News & Events

Speaker: Jeffrey Kok Hui Chan, PhD | Assistant Professor at Singapore University of Technology and Design
Time: October 6th, 2023, 12:00 – 12:30 PM (CST)
Zoom Link: https://utexas.zoom.us/j/91425104430

Abstract: To do good, design ideas are often congealed in a form—a deliberate configuration that is material or immaterial, tangible or intangible—that can interact with us. What then is a good form? Horst Rittel suggested that the good form is one that avoids design failures; it should accomplish what it was intended for, and it also should not introduce unforeseen, unintended and undesirable side and after-effects. On the other hand, Christopher Alexander argued that tradition is the source of good forms, while Robert Grudin thought that the good form is truthful. In this presentation, the good form will be discussed in relation to the comportment frame of ethics (rather than the ‘detached’ or deliberative frame of ethics), where design, and also affordances, are argued to play increasingly pivotal roles in shaping our ethical interactions with automated and autonomous machines. To draw out the contours of this argument, three concise examples where the good form is salient will be examined: the design of the ‘kill switch’ on escalators, the choice architecture of a nuclear counterstrike, and the blinders of generative AI chatbots.

About: Jeffrey Chan is assistant professor in the Humanities, Arts and Social Sciences (HASS) cluster and the Design and Artificial Intelligence (DAI) program at the Singapore University of Technology and Design (SUTD). He conducts research and teaching at the intersection of design, ethics and cities. Recently, he has started to focus on mapping the ethical issues and challenges of social robotics in public spaces. He is also author of two books, Urban Ethics in the Anthropocene (2019), and Sharing by Design (2020). His most recent web-article on the ethics of chatbots can be found at: https://eu.boell.org/en/2023/09/08/ethics-communicating-generative-ai-chatbots.

Speaker: Tom Sanchez, PhD | Professor of Urban Affairs and Planning at Virginia Tech
Time: September 22, 2023, 2:00 – 2:30 PM (CST)
Zoom Link: https://utexas.zoom.us/j/91425104430

Abstract: Interest in AI has exploded since the release of platforms like ChatGPT providing mainstream access to powerful content-generation capabilities. In this talk, I will share insights from my recent work on AI in Planning done in collaboration with the American Planning Association. I will briefly describe some current AI tools that have much to offer planners, and improve the planning profession’s existing practices and ways of thinking, ultimately offering better ways to create sustainable, equitable, and resilient communities.

About: Tom Sanchez is a Professor of Urban Affairs and Planning at Virginia Tech in the National Capital Region (Washington, DC/Northern Virginia) but will soon be joining the Department of Landscape Architecture and Urban Planning (LAUP) at Texas A&M University. He conducts research in the areas of cities, technology, and scholarly impact. Over the past few years, he has been working in collaboration with the American Planning Association on a series of surveys, papers, and reports addressing the impact of AI on the planning profession.

Speaker: Yiming Xu, PhD | Postdoctoral Fellow | School of Architecture, UT
Time: September 8, 2023, 2:00 – 2:30 PM (CST)
Zoom Link: https://utexas.zoom.us/j/91425104430

Abstract: Accurately forecasting the real-time travel demand for dockless scooter-sharing is crucial for the planning and operations of transportation systems. This study proposes a novel deep learning architecture named Spatio-Temporal Multi-Graph Transformer (STMGT) to forecast the real-time spatiotemporal dockless scooter-sharing demand. The proposed model uses a graph convolutional network (GCN) based on adjacency graph, functional similarity graph, demographic similarity graph, and transportation supply similarity graph to attach spatial dependency to temporal input. The output of GCN is subsequently processed with weather condition information by the Transformer to capture temporal dependency. The proposed model is evaluated for two real-world case studies in Washington, D.C. and Austin, TX, respectively, and the results show that for both case studies, STMGT significantly outperforms all the selected benchmark models, and the most important model component is the weather information. The proposed model can help micromobility operators develop optimal vehicle rebalancing schemes and guide cities to better manage dockless scooter-sharing operations.

About: Yiming Xu is a Postdoctoral Fellow in The School of Architecture at the University of Texas at Austin. He received his B.E. and M.E. degrees in transportation engineering from Tongji University, China in 2016 and 2019, and his Ph.D. in civil engineering from the University of Florida in 2023. His research focuses on developing and applying data-driven methods to tackle challenging problems in transportation systems. He specializes in data science, trustworthy machine learning, and deep learning applications in travel behavior analysis and time series modeling to support urban mobility management and operation.

Speaker: Naveen Sudharsan, PhD | Postdoctoral Fellow | Jackson School of Geosciences
Time: August 25, 2023, 2:00 – 2:30 PM (CST)
Zoom Link: https://utexas.zoom.us/j/98848018679

Abstract: Persistent changes in the climate have amplified the strength and occurrence rate of unusual weather events, temperature fluctuations, natural disasters, and the resulting destruction they bring about. The temperature rise is related to increased days with heat waves and tropical nights, which generate problems in many different areas. The risks stemming from heat waves manifest in diverse manners, encompassing aspects like the severity of diseases and deficits in energy supply. To address effectively to such complex risks, it is necessary to analyze where and what risks occur. Most studies on heatwaves investigate different characteristics independently without their inter-relationships. However, this univariate approach to characterizing heatwaves often underestimates the impacts of heatwaves because they fail to characterize the extreme event comprehensively. So, here we developed a non-parametric multivariate approach to estimate heatwave risk, combining a non-parametric multivariate kernel density estimation and data envelopment analysis for hazard and vulnerability, respectively. Moreover, we use the universal thermal climate index to map risk to accommodate the effect of humidity, wind, radiation, and human metabolism along with temperature on heatwaves. In this presentation, I demonstrate a reproducible method that can provide guidance for policymakers in developing more efficient climate impact adaptations.

About: Naveen Sudharsan is a Postdoctoral Fellow in the Department of Earth and Planetary Sciences at Jackson School of Geosciences, The University of Texas at Austin. He has a Ph.D. from the Indian Institute of Technology Bombay in Environmental Science and Engineering, where his research was on heatwaves and extreme precipitation events. His research interests include Mechanisms, observations, attribution, and projection of hydro-meteorological extremes; Temperature Extremes and its impact on human health; Compound events, and Climate risk. Currently, he is working on multiple projects such as heat risk mapping for CONUS, coastal risk mapping, hurricane rainfall climatology for Texas, short-term forecasting through deep learning, assessment of heat mitigation strategies, digital twin development for the Gulf of Mexico, and heatwave causality studies.

The Smart Cities and Generative AI Symposium is a day-long gathering of interdisciplinary professionals across academia and industry to inform, examine, and discuss how generative artificial intelligence impacts our cities. Happening at the Texas Advanced Computing Center in Austin, Texas, this robust program will feature presenters from multiple universities across Texas and beyond, as well as smart mobility professionals working in our cities. Students from the Urban Information Lab at UT Austin will also present their research in this area, further generating discussion on this timely topic.

Symposium attendees will benefit from the rich diversity of perspectives present, and a networking lunch will allow opportunities for connection and collaboration. We aim to create a space for interdisciplinary learning, relationship building, and problem solving as AI technologies continue to shape our cities.

This event is organized by UT Austin researchers working on A Good System for Smart Cities, a core research project of Good Systems. Good Systems is a university-wide, interdisciplinary research grand challenge focused on designing ethical AI technologies for the benefit of society.

To RSVP and view the full schedule, view here: https://smartcitiessymposium.splashthat.com/