Chico Camargo is a research assistant at the OII working on network science and traffic modelling. In his DPhil, at Oxford Physics, he uses complex systems and data science to study evolution.Email: email@example.com
Chico Camargo is working at the TRANSNET project, which aims at using data science to forecast and understand transport network resilience and anomalies. He’s working with Dr Jonathan Bright, Dr Scott Hale and Dr Graham McNeill. He is also a member of the #SocialHumanities network at The Oxford Research Centre in the Humanities (TORCH).
Meanwhile, he is finishing his DPhil at the Rudolf Peierls Centre for Theoretical Physics, also at Oxford, where he is using complex systems and data science to investigate the physical principles that rule biological evolution – or more specifically, genotype-phenotype maps. It turns out that the way biology transforms genotype into phenotype is very similar to the way computers turn inputs into outputs, and he hopes to shed some light into this matter using algorithmic information theory and some tools from machine learning.
Previously, he has been at the University of São Paulo, where he graduated with a BSc as part of the Molecular Sciences Programme, and has worked in Mathematical Biology at the Wolfson Centre for Mathematical Biology, and at Department of Zoology, University of Oxford. Chico is very grateful to the Clarendon Fund and to Brasenose College for full funding support.
Apart from doing science himself, Chico is also passionate about communicating it to wider audiences. He is part of an award-winning YouTube channel called BláBláLogia, which hosts daily videos on topics ranging from space travel to ecology to film making. In his fortnightly show, Top Models, Chico speaks about his favourite scientific tool: mathematical models. Apart from his YouTube work, he has also spoken at science communication events such as FameLab, as well as Oxford’s Science Cabaret.
Position held at OII:
- Research Assistant, May 2017 –
complex systems, evolution, information theory, algorithmic complexity, data science, data visualisation