プロフィール詳細

プロジェクトを作成

★★★★★

☆☆☆☆☆

★★★★★

☆☆☆☆☆

Ryan-Rhys G.に依頼

United States

LLMs, Physics, Chemistry, Mathematics, ML, Data Science

プロフィール概要

AI Research Scientist (PhD, Cambridge), 13 years in ML/AI, formerly at Meta and Huawei. Specializing in LLMs, AI for science, and mathematical reasoning, with NeurIPS papers spanning LLM math evaluation (Mathematical Capabilities of ChatGPT), foundation models for drug discovery (GAUCHE), and a 24B chemistry reasoning model in production. Outstanding Reviewer awards at NeurIPS and AISTATS. Tools: PyTorch, Hugging Face. I help enterprises turn frontier AI into deployable systems.

専門分野

• ☆ Data Analysis
• ☆ Machine Learning
• ☆ Python

サービス

• Writing

Writing Technical Writing, General Proofreading & Editing

職務経験

AI Research Scientist

Independent Contractor

6月 2025 - 現在

AI Research Scientist

FutureHouse

5月 2024 - 現在

Postdoctoral Research Scientist

Meta

8月 2022 - 5月 2024

Machine Learning Consultant

Cambridge Spark

11月 2017 - 11月 2021

AI Research Scientist

Huawei Technologies

10月 2020 - 10月 2021

Machine Learning Engineer

PROWLER.io

10月 2017 - 10月 2018

学歴

PhD in Physics (Physics)

UNIVERSITY OF CAMBRIDGE

10月 2018 - 8月 2022

MPhil in Machine Learning (Engineering)

UNIVERSITY OF CAMBRIDGE

9月 2016 - 9月 2017

MSci in Chemistry with Molecular Physics (Chemistry)

Imperial College London

10月 2012 - 6月 2016

認定資格

• 認定資格の詳細は未入力です。

出版物

JOURNAL ARTICLE

Ryan-Rhys Griffiths, Bojana Ranković, Henry B. Moss, Philippe Schwaller (2024). Bayesian optimisation for additive screening and yield improvements – beyond one-hot encoding . Digital Discovery.

Griffiths, Ryan-Rhys (2023). Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black Holes . ArXiv.

Griffiths, Ryan-Rhys, Rankovic, Bojana, Moss, Henry B., Schwaller, Philippe (2023). Bayesian optimisation for additive screening and yieldimprovements in chemical reactions - beyond one-hot encoding . ChemRxiv.

Griffiths, Ryan-Rhys, Rankovic, Bojana, Moss, Henry B., Schwaller, Philippe (2023). Bayesian optimisation for additive screening and yieldimprovements in chemical reactions - beyond one-hot encoding . ChemRxiv.

Anthony Bourached, Ryan‐Rhys Griffiths, Robert Gray, Ashwani Jha, Parashkev Nachev(2022). Generative model‐enhanced human motion prediction . Applied AI Letters. 3. (2). Wiley

Ryan-Rhys Griffiths, Alexander A Aldrick, Miguel Garcia-Ortegon, Vidhi Lalchand, Alpha A Lee(2022). Achieving robustness to aleatoric uncertainty with heteroscedastic Bayesian optimisation . Machine Learning: Science and Technology. 3. (1). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 015004. {IOP} Publishing

Griffiths, R.-R., Aldrick, A.A., Garcia-Ortegon, M., Lalchand, V., Lee, A.A.(2022). Achieving robustness to aleatoric uncertainty with heteroscedastic Bayesian optimisation . Machine Learning: Science and Technology. 3. (1).

Griffiths, Ryan-Rhys, Rankovic, Bojana, Moss, Henry, Schwaller, Philippe (2022). Bayesian optimisation for additive screening and yield improvements in chemical reactions – beyond one-hot encodings . ChemRxiv.

Griffiths, R.-R., Greenfield, J.L., Thawani, A.R., Jamasb, A.R., Moss, H.B., Bourached, A., Jones, P., McCorkindale, W., Aldrick, A.A., Fuchter, M.J., et al.(2022). Data-driven discovery of molecular photoswitches with multioutput Gaussian processes . Chemical Science. 13. (45). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 13541-13551.

Cowen-Rivers, A.I., Lyu, W., Tutunov, R., Wang, Z., Grosnit, A., Rhys, R., Maravel, A.M., Jianye, H., Wang, J., Peters, J., et al.(2022). HEBO: Pushing The Limits of Sample-Efficient Hyperparameter Optimisation . Journal of Artificial Intelligence Research. 74. Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 1269-1349.

