"Towards Resilient Cities: Harnessing Data for Predicting Risk in Complex Urban Systems"

Dr. Hiba Baroud is an associate professor and the associate chair in the Department of Civil and Environmental Engineering at Vanderbilt University. She holds secondary appointments in Computer Science and Earth and Environmental Science. Her research is at the intersection of data analytics and risk and resilience modeling. Her group develops and applies methods founded in statistical learning, network models, and decision analysis to evaluate infrastructure performance during disasters. She is particularly interested in uncertain and dynamic interdependencies across multiple systems (infrastructure, humans, environment). Applications are focused on smart cities, developing countries, and Arctic communities. She is the co-chair of the Risk and Resilience Measurements Committee of the Infrastructure Resilience Division in the American Society of Civil Engineers (ASCE). She serves on the editorial board of the ASCE Journal of Infrastructure Systems and as Associate Editor of the ASCE Natural Hazards Review. Hiba is the recipient of the 2019 Global Voices Fellowship, the 2020 National Science Foundation Early CAREER award, and the 2022 National Academy of Sciences Arab-American Frontiers Fellowship. In 2023, Hiba was selected as a member of the Global Young Academy.

"City of Austin Smart Mobility Office Pilot Program and Partnerships" 

Heather Bishop, Smart Mobility Office/Austin Transportation Public Works, wears many hats on a cross-functional team of mobility, technology, policy, data and user experience professionals working to deliver outcomes that improve mobility, safety, and equitable access for all.  Raised in Austin, Heather is a long-time member of both the business and volunteer community and her career spans transportation, civil engineering, finance, and STEM education.

"Peer to Peer Carpooling: Early Practice and Customer Churn"

Dr. Chen is a faculty member at the School of Public Affairs and an Affiliate Faculty member of the Center for Urban Transportation Research at the University of South Florida. He holds a Ph.D. from the University of Washington, specializing in multimodal transport planning, transport economics, and policy, and the application of machine learning and AI in travel demand forecasting. Dr. Chen's research has made a significant impact in the field, with over 30 refereed journal articles that have garnered substantial attention. He holds the role of Associate Editor for Transportation Research Part D: Transport and Environment, a prestigious journal published by Elsevier Science. His contributions have earned him numerous awards and recognitions, including the prestigious honor of being recognized as one of the World's Top 2% Scientists-Highly Cited Researchers in Transportation and Logistics for both 2021 and 2022. In addition, he received the Best Paper award for Section F: Transport, Land-Use, and Sustainability at the World Conference on Transport Research Society in 2016.

"Revolutionizing Equitable Transportation Electrification: A Future Shaped by AI Planning"

Seung Jun’s research focuses on the urban informatics field, with a specific interest in transportation, equity, and machine learning. Seung Jun serves as a Lead Teaching Assistant in the Computer Science Department at UT Austin. Before entering the Ph.D. program, he received his Master’s in Community and Regional Planning at The University of Texas at Austin. Since then, he has worked closely with the Urban Information Lab as a Graduate Research Assistant.

"Assessing LLM Faithfulness: Lessons from Political Fact-Checking"

 
Dr. Greg Durrett is an assistant professor of Computer Science at UT Austin. His research focuses on techniques for accessing and reasoning about knowledge in text. Large language models (LLMs) like ChatGPT and GPT-4 have dramatically advanced the frontiers in this area; currently his team is looking at where these systems succeed and fail and how to enhance their capabilities, particularly via systems that use LLMs as primitives. He is a 2023 Sloan Research Fellow and a recipient of a 2022 NSF CAREER award, among other grants from agencies including the NSF, Open Philanthropy, DARPA, Salesforce, and Amazon. He completed his Ph.D. at UC Berkeley where he was advised by Dan Klein, and he was previously a research scientist at Semantic Machines.

