Training in Data Science & Machine Learning
for Health, Disease and Bioscience

Geraint is the lead on the L2D project and is Director of SysMIC, a parent project to L2D that focuses on mathematical, computational and statistical approaches to bioscience research. He is based in the Department of Cell & Developmental Biology at UCL.

Geraint is Deputy Director of the UKRI-BBSRC LIDo DTP PhD programme and leads on Research and Training organisation for the UK Food Systems CDT. He also chairs the UKRI-BBSRC People and Talent Strategy Advisory Panel.

Originally trained as a biochemist, Geraint has studied intracellular signal transduction processes for a long time. Over the years he developed expertise in cell biology and trained in protein chemistry and structural biology at post-graduate level. He also completed a degree in mathematics and applies this understanding to a range of projects, most recently to biochemical systems modelling and hyperspectral Raman imaging of cells and tissues in health and disease.

Gerold leads the development and delivery of the L2D course. He is with the Department of Cell & Developmental Biology at University College London. His focus is on the Python programming language and all flavours of machine learning as applied to biomedical and clinical data.

He has a background in biochemistry and nonlinear dynamics. In his research, he analyses, models, and tries to control epileptic seizures in particular and human brain dynamics in general.

Saba has obtained her PhD in Structural Bioinformatics from University College London in 2017 and then worked as a Senior Bioinformatician at Cancer Research UK, Manchester Institute. In past, she has collaborated with industry and universities and contributed in successful completion of several projects.

Recently, she joined L2D team as an Associate Lecturer at University College London, within the Division of Biosciences. In L2D project, she will be responsible for designing the high quality contents of a training course on application of data science and machine learning / AI techniques in health sciences for UK wide academia and industry. She has diverse computational skills in scripting, programming, data analysis and hands-on experience with big data.

Adam is an Associate Lecturer at University College London, within the Division of Biosciences. Adam’s research interests centre around studying the evolution of endogenous retroviruses, and their effects on the genomes of their hosts, exploring these using bioinformatic techniques and technologies.

Adam has a PhD in retroviral evolution, from Imperial College London, where he completed all his studies, and previously worked as a Teaching Fellow.

Jade is a research technician at the Meyer Lab in King’s College London where she works on genetic models of epilepsy in larval zebrafish with Dr. Richard Rosch (dynamic-brains.com).

Jade joined the LearnToDiscover.ai team as the Social Media Manager and Content Creator, which lies at the intersection between her artistic creativity and her passion for data science. Jade works part time. Website: jyslau.co

Phil is a lecturer at UCL teaching in the Division of Biosciences at UCL and is involved in the UCL International Summer School in Data Science alongside courses in Computational Biology.

Phil also works as the developer on the SysMIC course and has developed software in the biotech industry. Phil has a PhD in Physics, has Qualified Teacher Status and is interested is innovations in eLearning.

Hannah has been with the Division of Biosciences at UCL for 8 years and prior to this had 10 years experience in various administration roles. Hannah has a Higher National Certificate in Business, covering marketing, management accounting, HR and general business environments and behaviours.

Hannah works 20 hours per week.

Bethany has a background in accounting and has over 7 years experience working in Administration. Bethany works 16.5 hours per week.

Christine is a Professor of Bioinformatics within the Division of Biosciences at University College London.

Christine leads a group who develop computational methods for classifying proteins into evolutionary families, exploiting structural and sequence data to do this. A major interest is in the development of algorithms to recognise very distant relationships. The group have developed methods for predicting protein functions and functional networks. These methods are being exploited by experimental groups in several BBSRC funded collaborations that are researching proteins involved in ageing, B-cell signalling and plastic degradation.

Christine organises a course on Bioinformatics for 3rd year undergraduates in the Department of Structural and Molecular Biology and has co-edited a textbook on Bioinformatics (Bioinformatics: Genes, Proteins and Computers) for undergraduates and PhD students.

Chris is a Professor of Systems and Synthetic Biology within the Division of Biosciences at University College London.

Research includes: (1) mutational processes, focusing on the mechanistic modelling of genetic and sequence data to try to infer the biological processes that shape genomes; (2) microbiome engineering, developing tools to aid in the quantitative understanding of dynamics within microbial communities; (3) robust gene regulatory networks, using model space exploration from Bayesian statistics, Chris’ group have developed and parameterised a number of models of morphogenesis in collaboration with developmental biologists.

Caswell is a Professor based in the Department of Cell and Developmental Biology at UCL. His lab uses a range of theoretical, computational, and experimental techniques to study the brain with a particular focus on the spatial-memory system.

Romain is an MRC Skills Development Research Fellow within the Medical Research Council Laboratory for Molecular Cell Biology.

Romain is interested in developing and applying advanced microscopy techniques to decipher functional and structural organisation of life at multiple scales. For this, Romain develops analytical and optical tools.

David is a Senior Research Software Developer within Research IT Services at University College London.

David supports researchers to build and maintain first-class scientific software. As part of Research IT Services, David trains researchers in basic & advanced research computing through a variety of courses, some of which are now fully on-line.

Sanaz is a Senior Research Software Developer – Data Science Specialist within Research IT Services at University College London.

Sanaz leads the AI Studio Service, which is available to both novice and experienced data scientists. For researchers who are experienced programmers and machine learning savvy, the service provides advice on issues such as model selection, parameter tuning, or achieving a higher performance. For researchers new to data science, the AI Studio provides a consultation service that can help determine what sort of predictions and analysis are possible from their data and what type of data they need to collect.

Andrew is a Professor of Bioinformatics and Computational Biology within the Division of Biosciences at University College London.

Andrews main interests are sequence, structure and function of antibodies, the effects of mutation on protein structure and function, protein modelling and software development for Bioinformatics.

Andrew has acted as a consultant to pharmaceutical companies and as an advisor or expert witness in several patent disputes. In 2016, Andrew joined the World Health Organization International Nonproprietary Names committee as an advisor on naming and annotation of antibody-based drugs

Adrian is co-PI on L2D and a Professor of Computational Biology in the Department of Biological Sciences at Birkbeck. He has been the Director of Birkbeck's highly-regarded MSc Bioinformatics course since 2006.

Having completed his PhD in machine learning at UCL, Adrian spent six years doing post-doctoral research in computational biology before joining Birkbeck in 2002. His research group focuses on the application of computational methods, including deep learning techniques, to challenges in the area of human adaptive immunity.