The Crick-Turing Biomedical Data Science Awards (BDSA) provide funding to early career data science researchers to undertake a part-time 12-month pilot collaborative research project using data generated by Crick scientists.
The projects apply data science approaches to challenges faced by biomedical investigators, under the leadership of a PI from Crick and a PI from the Turing. The aim of these pilot projects is to establish partnerships and develop results that could lead to larger funded collaborations.
Proposals were solicited through an open call in 2019 for proof of concept collaborative projects. The following proposals were successfully funded and form the first cohort of Crick-Turing BDSA’s.
Award holder: Asher Mullokandov
Asher Mullokandov is Crick-Turing Biomedical Data Science award holder and a postdoctoral associate in the Imperial College Mathematics Department, EPSRC Centre for Mathematics of Precision Healthcare. His research is focused on developing new mathematical and computational techniques to study large and complex data sets, in particular deep learning methods for graph structured data as well as machine learning interpretability tools. Asher is also interested in developing mathematical approaches for dimensionality reduction and coarse graining of high-dimensional data, using tools from graph theory, statistical physics, and statistical inference.
Asher obtained a BA in Physics and Mathematics from Columbia University, an MA in Physics from UC Santa Barbara, and PhD in Physics from Boston University, where he conducted research in applications of non-equilibrium statistical mechanics to complex systems, network theory analysis of financial systems, as well as in the application of methods from quantum information theory to the study of graphs and networks.
Project title: Integrating spatial relationships in single cell data analysis and visualisation with novel graph convolutional neural network architectures
Award holder: Aleks Domanski
Aleks Domanski completed a Wellcome-NIH joint PhD at the National Institutes of Health (USA) and University of Edinburgh investigating the circuit basis of sensory disruption in Autism. He then joined Professor Matt Jones’ lab in Bristol as a postdoc, developing innovative methods for recording, analysing and stimulating network dynamics in large populations of cortical neurons. Aleks’ interest is in the circuit computations that support flexible decision making and transfer learning. He is also a visiting scientist in Professor Flor Iacaruso’s group at the Francis Crick Institute, studying how multisensory integration in cortical circuits contributes to the fidelity of internal models of sensory experience.
Project title: Investigating population-level multisensory integration for predictive coding in the primary visual cortex
Award holder: Jeremy Pike
Jeremy Pike completed his PhD at the University of Birmingham where he developed automated image analysis workflows to quantify receptor trafficking using confocal microscopy. Subsequently he worked as an image analyst at the Cancer Research UK Cambridge Institute. Here he developed expertise in a range of analysis techniques and applied these methods to applications in cancer research, including the development of automated feedback microscopy protocols. In his current position at the Centre of Membrane Proteins and Receptors (COMPARE) Jeremy collaborates with research groups to design image analysis workflows and software, focusing on single molecule localization microscopy data.
Project title: Quantifying the spatial networks and cross-talk between bone marrow cells within acute myeloid leukaemia models
Award holder: Kaspar Märtens
Kaspar Martens has a broad interest in Statistical Machine Learning and its applications to healthcare and genomics. He is currently a Research Fellow, funded by the Crick-Turing Biomedical Data Science Award. As the recipient of the Crick-Turing award, in collaboration with Christopher Yau and Francesca Ciccarelli, he is planning to focus on developing methodology for identifying rare cell populations. Prior to this, Kaspar completed his PhD at the University of Oxford, under the supervision of Professors Christopher Yau and Chris Holmes. His PhD research focused on enabling feature-level interpretability in black-box latent variable models (such as Variational Autoencoders).
Project title: Identification of rare cell populations from single cell analysis of Imaging Mass Cytometry data