Fergus Imrie

Florence Nightingale Bicentenary Fellow, University of Oxford

fergus.imrie [AT] stats.ox.ac.uk

About me

I am a Florence Nightingale Bicentenary Fellow at the University of Oxford in the Department of Statistics. My research focuses on developing machine learning methods and techniques for medicine and drug discovery. I am particularly interested in approaches for designing potent, selective small molecules using structure-based techniques, experimental design and decision-making in drug discovery, and how to learn efficiently from small quantities of often noisy data.

If you're a current DPhil student at Oxford and would like to do a rotation project with me, please get in touch. More generally, if you'd like to collaborate, please reach out.

From 2021-2024, I was a postdoctoral scholar at the University of California, Los Angeles (UCLA) in the Department of Electrical and Computer Engineering, working with Mihaela van der Schaar. Before that, I completed my DPhil (PhD) at the University of Oxford in the Department of Statistics in the Oxford Protein Informatics Group (OPIG), supervised by Charlotte Deane. My studentship was supported by an iCASE award with Exscientia, supervised by Anthony Bradley. My thesis explored deep learning approaches for pre-clinical drug discovery, with an emphasis on generative molecular design.

Recent News

Publications

Most recent publications on Google Scholar.
indicates equal contribution.

Automated Ensemble Multimodal Machine Learning for Healthcare

Fergus Imrie, Stefan Denner, Lucas S Brunschwig, Klaus Maier-Hein, Mihaela van der Schaar

arXiv (Preprint). 2024.

You Can't Handle The (Dirty) Truth: Data-Centric Insights Improve Pseudo-Labeling

Nabeel Seedat, Nicolas Huynh, Fergus Imrie, Mihaela van der Schaar

Journal of Data-Centric Machine Learning Research. 2024.

Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AI

Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar

International Conference on Learning Representations (ICLR). 2024.

A Neural Framework for Generalized Causal Sensitivity Analysis

Dennis Frauen, Fergus Imrie, Alicia Curth, Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar

International Conference on Learning Representations (ICLR). 2024.

Navigating Data-Centric Artificial Intelligence with DC-Check: Advances, Challenges, and Opportunities

Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar

IEEE Transactions on Artificial Intelligence. 2023.

Can you rely on your model evaluation? Improving model evaluation with synthetic test data

Nabeel Seedat, Boris van Breugel, Fergus Imrie, Mihaela van der Schaar

Neural Information Processing Systems (NeurIPS). 2023.

Assessing eligibility for lung cancer screening using parsimonious ensemble machine learning models: A development and validation study

Thomas Callender, Fergus Imrie, Bogdan Cebere, Nora Pashayan, Neal Navani, Mihaela van der Schaar, Sam M Janes

PLOS Medicine. 2023.

Multiple stakeholders drive diverse interpretability requirements for machine learning in healthcare

Fergus Imrie, Robert Davis, Mihaela van der Schaar

Nature Machine Intelligence. 2023.

AutoPrognosis 2.0: Democratizing Diagnostic and Prognostic Modeling in Healthcare with Automated Machine Learning

Fergus Imrie, Bogdan Cebere, Eoin F McKinney, Mihaela van der Schaar

PLOS Digital Health. 2023.

Differentiable and Transportable Structure Learning

Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar

International Conference on Machine Learning (ICML). 2023.

Testing the Limits of SMILES-based De Novo Molecular Generation with Curriculum and Deep Reinforcement Learning

Maranga Mokaya, Fergus Imrie, Willem P van Hoorn, Aleksandra Kalisz, Anthony R Bradley, Charlotte M Deane

Nature Machine Intelligence. 2023.

TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization

Alan Jeffares, Tennison Liu, Jonathan Crabbé, Fergus Imrie, Mihaela van der Schaar

International Conference on Learning Representations (ICLR). 2023.

Improving Adaptive Conformal Prediction Using Self-Supervised Learning

Nabeel Seedat, Alan Jeffares, Fergus Imrie, Mihaela van der Schaar

International Conference on Artificial Intelligence and Statistics (AISTATS). 2023.

To Impute or not to Impute? Missing Data in Treatment Effect Estimation

Jeroen Berrevoets, Fergus Imrie, Trent Kyono, James Jordon, Mihaela van der Schaar

International Conference on Artificial Intelligence and Statistics (AISTATS). 2023.

SurvivalGAN: Generating Time-to-Event Data for Survival Analysis

Alexander Norcliffe, Bogdan Cebere, Fergus Imrie, Pietro Lio, Mihaela van der Schaar

International Conference on Artificial Intelligence and Statistics (AISTATS). 2023.

