- New Haven, CT
- https://rish-16.github.io/
- @rishabh16_
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Starred repositories
Official implementation of Sharpness-Aware Flow Matching
Generative Modeling with Flux Matching
PyMOL plugin - drive PyMOL from Claude Code, Cursor, or any MCP client.
Geometric Byte Pair Encoding of Protein Structure (ICLR 2026)
SwitchCraft: A Programmatic Framework for Designing State-Switching Proteins
AutoScientists: Self-Organizing Agent Teams for Long-Running Scientific Experimentation
Uncertainty quantification (UQ) for Machine-Learning Interatomic Potentials (MLIPs)
Official pytorch repository for "Diffusion Posterior Sampling for General Noisy Inverse Problems"
Genie 3 is a fast, all-atom SE(3)-equivariant diffusion model for protein design. It achieves state-of-the-art performance on unconditional generation, motif scaffolding, and binder design while re…
oxpig / ABB4
Forked from fspoendlin/ABB4Antibody structure prediction model for sampling conformational ensembles
Terminal protein structure viewer — interactive 3D visualization of PDB/mmCIF structures with cartoon ribbons, braille rendering, and Sixel/Kitty graphics
ml-struct-bio / cryoboltz
Forked from jwohlwend/boltzCryoBoltz code for protein structure prediction with cryo-EM guidance. NeurIPS 2025.
A high-performance toolkit for atomistic simulations in JAX.
Training Neural Network potentials through customizable routines in JAX.
Fully-Differentiable Tensor Spherical Harmonics in JAX
Code for the DISCO model: General Multimodal Protein Design Enables DNA-Encoding of Chemistry
A pure-Python implementation of the Nvidia CuTe layout algebra intended to be approachable and easy to learn.
Public FSM implementation with support for ML-based potential energy surfaces
A benchmark for comprehensive evaluation on protein structure tokenization methods
KV cache compression via block-diagonal rotation. Beats TurboQuant: better PPL (6.91 vs 7.07), 28% faster decode, 5.3x faster prefill, 44x fewer params. Drop-in llama.cpp integration.
The Official repo for Self-Conditioned Denoising for Atomistic Representation Learning