Raphael Sonabend

Hey there and welcome to my site!
My name is Raphael Sonabend (PhD, GradStat) [They/Them or He/Him].

I am a Technology Manager in the Data for Science and Health team at the Wellcome Trust where I manage projects related to foundational technology and infectious diseases.

I'm also a Research Assistant in the Imperial College London COVID-19 Response Team in the Department of Infectious Disease Epidemiology. I am part of Professor Neil Ferguson's COVID-19 real-time modelling where we helped forecast the trajectory of COVID-19 throughout the pandemic and reported to SPI-M-O to help inform decisions around lockdowns and other non-pharmaceutical interventions.

I'm also a Postdoctoral Fellow in the Vollmer Research Group in the Department of Computer Science in the University of Kaiserslautern, where I primarily research machine learning applications to survival analysis including fairness in survival and incorporation of Julia in R to optimise speed and accuracy performance for survival models.

As well as theoretical research, I have published several R packages to help make machine learning more accessible, including a universe of packages to expand R's object-oriented capabilities.

Outside of work you may find me walking my dog, reading, or learning BSL.

Explore my website to find out more.

Affiliations

Research Associate, Imperial College London
Postdoctoral Researcher, University of Kaiserslautern

Education

PhD Statistics, UCL, 2021
BSc Statistics, UCL, 2018

Selected Research and Media

Sonabend et al. (2021). Non-pharmaceutical interventions, vaccination, and the SARS-CoV-2 delta variant in England: a mathematical modelling study. The Lancet. 10.1016/S0140-6736(21)02276-5
Sonabend et al. (2021). mlr3proba: An R Package for Machine Learning in Survival Analysis. Bioinformatics. 10.1093/bioinformatics/btab039
Sonabend et al. (2021). Evaluating the Roadmap out of Lockdown: modelling step 4 of the roadmap in the context of B.1.617.2.
Sonabend, R. E. B. (2021). A Theoretical and Methodological Framework for Machine Learning in Survival Analysis: Enabling Transparent and Accessible Predictive Modelling on Right-Censored Time-to-Event Data [University College London (UCL)].
2019 interview with The Naked Scientists.

Research Interests

Machine learning
Survival analysis
Mathematical modelling
Probabilistic programming
Benchmarking
Model validation
Ethics in AI
Risk and uncertainty
OOP
Symbolic programming
R
Julia

I use Medium to publish short blog posts about statistics, data science, machine learning, and occassionally ethics. Check out my articles at the links below (contact me if you have any trouble accessing these).

If you want to work, research, or collaborate with me, please feel free to reach out via any of the methods below. I'm actively looking for people to join our lab and to help maintain R packages.

Check out Professor Sebastian Vollmer's jobs page if you want to work with me. We are looking for MSc and PhD students as well as junior and senior postdocs. Remote positions available!
Email me to chat about data science, research, open-source, or anything else.
Follow me on Twitter to stay up to date with my latest work, research, general thoughts, and coding activities (snapshot to the right).
Subscribe to my Medium to get my latest blogs as they're published.
Follow my GitHub to keep up to date on my coding projects in public repos. Reach out if you're interested in interning with me (paid from GitHub sponsorships only (currently $0)).
You can also find me on Stack Overflow.
Check out my LinkedIn for an extended CV.
Finally, my publications and preprints can be found on my ORCID (0000-0001-9225-4654), ResearchGate, and Google Scholar.

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