Composite Feature Selection using Deep Ensembles

Fergus Imrie, Alexander Norcliffe, Pietro Lio, Mihaela van der Schaar

Neural Information Processing Systems (NeurIPS). 2022.

Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations

Nabeel Seedat, Fergus Imrie, Alexis Bellot, Zhaozhi Qian, Mihaela van der Schaar

International Conference on Machine Learning (ICML). 2022.

Self-Supervision Enhanced Feature Selection with Correlated Gates

Changhee Lee, Fergus Imrie, Mihaela van der Schaar

International Conference on Learning Representations (ICLR). 2022. [Spotlight]

Explaining Latent Representations with a Corpus of Examples

Jonathan Crabbé, Zhaozhi Qian, Fergus Imrie, Mihaela van der Schaar

Neural Information Processing Systems (NeurIPS). 2021. [Spotlight]

Closing the loop in medical decision support by understanding clinical decision-making: A case study on organ transplantation

Yuchao Qin, Fergus Imrie, Alihan Hüyük, Daniel Jarrett, Alexander Gimson, Mihaela van der Schaar

Neural Information Processing Systems (NeurIPS). 2021.

Deep generative design with 3D pharmacophoric constraints

Fergus Imrie, Thomas E Hadfield, Anthony R Bradley, Charlotte M Deane

Chemical Science. 2021.

Generating property-matched decoy molecules using deep learning

Fergus Imrie, Anthony R Bradley, Charlotte M Deane

Bioinformatics. 2021.

Deep Generative Models for 3D Linker Design

Fergus Imrie, Anthony R Bradley, Mihaela van der Schaar, Charlotte M Deane

Journal of Chemical Information and Modeling. 2020.

Protein Family-Specific Models using Deep Neural Networks and Transfer Learning Improve Virtual Screening and Highlight the Need for More Data

Fergus Imrie, Anthony R Bradley, Mihaela van der Schaar, Charlotte M Deane

Journal of Chemical Information and Modeling. 2018.

Automated Ensemble Multimodal Machine Learning for Healthcare

Fergus Imrie, Stefan Denner, Lucas S Brunschwig, Klaus Maier-Hein, Mihaela van der Schaar

arXiv (Preprint). 2024.

You Can't Handle The (Dirty) Truth: Data-Centric Insights Improve Pseudo-Labeling

Nabeel Seedat, Nicolas Huynh, Fergus Imrie, Mihaela van der Schaar

Journal of Data-Centric Machine Learning Research. 2024.

Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AI

Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar

International Conference on Learning Representations (ICLR). 2024.

A Neural Framework for Generalized Causal Sensitivity Analysis

Dennis Frauen, Fergus Imrie, Alicia Curth, Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar

International Conference on Learning Representations (ICLR). 2024.

Machine Learning with Requirements: A Manifesto

Eleonora Giunchiglia, Fergus Imrie, Mihaela van der Schaar, Thomas Lukasiewicz

Neurosymbolic Artificial Intelligence. 2024.

Navigating Data-Centric Artificial Intelligence with DC-Check: Advances, Challenges, and Opportunities

Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar

IEEE Transactions on Artificial Intelligence. 2023.

Can you rely on your model evaluation? Improving model evaluation with synthetic test data

Nabeel Seedat, Boris van Breugel, Fergus Imrie, Mihaela van der Schaar

Neural Information Processing Systems (NeurIPS). 2023.

Redefining Digital Health Interfaces with Large Language Models

Fergus Imrie, Paulius Rauba, Mihaela van der Schaar

arXiv (Preprint). 2023.

Assessing eligibility for lung cancer screening using parsimonious ensemble machine learning models: A development and validation study

Thomas Callender, Fergus Imrie, Bogdan Cebere, Nora Pashayan, Neal Navani, Mihaela van der Schaar, Sam M Janes

PLOS Medicine. 2023.

Novel Preoperative Risk Stratification using Digital Phenotyping Applying a Scalable Machine Learning Approach

Pascal Laferriere-Lanlgois, Fergus Imrie, Marc-Andre Geraldo, Theodora Wingert, Nadia Lahrichi, Mihaela van der Schaar, Maxime Cannesson

Anesthesia & Analgesia. 2023.

Multiple stakeholders drive diverse interpretability requirements for machine learning in healthcare

Fergus Imrie, Robert Davis, Mihaela van der Schaar

Nature Machine Intelligence. 2023.

