Title of Talk: Machine Learning and Data-driven Decision Making for Sustainable Urban Development
Abstract of Talk
The increased influx of the population from rural to urban areas in the developing world poses major economic, social, and environmental challenges. These challenges necessitate the development of policies and plan to improve urban infrastructure and basic services. One of The United Nation’s 17 sustainable development goals (SDGs), Goal 11: Sustainable, Green and Resilient Cities, – outlines the defining constructs of an emerging urban planning paradigm. With gaining global traction, the paradigm focuses on utilizing technological revolutions for innovations in urban design that will help and catalyze lifestyle changes.
In this talk, we will provide an overview of the use of technological innovations in data gathering, data analytics, data-driven decision making, and policy design to address complex problems in three important and intertwined areas of an urban system that need to be tackled for sustainable development: (a) urban sprawl, (b) urban mobility, and (c) urban environment and health. The proliferation of mobile and electronic devices, the availability of publicly available sensing modalities, as well as the increasing digitalization of processes, has enabled the generation of unprecedented amounts of data. This, coupled with unprecedented progress in computational power, provides us an opportunity to use artificial intelligence and machine learning methods for data-aided analysis, discovery, and decision-making to address the complex issues surrounding urban systems. We will provide an overview of our pilot projects in the aforementioned areas to establish a mesh of physical, social, and economic connections in the urban systems. In our approach, we feed the data, collected from a wide variety of modalities (such as remote satellite measurements, surveys, socio-economic indicators, on-ground sensing networks such as video feeds, etc.), into cutting-edge machine learning techniques to model these connections and to derive useful insights for informed decision making, and to conceptualize a policy cycle for attaining public value.
Biography of the Speaker
Dr. Zubair Khalid is an Associate Professor in the Electrical Engineering Department, School of Science and Engineering, Lahore University of Management Sciences. He received a Ph.D. degree from the Australian National University funded by the prestigious Endeavour Scholarship. He received his undergraduate degree in Electrical Engineering from the University of Engineering and Technology Lahore, Lahore, Pakistan, where he was awarded three University Gold medals and two Industry Gold medals (Siemens and Nespak).
His research interests include the use of machine learning, data processing, computer vision, natural language processing, and the development of novel information processing techniques in these areas for applications in cosmology, medical imaging, acoustics, speech processing, and data-driven urban development. He has supervised 7 Ph.D. students and a large number of research projects both at the graduate and undergraduate levels. He has over 75 publications to date in refereed international journals and conferences. He is a senior member of IEEE and is serving on the Editorial Board as an Associate Editor for the IEEE Signal Processing Letters.