Ryan-Rhys
Research Scientist @Future-House | Ex Postdoc @ Meta | PhD - Machine Learning and Physics @ Cambridge University | Ex MILA | Ex Huawei Noah's Ark Lab
FutureHouse Inc.San Francisco
Pinned Repositories
gauche
A Library for Gaussian Processes in Chemistry
Constrained-Bayesian-Optimisation-for-Automatic-Chemical-Design
Code to accompany the paper "Constrained Bayesian Optimisation for Automatic Chemical Design" https://pubs.rsc.org/en/content/articlehtml/2019/sc/c9sc04026a
Cyg_X1
Gaussian process modelling of lightcurves from Cyg-X1
FlowMO
Library for training Gaussian Processes on Molecules
GProTorch
Heteroscedastic-BO
Heteroscedastic Bayesian Optimisation in Numpy
Mrk_335
Modelling the Multiwavelength Variability of Mrk-335 using Gaussian processes
Nanoparticle-Systems
PILCO
An Implementation of PILCO: Probabilistic Inference for Learning Control
The-Photoswitch-Dataset
Repository containing a benchmark dataset for machine learning property prediction of photoswitch molecules: https://pubs.rsc.org/en/content/articlehtml/2022/sc/d2sc04306h
Ryan-Rhys's Repositories
Ryan-Rhys/The-Photoswitch-Dataset
Repository containing a benchmark dataset for machine learning property prediction of photoswitch molecules: https://pubs.rsc.org/en/content/articlehtml/2022/sc/d2sc04306h
Ryan-Rhys/Constrained-Bayesian-Optimisation-for-Automatic-Chemical-Design
Code to accompany the paper "Constrained Bayesian Optimisation for Automatic Chemical Design" https://pubs.rsc.org/en/content/articlehtml/2019/sc/c9sc04026a
Ryan-Rhys/FlowMO
Library for training Gaussian Processes on Molecules
Ryan-Rhys/Heteroscedastic-BO
Heteroscedastic Bayesian Optimisation in Numpy
Ryan-Rhys/Mrk_335
Modelling the Multiwavelength Variability of Mrk-335 using Gaussian processes
Ryan-Rhys/GProTorch
Ryan-Rhys/PILCO
An Implementation of PILCO: Probabilistic Inference for Learning Control
Ryan-Rhys/Nanoparticle-Systems
Ryan-Rhys/Cyg_X1
Gaussian process modelling of lightcurves from Cyg-X1
Ryan-Rhys/Nanoparticle-Systems-Interaction-Potential-Computation
A collection of Python scripts for computing the interaction potential energy profiles in nanoparticle systems including, but not limited to, nanoparticle-electrode ensembles. The two principle forces accounted for in the computations are the Coulombic interactions between dissociated charge-bearing ligands functionalising the surface of the nanoparticles in addition to intrinsic van der Waals forces between the metal cores of the nanoparticles.
Ryan-Rhys/ac-bo-hackathon.github.io
Ryan-Rhys/Ax
Adaptive Experimentation Platform
Ryan-Rhys/botorch
Bayesian optimization in PyTorch
Ryan-Rhys/genedisco-starter
The starter repository for submissions to the GeneDisco challenge for optimized experimental design in genetic perturbation experiments.
Ryan-Rhys/HEBO
Bayesian optimisation library developped by Huawei Noah's Ark Library
Ryan-Rhys/Heteroscedastic-BayesOpt
Heteroscedastic Bayesian Optimisation in BoTorch
Ryan-Rhys/low_rank_BOPE
Ryan-Rhys/noah-research
Noah Research
Ryan-Rhys/papers-for-molecular-design-using-DL
List of molecular design using Generative AI and Deep Learning