Post-disaster population displacement and recovery analysis
Quantifying the dynamic flows of population displacement after disasters is crucial for response and recovery. How can use mobile phone data, which has its limitations and biases? See our papers in Royal Society Interface, PLoS ONE, & review paper in Computers, Environment, and Urban Systems.
World Bank Consultancy:
Data for Development
I am also passionate in translating research into practice. I have been working with the Global Facility for Disaster Reduction and Recovery (GFDRR) and other units at the World Bank to bring the data-driven methods into project operations. We developed Mobilkit, an open-source toolkit for mobility data analytics with MindEarth.
Urban diversity and social capital
Diverse social encounters in urban areas are key components for economic development, resilience, and social capital. How did the quality of our urban physical encounters change during the pandemic and beyond? See our arxiv paper for more details!
Geospatial AI for urban analytics and cross-city learning
Deep learning on spatio-temporal data enables us to learn high-dimensional representations of places, trajectories, and regions. How can we transfer insights across cities to predict the black swan events? See our papers in Nature Machine Intelligence, KDD2019, KDD2020, SIGSPATIAL2019.
COVID-19 mobility analysis and outbreak prediction
Were the non-compulsory stay-at-home orders in Japan effective in restricting mobility patterns in the early stages of the pandemic? We use mobility data and web search data provided by Yahoo Japan to analyze and predict outbreaks in Tokyo. For more, see our papers in Scientific Reports, Computers, Environment, and Urban Systems, and KDD2022.