Alexander I Cowen-Rivers, Jan Peters, Haitham Bou Ammar, Wenlong Lyu, Rasul Tutunov, Zhi Wang, Antoine Grosnit, Ryan Rhys Griffiths, Alexandre Max Maraval, Hao Jianye, et al. (2022). HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation . ArXiv.

Cowen-Rivers, Alexander I., Lyu, Wenlong, Tutunov, Rasul, Wang, Zhi, Grosnit, Antoine, Griffiths, Ryan Rhys, Maravel, Alexandre Max, Hao, Jianye, Wang, Jun, Peters, Jan, et al. (2022). HEBO: Pushing The Limits of Sample-Efficient Hyperparameter Optimisation . Journal of Artificial Intelligence Research.

Data-driven discovery of molecular photoswitches with multioutput Gaussian processes <head> <META HTTP-EQUIV="Refresh" CONTENT="0;URL=/servlet/useragent"> </head> . Chemical Science.

Griffiths, Ryan-Rhys, Greenfield, Jake L., Thawani, Aditya R., Jamasb, Arian R., Moss, Henry B., Bourached, Anthony, Jones, Penelope, McCorkindale, William, Aldrick, Alexander A., Fuchter, Matthew J., et al. (2022). Data-driven discovery of molecular photoswitches with multioutput Gaussian processes . Chemical Science.

Ryan-Rhys Griffiths, Jiachen Jiang, Douglas J. K. Buisson, Dan Wilkins, Luigi C. Gallo, Adam Ingram, Alpha A. Lee, Dirk Grupe, Erin Kara, Michael L. Parker, et al. (2021). Modeling the Multiwavelength Variability of Mrk 335 Using Gaussian Processes . The Astrophysical Journal.

Griffiths, Ryan-Rhys, Grosnit, Antoine, Cowen-Rivers, Alexander, I, Tutunov, Rasul, Wang, Jun, Bou-Ammar, Haitham (2021). Are we Forgetting about Compositional Optimisers in Bayesian Optimisation? . Journal of Machine Learning Research.

Grosnit, A., Cowen-Rivers, A.I., Tutunov, R., Griffiths, R.-R., Wang, J., Bou-Ammar, H.(2021). Are we forgetting about compositional optimisers in bayesian optimisation? . Journal of Machine Learning Research. 22.

Ryan-Rhys Griffiths, Ajmal Aziz, Edward Elson Kosasih, Alexandra Brintrup (2021). Data Considerations in Graph Representation Learning for Supply Chain Networks . ArXiv.

Griffiths, Ryan-Rhys, Schwaller, Philippe, Lee, Alpha A (2021). Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design . ArXiv.

Griffiths, Ryan-Rhys, Jiang, Jiachen, Buisson, Douglas J. K., Wilkins, Dan, Gallo, Luigi C., Ingram, Adam, Lee, Alpha A., Grupe, Dirk, Kara, Erin, Parker, Michael L., et al. (2021). Modeling the Multiwavelength Variability of Mrk 335 Using Gaussian Processes . The Astrophysical Journal.

Zagar, C., Griffith, R.-R., Podgornik, R., Kornyshev, A.A.(2020). On the voltage-controlled assembly of nanoparticle arrays at electrochemical solid/liquid interfaces . Journal of Electroanalytical Chemistry. 872.

Ryan-Rhys Griffiths, Jos&#233; Miguel Hern&#225;ndez-Lobato(2020). Constrained Bayesian optimization for automatic chemical design using variational autoencoders . Chemical Science. 11. (2). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 577--586. Royal Society of Chemistry ({RSC})

Zagar, Cristian, Griffith, Ryan-Rhys, Podgornik, Rudolf, Kornyshev, Alexei A. (2020). On the voltage-controlled assembly of nanoparticle arrays at electrochemical solid/liquid interfaces . Journal of Electroanalytical Chemistry.

Griffiths, Ryan-Rhys, B. Moss, Henry (2020). Gaussian Process Molecule Property Prediction with FlowMO . ArXiv.

Griffiths, Ryan-Rhys, Cheng, Bingqing, Wengert, Simon, Kunkel, Christian, Stenczel, Tamas, Zhu, Bonan, Deringer, Volker L., Bernstein, Noam, Margraf, Johannes T., Reuter, Karsten, et al. (2020). Mapping Materials and Molecules . Accounts of Chemical Research.

