Imperial College London

DrOliverRatmann

Faculty of Natural SciencesDepartment of Mathematics

Reader in Statistics and Machine Learning for Public Good
 
 
 
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Contact

 

oliver.ratmann05 Website

 
 
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Location

 

525Huxley BuildingSouth Kensington Campus

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Summary

 

Summary

I am passionate about developing and applying scalable, interdisciplinary methods to tackle the grand challenges in global health research, and support underserved populations. I am co-leading the PANGEA HIV consortium, co- investigator and principal statistician on several studies of the Rakai Health Sciences Program, and co-founded the Global Reference Group for Children in Crisis and the Machine Learning and Global Health network. I am director of the MSc in Statistics at Imperial, and teach the Biostatistics course on the MSc. My group currently includes two post-docs, a research assistant, three PhD students, and several students and visitors. We are curious, diverse, and dynamic. We always look for people who want to grow with us, and are here to support young talents  – get in touch.

My group and I are involved in a number of multinational public health projects. See my research page for details:

Phylogenetics and Networks of Generalised HIV epidemics in Africa

Global reference group on children affected by COVID-19


Machine learning & Global Health network

Long-term impact of universal treatment and dolutegravir on population HIV virologic and incidence outcomes in Africa

HIV transmission elimination initiative Amsterdam








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I enjoy working with a really brilliant team, and within our Machine Learning & Global Health Network:

Team

  • Dr. Alex Blenkinsop (post-doc, working on estimating children who lost their parent or caregiver, branching processes, and HIV deep-sequence phylogenetics).
  • Dr. André Ribeiro Amaral (post-doc, working on estimating children who lost their parent or caregiver, scalable spatiotemporal modelling, and HIV deep-sequence phylogenetics).
  • Shozen Dan (PhD student, working on full Bayesian inference of age-specific transmission models with Stan, scalable inference with Gaussian process approximations, and high-resolution inference of human contact patterns).
  • Andrea Brizzi (PhD student, working on age-specific fatality rate models, and semi-parametric models of group-level trajectories).
  • Sydney Tucker (Research Assistant, working on estimating children who lost their parent or caregiver, clinical trial methodologies, and evidence-based psychosocial and parenting support programs)
  • Yu Chen (PhD student, working on full Bayesian inference of transmission models with Stan, and high-resolution inference of human contact patterns).
  • Students on the MSc in Statistics, the MSci in Mathematics, the MSc in Applied Computational Science and Engineering, our Mary Lister McCannon summer research fellows, and undergraduate research fellows who contributed substantially to research, especially Megan Andrews, Sofocleus Lysandros, Yining Chen, Zhaorui Zhang, Martin McManus, Vidoushee Jogarah, Imogen Kyle, Yuanrong Wang, Yixin Wang, Cathal Mills, Niamh Arthur, Victor Kolyan Merle, Douhan Wang, Zhi Ling, Xialu Zheng, Linden Graves, Yujia Luan, Eric Brine, Michael Seath.

We work closely within our Machine Learning & Global Health Network, with Christophe Fraser's group at the Big Data Institute at Oxford, Samir Bhatt's group at the University of Copenhagen, Seth Flaxman's group at the University of Oxford, Kate Grabowski's group at John's Hopkins University and Susan Hillis' team at the CDC. We are always looking for creative and brilliant people to join our team, especially if you have an excellent background in Bayesian statistics, Stan, Gaussian processes, low rank GP approximations, and viral deep sequence analysis. Please just reach out by email and describe the type of our research you find most exciting, and how you would fit.

Press

Some of our press coverage can be found here

VERY SHORT BIO

Oliver completed a Wellcome Trust funded PhD in Bayesian Statistics and Network Sciences in 2009 at Imperial College London, spent time overseas at Duke University in the U.S. as part of a Sir Henry Wellcome fellowship, and returned to Imperial in 2012. He started his own research group in 2017 with a focus on harnessing novel data streams and providing freely accessible statistical tools for public good, and informing public health interventions. Methodologically, Oliver is interested in phylodynamics, Gaussian process approximations, Stan, mathematical models of infectious disease dynamics, and survey and sampling methods.