"Deep Scene Synthesis Using Hybrid Geometric Representations"

Qixing Huang is an associate professor of Computer Science at the University of Texas at Austin. He obtained his PhD in Computer Science from Stanford University. He was a research assistant professor at Toyota Technological Institute at Chicago before joining UT Austin. Dr. Huang's research spans the fields of computer vision, computer graphics, and machine learning, and publishes extensively in venues such as SIGGRAPH, CVPR, ICCV, ECCV, NeuriPS, ICML, and etc. In particular, his recent focus is on developing machine learning algorithms (particularly deep learning) that leverage Big Data to solve core problems in computer vision, computer graphics and computational biology. He is also interested in statistical data analysis, compressive sensing, low-rank matrix recovery, and large-scale optimization, which provides theoretical foundation for his research. He also received the best paper award at the Symposium on Geometry Processing 2013, the best dataset award at the Symposium on Geometry Processing 2018, and the most cited paper award of Computer-Aided Geometric Design in 2010 and 2011, IJCAI Career Spotlight, and an NSF Career award.

Welcome Remarks and Moderator

Junfeng Jiao is a renowned expert in smart city/urban informatics and a Fulbright Specialist on Smart City for the National Government of Egypt. His research has been widely reported by major media outlets such as ABC, Associated Press, CNN, Fox, NBC, NPR, New York Times, and Wired. Dr. Jiao is the founding directors of Urban Information Lab, Texas Smart Cities, and UT NSF Ethical AI Program. He is also the founding member and past chair of UT Good Systems Grand Challenge. His research focuses on Smart City, Smart Transportation, Urban Informatics and AI. Dr. Jiao develops and applies different technologies to understand the impact of urban environments on people’s daily living. He firstly coined the term transit deserts and measured it in all US cities. Using different AI methods, Dr. Jiao investigated the spatial-temporal patterns of various shared activities (Airbnb, Uber, Scooter, and Bike sharing) in major US cities. In addition to that, Dr. Jiao has developed an autonomous robot delivery system at UT Austin, designed two cutting edge generative AI systems (ComputeGPT.org and OpenCityAI.com), and deployed one active digital twin model for Austin (Austin Fire Digital Twin). Currently he is leading four large Smart City projects: NSF: CIVIC Challenge Community Hub for Smart Mobility (Smart Hub), NSF NRT: AI-Convergent, Responsible, Ethical, Applied Training Experience for Roboticists (Ethical AI), An AI based Smart City Knowledge System (A Good System for Smart City), and USDOT Tier 1 UTC focusing on Smart Transportation and Climate (John Hopkins, MIT, UT, Utah, and others). As of July 2023, Dr. Jiao has raised over $30 million research funding from different sources such as NSF, USDOE, USDOT, TXDOT, UT, MITRE, Microsoft, Google, and others. Dr. Jiao also published over 110 peer reviewed articles and two single authored books in Shared Mobility and Smart City.

"A Holistic Approach to Study Urban Heat Vulnerability and Design Mitigation Strategies"

Harsh is a PhD student at the Jackson School of Geoscience, The University of Texas at Austin co-advised by Prof. Dev Niyogi and Prof. Zong-Liang Yang. His work focuses on the development of new datasets and model parametrizations to study urban hazards such as heat. He is a part of Texas extreme weather and Urban Sustainability (TExUS) group.

"Modeling and Quantifying Group Disparities: A Case Study of Homeownership Racial Disparity"

Inez Khan is currently a PhD student at the University of Texas at Austin in the Department of Statistics and Data Science. Inez's research interests broadly lie in applying machine learning and statistical modeling approaches to social science problems with background in areas such as casual inference and algorithmic fairness. Her background includes working with the City of Pittsburgh and the Pittsburgh Bureau of Police developing tools and machine learning models as an undergraduate at Carnegie Mellon University. Before coming to UT, Inez was a statistical programmer at the RAND Corporation working on algorithmic fairness and capabilities modeling in various settings for the Department of Defense and Department of Homeland Security while providing expert advice to policy stakeholders surrounding AI policy.