Cheng, B., Griffiths, R.-R., Wengert, S., Kunkel, C., Stenczel, T., Zhu, B., Deringer, V.L., Bernstein, N., Margraf, J.T., Reuter, K., et al.(2020). Mapping materials and molecules . Accounts of Chemical Research. 53. (9). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 1981-1991.

Griffiths, Ryan-Rhys, Bourached, Anthony, Gray, Robert, Jha, Ashwani, Nachev, Parashkev (2020). Generative Model-Enhanced Human Motion Prediction . ArXiv.

Griffiths, R.-R., Hern&#225;ndez-Lobato, J.M.(2020). Constrained Bayesian optimization for automatic chemical design using variational autoencoders . Chemical Science. 11. (2). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 577-586.

Griffiths, Ryan-Rhys, Grosnit, Antoine, I. Cowen-Rivers, Alexander, Tutunov, Rasul, Wang, Jun, Bou-Ammar, Haitham (2020). Are we Forgetting about Compositional Optimisers in Bayesian Optimisation? . ArXiv.

Ryan-Rhys Griffiths, Aditya Thawani, Arian Jamasb, Anthony Bourached, Penelope Jones, William McCorkindale, Alexander Aldrick, Alpha Lee (2020). The Photoswitch Dataset: A Molecular Machine Learning Benchmark for the Advancement of Synthetic Chemistry . ArXiv.

Griffiths, Ryan-Rhys, Hernandez-Lobato, Jose Miguel (2020). Constrained Bayesian optimization for automatic chemical design using variational autoencoders . Chemical Science.

Griffiths, Ryan-Rhys, A Grant, James, Boukouvalas, Alexis, S Leslie, David, Vakili, Sattar, Munoz de Cote, Enrique (2019). Adaptive Sensor Placement for Continuous Spaces . ArXiv.

Griffiths, Ryan-Rhys, Hern&#225;ndez-Lobato, Jos&#233; Miguel (2019). Constrained Bayesian Optimization for Automatic Chemical Design . ArXiv.

Griffiths, Ryan-Rhys, Aldrick, Alexander A, Garcia-Ortegon, Miguel, Lalchand, Vidhi R, Lee, Alpha A (2019). Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation . ArXiv.

Griffiths, Ryan-Rhys (2017). A Theory of a Self-Assembling Electrovariable Smart Mirror . ArXiv.

OTHER

Frieder, S., Pinchetti, L., Griffiths, R.-R., Salvatori, T., Lukasiewicz, T., Petersen, P.C., Chevalier, A., Berner, J.(2023). Mathematical Capabilities of ChatGPT . arXiv.

Ranković, B., Griffiths, R.-R., Moss, H.B., Schwaller, P.(2022). Bayesian optimisation for additive screening and yield improvements in chemical reactions – beyond one-hot encodings . ChemRxiv.

Griffiths, R.-R., Klarner, L., Moss, H., Ravuri, A., Truong, S., Stanton, S., Tom, G., Rankovic, B., Du, Y., Jamasb, A., et al.(2022). GAUCHE: A Library for Gaussian Processes in Chemistry . arXiv.

Kell, G., Griffiths, R.-R., Bourached, A., Stork, D.G.(2022). Extracting associations and meanings of objects depicted in artworks through bi-modal deep networks . arXiv.

Aziz, A., Kosasih, E.E., Griffiths, R.-R., Brintrup, A.(2021). Data considerations in graph representation learning for supply chain networks . arXiv.

Griffiths, R.-R., Schwaller, P., Lee, A.A.(2021). Dataset bias in the natural sciences: A case study in chemical reaction prediction and synthesis design . arXiv.

Stork, D.G., Bourached, A., Cann, G.H., Griffiths, R.-R.(2021). Computational identification of significant actors in paintings through symbols and attributes . arXiv.

Grosnit, A., Griffiths, R.-R., Zhu, L., Wang, J., Maraval, A.M., Cowen-Rivers, A.I., Lyu, W., Peters, J., Tutunov, R., Yang, L., et al.(2021). High-dimensional bayesian optimisation with variational autoencoders and deep metric learning . arXiv.

Bourached, A., Cann, G.H., Griffiths, R.-R., Stork, D.G.(2021). Recovery of underdrawings and ghost-paintings via style transfer by deep convolutional neural networks: A digital tool for art scholars . arXiv.

Cann, G.H., Bourached, A., Griffiths, R.-R., Stork, D.G.(2021). Resolution enhancement in the recovery of underdrawings via style transfer by generative adversarial deep neural networks . arXiv.

