Nicole Wheeler n.wheeler@bham.ac.uk

n.wheeler@bham.ac.uk

Position

Data Scientist, Bioinformatician

Affiliation

Wellcome Sanger Institute

Keywords

Machine Learning, Bacterial Evolution, Antimicrobial Resistance, Genomics

I work as a data scientist, taking large amounts of data from the real world, and using it to discover rules about how the world works.

I have developed expertise in machine learning, genomics, antibiotic resistance and infectious disease. I develop machine learning (ML) algorithms for identifying antibiotic resistant infections. These algorithms can be used to monitor antibiotic resistance in public health surveillance systems, to improve the decisions we make about when and how to prescribe antibiotics, and could in the future be used by doctors to prescribe the right antibiotics to treat infections.

The data I use to build these algorithms often comes from wealthier communities linked with large universities. ML algorithms are sensitive to bias, so I am working to test whether this data collection strategy lowers performance in low-income settings, and develop ways to prevent this. This includes developing methods to correct for this bias, and developing capacity for low-income areas to collect and contribute their own data.

I also work to educate the public, policy makers and experts on how to prevent the spread of superbugs and produce trustworthy AI products. I achieve this through writing, speaking and designing activities which encourage people to explore why things go wrong, in nature and in ML.

My work allows me to collaborate with people all over the world from biology, computing, physics, mathematics, public health, global development and policy. This diverse network allows me to bring together key partners to make progress on pressing global issues.

writing: https://medium.com/@nwheeler443 speaking: https://quadram.ac.uk/events/dr-nicole-wheeler/ designing activities: https://medium.com/@nwheeler443/public-engagement-antibiotic-resistance-89cec2477e0 My work: https://nwheeler443.wixsite.com/bioinformatics

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StepWideStepWide showcases the profiles of early career cis and trans women researchers from the University of Cambridge, UK.



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About

This website

This website showcases the profiles of early career cis and trans women researchers from the University of Cambridge, UK, and affiliated institutions. It originated as part of StepWide, a leadership programme that aims to support the next generation of female researchers.

We hope that by making the expertise and stories of early career women researchers more visible (and searchable!), will highlight how much they contribute to the research that is done in the University and affiliated institutions.

Who is it for

This website is designed for a wide audience, be it other researchers looking for particular expertise for a collaboration; the media looking for experts; those that are simply curious about what type of research is done in Cambridge, or those trying to get a clearer idea of what a ‘typical’ woman researcher in this years old institution does (there is no ‘typical’!).

The StepWide programme

StepWide was designed by 3 postdocs at Cambridge (see below for more on Marta, Laura and Cemre). It aims to support female postdoctoral researchers at the University of Cambridge, UK, from any discipline, who feel that taking a step into leadership is not for them. The programme is designed to suit both early and more senior postdocs, providing them with the skills to challenge the current ideas of what a leader is, learn how to raise their public profiles, as well as a close and supportive network of peer-to-peer female postdocs.

StepWide ran for the first time in 2019/2021, and we are currently running a new series of workshops in 2022/2023. We will post updates here when applications open for its next run.

Founders

Laura, Marta and Cemre (left to right on the photo) met at The Postdocs of Cambridge (PdOC) Society, at the University of Cambridge, UK. When the Researcher Development (RD) Pitch Competition was announced in late 2018, they felt this provided the ideal opportunity to work together to develop a leadership programme for women postdocs. They saw a gap in the current leadership RD provision, with a lack of opportunities that challenge current leadership views. Their proposal was successful and obtained funding for a one year pilot, giving rise to the StepWide programme.

Laura Fachal is a Senior Staff Scientist at Wellcome Sanger Institute. She earned her BS in Veterinary, MSc in Biotechnology and PhD from University of Santiago de Compostela, Spain. She completed her postdoc at the Centre for Cancer Genetic Epidemiology, University of Cambridge. She is also a Research Associate at Lucy Cavendish College.

Marta Costa is a Senior Research Associate at the Department of Zoology. She did her undergrad in Biology in Lisbon, Portugal, followed by an MSc in Neuroscience at UCL in London. She then moved to Cambridge for her PhD, followed by a postdoc. She is also a Research Associate at Lucy Cavendish College.

Cemre Ustunkaya was a Post-doctoral Research Associate at the McDonald Institute for Archaeological Research. She earned her BSc in Biological Science, followed by an MSc in Archaeometry at Middle East Technical University, Turkey. She later moved to Australia for her PhD in Archaeology at The University of Queensland. She is also a postdoc affiliate at Newnham College.

Funding

Thanks and funding

StepWide was funded by the Researcher Development Pitch Competition which included support from the Researcher Development Programme, The Postdocs of Cambridge (PdOC) Society, the Postdoc Academy, the Postdoc Chairs’ Network and the Careers Service at the University of Cambridge. We are very thankful for their support. We would also like to thank Alba Gómez for her expert support with the first version of the website, and to Arian Jamasb for redesigning and implementing the newest version of this website. Finally, we thank Natacha Wilson and Rebecca Nestor for the advice and support they provided for the development of the workshops.

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