10 citations · 16 across the 14 of their papers we have counts for
6 papers · 1 filter
A Complete Guide to Spherical Equivariant Graph Transformers
Sophia Tang
Spherical equivariant graph neural networks (EGNNs) provide a principled framework for learning on three-dimensional molecular and biomolecular systems, where predictions must resp…
Entangled Schrödinger Bridge Matching
Sophia Tang, Yinuo Zhang, Pranam Chatterjee
Simulating trajectories of multi-particle systems on complex energy landscapes is a central task in molecular dynamics (MD) and drug discovery, but remains challenging at scale due…
TR2-D2: Tree Search Guided Trajectory-Aware Fine-Tuning for Discrete Diffusion
Sophia Tang, Yuchen Zhu, Molei Tao +1
Reinforcement learning with stochastic optimal control offers a promising framework for diffusion fine-tuning, where a pre-trained diffusion model is optimized to generate paths th…
Branched Schrödinger Bridge Matching
Sophia Tang, Yinuo Zhang, Alexander Tong +1
Predicting the intermediate trajectories between an initial and target distribution is a central problem in generative modeling. Existing approaches, such as flow matching and Schr…
Multi-Objective-Guided Discrete Flow Matching for Controllable Biological Sequence Design
Tong Chen, Yinuo Zhang, Sophia Tang +1
Designing biological sequences that satisfy multiple, often conflicting, functional and biophysical criteria remains a central challenge in biomolecule engineering. While discrete…
Gumbel-Softmax Flow Matching with Straight-Through Guidance for Controllable Biological Sequence Generation
Sophia Tang, Yinuo Zhang, Alexander Tong +1
Flow matching in the continuous simplex has emerged as a promising strategy for DNA sequence design, but struggles to scale to higher simplex dimensions required for peptide and pr…