Bourached, A., Gray, R., Guan, X., Griffiths, R.-R., Jha, A., Nachev, P.(2021). Hierarchical Graph-Convolutional Variational Autoencoding for Generative Modelling of Human Motion . arXiv.

Griffiths, R.-R., Jiang, J., Buisson, D.J.K., Wilkins, D., Gallo, L.C., Ingram, A., Lee, A.A., Grupe, D., Kara, E., Parker, M.L., et al.(2021). Modelling the multiwavelength variability of Mrk 335 using Gaussian processes . arXiv.

Ryan-Rhys Griffiths, Aditya Thawani, Arian Jamasb, Anthony Bourached, Penelope Jones, William McCorkindale, Alexander Aldrick, Alpha Lee (2020). The Photoswitch Dataset: A Molecular Machine Learning Benchmark for the Advancement of Synthetic Chemistry .

Moss, H.B., Griffiths, R.-R.(2020). Gaussian Process Molecular Property Prediction with FlowMO . arXiv.

Cowen-Rivers, A.I., Lyu, W., Tutunov, R., Wang, Z., Grosnit, A., Griffiths, R.R., Maravel, A.M., Jianye, H., Wang, J., Peters, J., et al.(2020). HEBO: Pushing The Limits of Sample-Efficient Hyperparameter Optimisation . arXiv.

Thawani, A.R., Griffiths, R.-R., Jamasb, A.R., McCorkindale, W., Bourached, A., Aldrick, A., Jones, P., Lee, A.A.(2020). The photoswitch dataset: A molecular machine learning benchmark for the advancement of synthetic chemistry . arXiv.

Grosnit, A., Cowen-Rivers, A.I., Tutunov, R., Griffiths, R.-R., Wang, J., Bou-Ammar, H.(2020). Are we forgetting about compositional optimisers in bayesian optimisation? . arXiv.

Thawani, A.R., Griffiths, R.-R., Jamasb, A.R., McCorkindale, W., Bourached, A., Aldrick, A., Jones, P., Lee, A.A.(2020). The Photoswitch Dataset: A Molecular Machine Learning Benchmark for the Advancement of Synthetic Chemistry . ChemRxiv.

Griffiths, R.-R., Aldrick, A.A., Garcia-Ortegon, M., Lalchand, V., Lee, A.A.(2019). Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation . arXiv.

Grant, J.A., Boukouvalas, A., Griffiths, R.-R., Leslie, D.S., Vakili, S., De Cote, E.M.(2019). Adaptive sensor placement for continuous spaces . arXiv.

Griffiths, R.-R., Schwaller, P., Lee, A.A.(2018). Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design . ChemRxiv.

Griffiths, R.-R., Hern&#225;ndez-Lobato, J.M.(2017). Constrained bayesian optimization for automatic chemical design . arXiv.

CONFERENCE PAPER

Kell, G., Griffiths, R.-R., Bourached, A., Stork, D.G.(2022). Extracting associations and meanings of objects depicted in artworks through bi-modal deep networks . IS and T International Symposium on Electronic Imaging Science and Technology. 34. (13).

Cann, G.H., Bourached, A., Griffiths, R.-R., Stork, D.G.(2021). Resolution enhancement in the recovery of underdrawings via style transfer by generative adversarial deep neural networks . IS and T International Symposium on Electronic Imaging Science and Technology. 2021. (14).

Bourached, A., Cann, G.H., Griffiths, R.-R., Stork, D.G.(2021). Recovery of underdrawings and ghost-paintings via style transfer by deep convolutional neural networks: A digital tool for art scholars . IS and T International Symposium on Electronic Imaging Science and Technology. 2021. (14).

Stork, D.G., Bourached, A., Cann, G.H., Griffiths, R.-R.(2021). Computational identification of significant actors in paintings through symbols and attributes . IS and T International Symposium on Electronic Imaging Science and Technology. 2021. (14).

Grant, J.A., Boukouvalas, A., Griffiths, R.-R., Leslie, D.S., Vakili, S., Munoz de Cote, E.(2019). Adaptive sensor placement for continuous spaces . 36th International Conference on Machine Learning, ICML 2019. 2019-June. Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 4264-4277.

PREPRINT

Ryan-Rhys Griffiths, Aditya Raymond Thawani, Arian Jamasb, Anthony Bourached, Penelope Jones, William McCorkindale, Alexander Aldrick, Alpha Lee (2020). The Photoswitch Dataset: A Molecular Machine Learning Benchmark for the Advancement of Synthetic Chemistry .