Urban Computing and Artificial Intelligence

Dr. Khan is an Assistant Professor of Geography at Lalbaba College, Howrah, India. His research incorporates simulation and numerical modeling of global climatic events including urban climate and microclimatic variation using the state-of-the-art Weather Research and Forecasting (WRF) model and Regional Climate Model (RegCM). A key focus of his research is searching for appropriate mitigation strategies and technologies to decrease the overheating of tropical urban areas while also decreasing energy consumption and protecting health. He has commendable expertise in handling data and implementing models in many software platforms. His research contribution has been recognized by some leading researchers in the domain of urban climate research. Mat Santamouris is the Anita Lawrence Professor of High Performance Architecture in the University of New South Wales in Australia. He is a past a professor at the University of Athens, Greece and visiting Professor at the Cyprus Institute, Metropolitan University of London, Tokyo Polytechnic University, Bolzano University, Brunnel University and National University of Singapore. Past President of the National Center of Renewable and Energy Savings of Greece. Editor and author of 15 international books on topics related to heat island, solar energy and energy conservation in buildings published by Earthscan, Springer, etc. Guest editor of twelve special issues of various scientific journals. Scientific coordinator of many international research programs and author of almost 290 scientific papers published in peer reviewed international scientific journals. Reviewer of research projects in 15 countries including USA, UK, France, Germany, Canada, Sweden, etc. Expert in various International Research Institutions. Highly Cited researcher according to Clarivate in 2017 and 2018.Dev Niyogi is Dave P. Carelton Centennial Professor in the Department of Geological Sciences at Jackson School of Geosciences, and Department of Civil, Architectural, and Environmental Engineering in the University of Texas at Austin, USA. He was the most recent chair of the American Meteorological Society (AMS) Board of Urban Environment and elected advisory board member of the International Association of Urban Climate. He is currently serving on the AMS Committee on Applied Climatology, and has previously served on AMS Committee on Agriculture and Forest Meteorology, invited member FGDC Spatial Climate Working Group, Member of the Weather Research and Forecast (WRF) model WG-14 (land surface models), and Member of the AGU Biogeochemistry meetings group/spring meeting student awards chair. He has provided invited testimonies to the National Academy study group, planning summer meetings, and Senate Working groups.

• 1. Weather extremes and urban warming: Are urban digital twins approaches emerging as the ultimate tool for resilient cities2. The use of urban digital twins through machine learning and artificial intelligence in the design, planning, and making resilient cities3. Urban warming and global energy crisis: how to achieve sustainable energy for future cities through machine learning and artificial intelligence4. Seasonal variations in peak electricity demand in response to urban warming: Application of Google earth engine and artificial intelligence5. Urban climate change and public health risk: deploying artificial intelligence for human health adaptation to urban warming in cities6. Urban heat and human thermal comfort: developing health data, impacts, and indices through machine learning and artificial intelligence7. Economic and societal costs of urban warming: understanding compound economic impact of climate change through machine learning algorithms8. Urbanization and urban warming: deployment of urban digital twins to study the impacts on diurnal rainfall modification9. Urban warming, energy balance and thermal management: the case studies for cost-effective and operative in urban heat mitigation through artificial intelligence10. The impact of urban pollution in cities: an ensemble machine learning model for accurate air pollution detection11. Major outcomes and limitations in future research and innovation agenda for using machine learning and artificial intelligence in urban digital twins for urban climate research