AutoPrognosis 2.0: Democratizing Diagnostic and Prognostic Modeling in Healthcare with Automated Machine Learning

Fergus Imrie, Bogdan Cebere, Eoin F McKinney, Mihaela van der Schaar

PLOS Digital Health. 2023.

Differentiable and Transportable Structure Learning

Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar

International Conference on Machine Learning (ICML). 2023.

Testing the Limits of SMILES-based De Novo Molecular Generation with Curriculum and Deep Reinforcement Learning

Maranga Mokaya, Fergus Imrie, Willem P van Hoorn, Aleksandra Kalisz, Anthony R Bradley, Charlotte M Deane

Nature Machine Intelligence. 2023.

TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization

Alan Jeffares, Tennison Liu, Jonathan Crabbé, Fergus Imrie, Mihaela van der Schaar

International Conference on Learning Representations (ICLR). 2023.

Improving Adaptive Conformal Prediction Using Self-Supervised Learning

Nabeel Seedat, Alan Jeffares, Fergus Imrie, Mihaela van der Schaar

International Conference on Artificial Intelligence and Statistics (AISTATS). 2023.

To Impute or not to Impute? Missing Data in Treatment Effect Estimation

Jeroen Berrevoets, Fergus Imrie, Trent Kyono, James Jordon, Mihaela van der Schaar

International Conference on Artificial Intelligence and Statistics (AISTATS). 2023.

SurvivalGAN: Generating Time-to-Event Data for Survival Analysis

Alexander Norcliffe, Bogdan Cebere, Fergus Imrie, Pietro Lio, Mihaela van der Schaar

International Conference on Artificial Intelligence and Statistics (AISTATS). 2023.

Composite Feature Selection using Deep Ensembles

Fergus Imrie, Alexander Norcliffe, Pietro Lio, Mihaela van der Schaar

Neural Information Processing Systems (NeurIPS). 2022.

Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations

Nabeel Seedat, Fergus Imrie, Alexis Bellot, Zhaozhi Qian, Mihaela van der Schaar

International Conference on Machine Learning (ICML). 2022.

Incorporating Target-Specific Pharmacophoric Information into Deep Generative Models for Fragment Elaboration

Thomas E Hadfield, Fergus Imrie, Andy Merritt, Kristian Birchall, Charlotte M Deane

Journal of Chemical Information and Modeling. 2022.

Self-Supervision Enhanced Feature Selection with Correlated Gates

Changhee Lee, Fergus Imrie, Mihaela van der Schaar

International Conference on Learning Representations (ICLR). 2022. [Spotlight]

Explaining Latent Representations with a Corpus of Examples

Jonathan Crabbé, Zhaozhi Qian, Fergus Imrie, Mihaela van der Schaar

Neural Information Processing Systems (NeurIPS). 2021. [Spotlight]

Closing the loop in medical decision support by understanding clinical decision-making: A case study on organ transplantation

Yuchao Qin, Fergus Imrie, Alihan Hüyük, Daniel Jarrett, Alexander Gimson, Mihaela van der Schaar

Neural Information Processing Systems (NeurIPS). 2021.

Deep generative design with 3D pharmacophoric constraints

Fergus Imrie, Thomas E Hadfield, Anthony R Bradley, Charlotte M Deane

Chemical Science. 2021.

Generating property-matched decoy molecules using deep learning

Fergus Imrie, Anthony R Bradley, Charlotte M Deane

Bioinformatics. 2021.

Virtual Screening with Convolutional Neural Networks

Fergus Imrie, Anthony R Bradley, Charlotte M Deane

Artificial Intelligence in Drug Discovery (Book Chapter). 2020.

Deep Generative Models for 3D Linker Design

Fergus Imrie, Anthony R Bradley, Mihaela van der Schaar, Charlotte M Deane

Journal of Chemical Information and Modeling. 2020.

Protein Family-Specific Models using Deep Neural Networks and Transfer Learning Improve Virtual Screening and Highlight the Need for More Data

Fergus Imrie, Anthony R Bradley, Mihaela van der Schaar, Charlotte M Deane

Journal of Chemical Information and Modeling. 2018.

Automated Ensemble Multimodal Machine Learning for Healthcare

Fergus Imrie, Stefan Denner, Lucas S Brunschwig, Klaus Maier-Hein, Mihaela van der Schaar

arXiv (Preprint). 2024.

Redefining Digital Health Interfaces with Large Language Models

Fergus Imrie, Paulius Rauba, Mihaela van der Schaar

arXiv (Preprint). 2023.

Acknowledgement
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