Kaveh Jahanshahi

Lead Data Scientist

Kaveh is a lead data scientist at the Data Science Campus.

He obtained a PhD in Urban and Transport Modelling from the University of Cambridge, using advanced statistical and machine learning techniques to understand travel patterns and their temporal and spatial variations.

Kaveh has worked research projects throughout on understanding urban issues, from travel and health to climate change, through digital urban modelling in the UK and across different geographic areas of the world.

Prior to joining the Campus, Kaveh worked to a combined policy-academic career, as transport modelling team leader at the Department for Transport (DfT) and Department for Environment, Food and Rural Affairs’ Joint Air Quality Unit. He was also a Research Associate in urban analysis at the University of Cambridge.

Kaveh also has experience in private sectors’ research and policy units and consultancies, working as senior Transport Modeller for different global consultants (including WSP Group). For example he managed the Department for Transport’s “National Transport Model”, and APRIL, which models the impact of transport policies in London.

Posts by Kaveh Jahanshahi

Using open-source data to measure our engagement with the natural environment 

ONS Data Science Campus (DSC) and Defra’s Spatial Data Science team developed a novel solution for estimating the number of visitors to natural spaces across England.

Read more on Using open-source data to measure our engagement with the natural environment 
A concept image of the novel coronavirus.

Use of hybrid data to understand the community-level influences on coronavirus (COVID-19) incidence

Understanding and monitoring the major influences on COVID-19 infection numbers in communities is essential to inform policy making and evaluate the impact of non-pharmaceutical interventions. We have developed a community-level analysis by assembling a large set of static and dynamic data for England.

Read more on Use of hybrid data to understand the community-level influences on coronavirus (COVID-19) incidence