Vahid Moosavi

Senior data scientist at Swiss Re, Zurich

Personal Update: I am no longer in academia and I have moved to industry and started a new position at Swiss Re, focusing on building data driven products in the context of risk management.

Academic Interests

Intersection of machine learning and complex urban and spatial systems

    • Planetary urban modeling: Modeling different urban phenomena at the scale of planet using open source geo-data streams

    • Data driven urban computing for fast and scalable modeling of urban flows (e.g. flood, wind, heat, etc.)

    • City as text: Data driven hierarchical representation of urban stocks (e.g. floor plans, buildings, parcels, neighborhoods, etc.)

    • Health and place (e.g. urban characteristics and urban air pollution)

    • Urban economy and real estate dynamics

    • Transportation networks dynamics

    • Multidimensional geo-visualizations and spatial search

Data driven modeling across disciplines

    • Structural design and optimization

    • Dynamical networks and systemic risk

    • Financial time series forecasting

    • Manufacturing and supply chain systems

    • Atmospheric science

Machine learning and deep learning literacy for non-computer scientists

Other computational and modeling concepts (Mainly from 2006-2010)

    • Agent based modeling

    • System dynamics

    • Optimization methods

    • Multi criteria decision-making