The Urban.AI seminar series will serve as a venue for top scientists around the world working in Urban.AI to discuss about new advances and collaborate on future research.
Defining and Practicing Computational Urban Science
February 1, 2024
Professor, Urban Informatics
Texas A&M University
Big data and computational algorithms have become increasingly integrated into the built environment and our daily lives, leading to a marked rise in computational urban science. Understanding the multi-dimensional interplay between computation and the spatial-social aspects of urban life carries both theoretical and practical implications for urban planning and management. Numerous analytical methods have been developed and applied to handle high-dimensional, heterogeneous, and unstructured location-based social data from urban settings. Computational urban science comprises four interdependent layers: human dynamics-centered, platform-based, action-oriented, and convergence-driven. As a research paradigm rooted in computational thinking and spatiotemporal synthesis, computational urban science offers a vital framework for addressing many of the urgent challenges in urban sustainability from a systematic perspective. This framework can foster a collective understanding of the current state of urban infrastructure and showcase innovative approaches to enhance urban resilience and efficacy beyond mere technological integration. This presentation will also highlight relevant research projects and activities undertaken by the Center for Geospatial Sciences, Applications and Technology, the Texas A&M Institute of Data Science-Urban AI Lab, and the Journal of Computational Urban Science.
Xinyue Ye is the Harold Adams Endowed Professor in Urban Informatics and Stellar Faculty Provost Target Hire at Texas A&M University (TAMU). He serves as the Faculty Fellow of Strategic Initiatives and Partnerships for The Division of Research at TAMU. With a career experience in urban planning, regional economics, geographic information systems, and computational science, his research focuses on geospatial artificial intelligence, smart cities, and convergence research. Dr. Ye models the space-time perspective of socioeconomic inequality and human dynamics for applications in various domains. Due to his innovative research integrating geography, planning, and computational science, Dr. Ye was the most junior faculty member and only the second planning faculty elected as a Fellow of the American Association of Geographers in 2022. His work has been funded by the National Science Foundation, National Institutes of Health, National Institute of Justice, National Academies, National Aeronautics and Space Administration, National Oceanic and Atmospheric Administration, Department of Commerce, Department of Energy, Department of Housing and Urban Development, Department of Transportation, Department of Health and Human Services, Microsoft, and the Canada Social Sciences and Humanities Research Council.
Collaborative Virtual Augmented Reality
November 10, 2023
Department of Electrical Engineering
Pennsylvania State University
Virtual reality (VR) over wireless networks can provide an interactive and immersive experience for multiple users simultaneously and thus has many applications, especially in VR-based education/training. However, satisfactory personalized user experience in such wireless immersive services demands stringent performance requirements, including: (1) high-speed and high-resolution panoramic image rendering; (2) extremely low delay guarantees; and (3) seamless user experience. Besides the aforementioned requirements, collaborative user experience requires scalability of VR service. In this talk, we will talk about our network algorithm design for providing both personalized and scalable collaborative VR experiences over wireless networks.
Bin Li is currently an associate professor in the Department of Electrical Engineering at Pennsylvania State University. He received Ph.D. degree in Electrical and Computer Engineering from The Ohio State University (OSU). He was a Postdoctoral Researcher in the Coordinated Science Lab (CSL) at the University of Illinois at Urbana-Champaign (UIUC). His research focuses on the intersection of networking, machine learning, and system development, and their applications in networking for virtual/augmented reality, mobile edge computing, mobile crowdsourcing, and Internet-of-Things (IoT). He received both the National Science Foundation (NSF) CAREER Award and Google Faculty Research Award.
The Next Big Leap: How AI and Data Science Can Transform Disaster Resilience Research and Practice
July 28, 2023
Zachry Endowed Associate Professor
Department of Civil and Environmental Engineering
Texas A&M University
This talk focuses on smart resilience, which is a new paradigm for harnessing community-scale big data and Artificial Intelligence (AI) to augment disaster resilience capabilities such as risk prediction, rapid impact assessment, predictive infrastructure failure monitoring and situational analysis. The big data revolution is transforming the science and practice of resilience to crises. Resilience in this context is defined as the ability of various systems (e.g., infrastructure, businesses, emergency response, and critical facilities) to maintain functionality needed for residents to reduce various social, economic, physical, and well-being impacts of floods. Data science and AI solutions could significantly augment the capabilities needed for smart flood resilience. In this talk, an overview of a number of studies conducted at the UrbanResilience.AI Lab will be presented in which various modes of big data such as location-based, digital trace, social media, crowdsourced, physical sensor data, and satellite images have been collected and analyzed in creating machine learning and deep learning models to improve different disaster resilience capabilities. The talk also highlights opportunities to promote equity in disaster resilience processes using responsible AI and data science opportunities. Also, challenges of responsible data science in this field will be discussed.