01
NeurIPS 2026 · Co-first author · Accepted
SymDrift
One-shot generative modelling under symmetries using optimal alignment and invariant embeddings, evaluated on molecular conformers and transition states.
PhD candidate · Stuttgart, Germany
Generative models
for the physical world.
I develop efficient and symmetry-aware diffusion and flow-based models, with applications in molecular modelling and scientific discovery.
- ELLIS Stuttgart 2024Best Poster Award
- ICLR 2025Oral Presentation
- NeurIPS 2026Top Reviewer
Max Planck Institute for Intelligent Systems
University of Stuttgart
PhD advisor: Prof. Mathias Niepert
Research statement
My work asks how generative models can become faster, respect physical symmetries, and transfer from large-scale generation to consequential scientific problems.
I began my PhD studying structured representations and now focus on diffusion models, flow matching, equivariance, and efficient inference across images, molecules, crystals, point clouds, proteins, and chemical reactions.
Selected work
Research across efficiency, symmetry, and science.
02
Journal of Chemical Information and Modeling · 2026
Adaptive Transition-State Refinement
Learned equilibrium flows for refining low-fidelity transition-state structures, increasing successful localisation by 41% and accelerating high-level quantum optimisation threefold.
03
NeurIPS 2025 · Equal contribution
Orbit Diffusion
Rao–Blackwellized gradient estimators for lower-variance, single-pass training of symmetry-aware diffusion models for molecules, crystals, and proteins.
04
ICLR 2025 Oral Presentation
Learning to Discretize Denoising Diffusion ODEs
Learned, sampler-specific time discretizations that improve few-step generation from pre-trained diffusion models without retraining the base model.
05
Experience
Research in academia and at production scale.
I enjoy moving between mathematical ideas, careful experiments, and implementation at scale.
2022 — now
PhD Researcher
MPI for Intelligent Systems & University of Stuttgart
Efficient and equivariant generative modelling for scientific applications. Advised by Prof. Mathias Niepert.
2025 — 2026
2020 — 2022
Research Resident
VinAI Research
Graph neural networks for knowledge-graph completion and multilingual alignment.
Recognition & academic service
ELLIS Stuttgart 2024
Best Poster Award
Awarded Best Poster at an ELLIS Stuttgart event in 2024.
ICLR 2025
Oral Presentation
Selected for an oral presentation of Learning to Discretize Denoising Diffusion ODEs.
NeurIPS 2026
Top Reviewer
Recognised as a Top Reviewer. Reviewer for ICLR, ICML, NeurIPS, EMNLP, ECCV, and WACV.
Mentoring & teaching
Supporting researchers
Mentored one Bachelor's and two Master's theses; TA for Introduction to AI and Reinforcement Learning.
Outside research
Out in the mountains.
Hiking in Austria, September 2026.
Contact
Let’s build generative AI for meaningful science.
I welcome conversations about research collaborations and Research Scientist or Applied Research opportunities in scientific discovery and health.
vinhbachkhoait@gmail.com