is a team lead and a senior researcher in machine learning and clinical artificial intelligence.
is a postdoctoral researcher at the Department of Psychiatry, University of Oxford. Qiang's expertise is in computer vision, data engineering and deep learning for personalised medicine. He works on the development of neural network frameworks for fluorescent microscopy and cellular phenotyping and personalised recommendations of medications in depression and dementia. Qiang holds PhD in Computer Science from the University of Essex.
is a research assistant at the Department of Psychiatry, University of Oxford. Piotr works on the development of novel deep learning and machine learning methods for high-content image analysis and to characterise cell health phenotypes and morphology profiling. Piotr holds MSc in Mathematics from the University of Oxford and his dissertation was on deep learning methods for image-based cell profiling, autoencoders, generative models and pattern recognition in mechanism of action of candidate drug for Alzheimer’s disease.
is a research assistant at the Department of Psychiatry, University of Oxford. Yi works on high-content image recognition, geometric deep learning (GDL) and graph neural networks (GNN). His particular interest is in the application of GDL and GNN to de novo drug discovery and phenotypic screening. Yi recieved BSc (University of Manchester) and MSc (University if Oxford) both in Mathematics . His dissertation was on matrix perturbation theory, numerical linear algebra and the theory of random walks.
Doctoral students (co-supervised)
is a DPhil students in Health Data Science at the Big Data Institute and the Department of Psychiatry, University of Oxford. Niall has a background in experimental psychology, with a Bachelors from the University of Plymouth, followed by a Masters by Research at the University of Bristol. Niall's overarching interests focus on the application and uptake of machine and deep learning in the realm of mental health, with the aid of big data and wearable devices.
Niall works on Natural Language Processing, prompt ingineering and explainable methods for clinical decision support with free-text data from UK-CRIS and MIMIC. He is developing method to generate abstractive salient reasons derived from texts to underpin the decisions made by models.
Master's students (2021-2022)
Tianyang (Derek) Li
is currently a MMath Mathematics student at the Mathematical Institute, University of Oxford. Tianyang‘s research interests lie within natural language processing and statistical analysis and works on reducing the self-attention complexity in Transformers architectures by using low-rank tensor approximations. The aim of Tianyang‘s work is to allow transformers-based models, in particular for NLP tasks, to capture long range dependencies in electronic health records to support clinical decision making.
is currently a MMath Mathematics student at the Mathematical Institute, University of Oxford. Jacob's background is in machine learning and data science, with a particular interest in a hospital the scalability and accuracy of machine learning systems. Jacob's research is dedicated to modelling the path of a patient over the course of a treatment period. In particular I am excited about using neural controlled differential equations to try and understand the dynamics of a care plan over time.
is currently a MMath Mathematics student at the Mathematical Institute, University of Oxford. Haobo has a background in statistics and statistical machine learning. Haobo's research is dedicated to learning models for actionable recourse in healthcare. He uses methods of adversarial neural network learning and optimisation to derive counterfactual explanations for decision support systems.
is currently a MMath Mathematics student at the Mathematical Institute, University of Oxford. Ziming has a background in computational statistics, machine learning and Natural Language Processing. Ziming’s research is dedicated to a new approach for zero-short learning with very large pre-trained language models – the Prompt Learning (PL) paradigm. He explores whether PL can be adapted to downstream analytical tasks to support clinical decision making, derive explanations and how to optimise the learning process.
Master's students (2020-2021)
studied towards Part C dissertation at the Mathematical Institute, University of Oxford. Yuchen's research is focused on statistical analysis and machine learning modelling for drug discovery for Alzhemer's disease. He analyses glial fluorecent cell images generated using the Cell Painting method with the aim to understand the mechanism of action of candidate drug compunds with higher accuracy than existing approaches.
Now at Squarepoint Capital.
studied towards Part C dissertation at the Mathematical Institute, University of Oxford. Piotr's research focused on the development of deep learning convolutional models for analysing fluorescent microscopy images and the development of fast screening tool for morphological changes of cells treated with various compunds.
Now a machine learning researcher at the University of Oxford.
studied towards Part C dissertation at the Mathematical Institute, University of Oxford. Wooseok's research is focused on the interface between deep learning methods for fluorecent microscopy image segmentation and topological data analysis. He developed a novel approach to derive persistence silhouettes and landscape signatures to quantify the geometry of cells' nucleui for downstream computational tasks.
Now at Vuno
studied towards the MSc Mathematical Sciences dissertation at the Mathematical Institute, University of Oxford. Alec's research is focused on artificial cell painting, whereas he develops deep learning methods to translate fluorescent information from stained images to non-stained ones. His work can speed up the statistical analyses of image-derived features and help biologists to study the morphological changes in cells by using bright-field images.
studied towards Part C dissertation at the Mathematical Institute, University of Oxford. Pawel's research is focused on developing new methods for generating high-resolution fluorecent images through adversarial training.
2019 Adam (Xinyu) Yang, "Deep learning for drug discovery in Alzheimer’s disease" (Mathematical Institute). Now a PhD student at Bristol Computational Neuroscience Unit. University of Bristol, UK
2018 Rattana Pukdee, "The signature-based pricing models" (Mathematical Institute). Now a PhD student in the Machine Learning Department at Carnegie Mellon University, USA.
2018 William Stone, "Deep learning for estimation of neuronal health" (Mathematical Institute). Now a Geophysicist at Compagnie Générale de Géophysique (CGG).
2018 Maximilian Hofer, "Few-shot Learning for Named Entity Recognition in Medical Text", (Computer Science). Now a PhD student at the Swiss Federal Institute of Technology in Lausanne