhD students
- 7
- MS students
- 0
- alumni listed
- 2
Papers by yearfrom the lab homepage, 2023–2026
- 12 papers2026 · T1 8 · collecting
- 6 papers2025 · T1 6
- 7 papers2024 · T1 7
- 3 papers2023 · T1 3
T1 venuesOtherVenues: NeurIPS, ICLR, ICML
Research focuspaper titles that use each topic's words
- Generative & foundation models11 papers
- Graph learning2 papers
- Geometry processing & modeling1 paper
- molecules0 papers
- proteins0 papers
Paper list
From the lab homepage.
202612 papers
- NeurIPS (T1)Aligning Flow Map Policies with Optimal Q-Guidance (opens in a new tab)
- ICML (T1)Autoregressive Boltzmann Generators (opens in a new tab)
- NeurIPS (T1)Coupling Models for One-Step Discrete Generation (opens in a new tab)
- ICLR (T1)Efficient Regression-Based Training of Normalizing Flows for Boltzmann Generators (opens in a new tab)
- NeurIPS (T1)Few-step Cofolding with All-Atom Flow Maps (opens in a new tab)
- NeurIPS STODY WorkshopFixed-Point Rates for Self-Distillation of Flow Maps (opens in a new tab)
- ICLR (T1)Generalised Flow Maps for Few-Step Generative Modelling on Riemannian Manifolds (opens in a new tab)
- NeurIPS STODY WorkshopNormalizing Flow Maps (opens in a new tab)
- NeurIPS STODY WorkshopOn the Stability and Convergence of Flow Map Training (opens in a new tab)
- ICLR (T1)OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Prediction (opens in a new tab)
- ICLR (T1)Planner Aware Path Learning in Diffusion Language Models Training (opens in a new tab)
- NeurIPS STODY WorkshopSteered Not Stirred: Effective Tilting in Generative Models using Surrogate Reward Mixing (opens in a new tab)
20256 papers
- NeurIPS (T1)Curly Flow Matching For Learning Non-Gradient Field Dynamics (opens in a new tab)
- NeurIPS (T1)Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities (opens in a new tab)
- NeurIPS (T1)RETRO SYNFLOW: Discrete Flow Matching for Accurate and Diverse Single-Step Retrosynthesis (opens in a new tab)
- ICML (T1)Scalable Equilibrium Sampling with Sequential Boltzmann Generators (opens in a new tab)
- ICLR (T1)Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction (opens in a new tab)
- ICLR (T1)The Superposition of Diffusion Models (opens in a new tab)
20247 papers
- NeurIPS (T1)Fisher Flow Matching for Generative Modeling over Discrete Data (opens in a new tab)
- ICML (T1)Iterated Denoising Energy Matching for Sampling from Boltzmann Densities (opens in a new tab)
- NeurIPS (T1)Metric Flow Matching for Smooth Interpolations on the Data Manifold (opens in a new tab)
- ICLR (T1)On the Stability of Iterative Retraining of Generative Models on their own Data (opens in a new tab)
- ICLR (T1)SE(3)-Stochastic Flow Matching for Protein Backbone Generation (opens in a new tab)
- NeurIPS (T1)Self-Consuming Generative Models with Curated Data Provably Optimize Human Preferences (opens in a new tab)
- NeurIPS (T1)Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation (opens in a new tab)
20233 papers
- ICLR (T1)A General Framework For Proving The Equivariant Strong Lottery Ticket Hypothesis (opens in a new tab)
- NeurIPS (T1)EDGI: Equivariant Diffusion for Planning with Embodied Agents (opens in a new tab)
- NeurIPS (T1)Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using Samples (opens in a new tab)
Where alumni went2 listed
Apply to Imperial2027 (Apr/Oct)
Computing PhD
- Round
- Round 2 (1 Dec 2026)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- none
Confirmed on the program page.
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