Selected Publications

Journal Articles

Bu F, Kagaayi J, Grabowski K, et al., 2024, Inferring HIV transmission patterns from viral deep sequence data via latent typed point processes, Biometrics, Vol:80, ISSN:0006-341X

Monod M, Brizzi A, Galiwango RM, et al., 2024, Longitudinal population-level HIV epidemiologic and genomic surveillance highlights growing gender disparity of HIV transmission in Uganda, Nature Microbiology, Vol:9, ISSN:2058-5276, Pages:35-54

Monod M, Blenkinsop A, Brizzi A, et al., 2023, Regularised B-splines projected Gaussian Process priors to estimate time-trends in age-specific COVID-19 deaths, Bayesian Analysis, Vol:18, ISSN:1931-6690, Pages:957-987

Dan SK, Chen Y, Chen Y, et al., 2023, Estimating fine age structure and time trends in human contact patterns from coarse contact data: the Bayesian rate consistency model, PLOS Computational Biology, Vol:19, ISSN:1553-734X

Flaxman S, Whittaker C, Semenova E, et al., 2023, Assessment of COVID-19 as the underlying cause of death among children and young people aged 0 to 19 years in the US., Jama Network Open, Vol:6, ISSN:2574-3805, Pages:1-9

Blenkinsop A, Monod M, van Sighem A, et al., 2022, Estimating the potential to prevent locally acquired HIV infections in a UNAIDS Fast-Track City, Amsterdam, Elife, Vol:11, ISSN:2050-084X

Xi X, Spencer SEF, Hall M, et al., 2022, Inferring the sources of HIV infection in Africa from deep-sequence data with semi-parametric Bayesian Poisson flow models, Journal of the Royal Statistical Society Series C - Applied Statistics, Vol:71, ISSN:0035-9254, Pages:517-540

Brizzi A, Whittaker C, Servo LMS, et al., 2022, Spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals, Nature Medicine, Vol:28, ISSN:1078-8956

Hillis S, Unwin H, Chen Y, et al., 2021, Global minimum estimates of children affected by COVID-19-associated orphanhood and deaths of caregivers: a modelling study, The Lancet, Vol:398, ISSN:0140-6736, Pages:391-402

Faria NR, Mellan TA, Whittaker C, et al., 2021, Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil, Science, Vol:372, ISSN:0036-8075, Pages:815-821

Volz E, Mishra S, Chand M, et al., 2021, Assessing transmissibility of SARS-CoV-2 lineage B.1.1.7 in England, Nature, Vol:593, ISSN:0028-0836, Pages:266-269

Monod M, Blenkinsop A, Xi X, et al., 2020, Report 32: Targeting interventions to age groups that sustain COVID-19 transmission in the United States, Pages:1-32

Ratmann O, Kagaayi J, Hall M, et al., 2020, Quantifying HIV transmission flow between high-prevalence hotspots and surrounding communities: a population-based study in Rakai, Uganda, The Lancet Hiv, Vol:7, ISSN:2352-3018, Pages:e173-e183

Chatzilena A, van Leeuwen E, Ratmann O, et al., 2019, Contemporary statistical inference for infectious disease models using Stan, Epidemics, Vol:29, ISSN:1755-4365

Ratmann O, Grabowski MK, Hall M, et al., 2019, Inferring HIV-1 transmission networks and sources of epidemic spread in Africa with deep-sequence phylogenetic analysis, Nature Communications, Vol:10, ISSN:2041-1723

Lachlan RF, Ratmann O, Nowicki S, 2018, Cultural conformity generates extremely stable traditions in bird song, Nature Communications, Vol:9, ISSN:2041-1723

Wymant C, Hall M, Ratmann O, et al., 2018, PHYLOSCANNER: Inferring Transmission from Within- and Between-Host Pathogen Genetic Diversity., Mol Biol Evol, Vol:35, Pages:719-733

Ratmann O, van Sighem A, Bezemer D, et al., 2016, Sources of HIV infection among men having sex with men and implications for prevention, Science Translational Medicine, Vol:8, ISSN:1946-6242

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