Jonathan Cardoso-Silva is an Assistant Professorial Lecturer in the Data Science Institute. He completed his PhD in Computer Science at King's College London in 2018, in which he developed optimisation algorithms for interdisciplinary complex network analysis (social, biological, political networks) and regression algorithms applied to the modelling of in-silico drug-disease efficacy.

Before joining LSE, he acted as the lead data scientist at a Brazilian data science consultancy start-up (Data Science Brigade) in commercial and research projects that involved forecast, regression, classification, and clustering of static and temporal structured data, as well as text documents. He has also had previous experience working as a software developer.

Jonathan is a computer engineer and data scientist interested in developing and applying optimisation algorithms to interdisciplinary problems, from the analysis of temporal data to the modelling of similarity networks, which arise naturally when evaluating social contact and biochemical data. These methods are grounded mostly in the field of operations research (engineering) and have been used in studies to optimize the production of power plants, to help understand certain dynamics arising in social, biological and molecular complex networks and to help medicinal chemists discover more effective drugs.

As a data scientist, he is interested in how data science teams are being structured and managed in practice (if at all) in their respective academic and industrial contexts, as well as the identification of coding best practices and interpersonal dynamics with the highest potential to lead to the success of a project. A qualitative assessment of what "success" means for most data science projects is a topic of interest, too. He is also interested in explainable AI, in developing methods and strategies to untangle the numerous biases that are embedded in the machine learning pipelines responsible for automating repetitive tasks at medium and large-sized companies.