"Toward Smart Coastal Cities: Coastal Hazards and Machine Learning"

Dr. Jun-Whan Lee is an assistant professor in the Department of Civil Architectural and Environmental Engineering at The University of Texas at Austin, USA, specializing in coastal engineering. He conducts research utilizing machine learning, numerical modeling, and remote sensing to study extreme hazards in coastal and ocean environments. His research interests include coastal hazards (storm surges, tsunamis, and compound flooding), climate and coastal resilience, nature-based solutions in coastal and ocean environments, coastal vulnerability and risk assessments, and coastal processes. Through his research and teaching, he aims to build sustainable and resilient coastal communities by better understanding coastal hazards. Jun-Whan Lee obtained his Ph.D. in Civil and Environmental Engineering from Virginia Tech in 2021. He completed his B.S. (2012) and M.S. (2014) degrees in Civil and Environmental Engineering at Hanyang University, South Korea. From 2014 to 2017, he worked as a coastal hazard researcher at the National Institute of Meteorological Sciences in South Korea. After obtaining his Ph.D., he served as a postdoctoral associate at the Center for Coastal Studies at Virginia Tech before joining The University of Texas at Austin in 2023.

"Building Cities Twice: The Making of Digital Twins"

Ryan Hardesty Lewis is a BS student in Mathematics at the University of Texas at Austin with a focus in real-time tracking and prediction of disaster events and urban forecasting. Ryan’s research focuses on creating a digital twin for Austin by interfacing with IoT sensors and working closely with the City of Austin. His research primarily collaborates with Texas Extreme weather and Urban Sustainability “TExUS” Lab to develop realistic and holistic machine learning models.

"AI-Powered Insights: Extracting Value from Complex Data Ecosystems"

Sean Henry Lewis is a BS student in Mathematics at the University of Texas at Austin with a focus in data science and transportation. Sean’s research focuses on building full-stack web applications and programming deployable scripts for urban information systems for the Smart Cities initiative of Good Systems.

"Deciphering Emotional Dynamics with Language Models"

Junyi Jessy Li is an associate professor in the Linguistics Department at the University of Texas at Austin. She earned her Ph.D. (2017) in Computational Linguistics from the Department of Computer and Information Science at the University of Pennsylvania. Her research interests are in computational linguistics and natural language processing, specifically discourse and pragmatics, natural language generation, and computational social science. She received an NSF CAREER Award, an ACL Outstanding Paper Award (2022), an ACM SIGSOFT Distinguished Paper Award (2019), an Area Chair Favorite honor at COLING (2018), and a Best Paper Award nomination at SIGDIAL (2016).

"Generative AI for Engineering Design"

Xingang is a Ph.D. student working as a Research Assistant in the Walker Department of Mechanical Engineering at the University of Texas at Austin. Before joining the Ph.D. program, Xingang worked in the auto industry at OTICS Corporation in Japan for four years. He has practical working experience in the design of automotive engine parts for major car manufacturers, such as Toyota and SAIC Motor Co., Ltd. His research interests include generative design, deep learning for engineering design, and human-AI design collaboration.

"LLM Guidance with Autonomous Vehicle Exploration in Complex Environments"

Sean Zanyk-McLean is a Masters candidate in the Computational Science, Engineering, and Mathematics (M.S.) program in the Oden Institute at UT Austin. Over the past year, he has been focused on research related to optimization and differentiable rendering in computer graphics, this is a Institute for Foundations of Machine Learning (IFML) research project at UT Austin. Prior to attending graduate studies, he worked in industry for two years as a software engineer and also has an undergraduate degree in Computer Engineering from Michigan State University.

"Designing the Future: Leveraging ChatGPT and DALL-E in Urban Planning"

Connor is a PhD student in Community & Regional Planning. With an educational background in public health and urban planning, Connor has studied tobacco cessation programs, micro-mobility and health, and most recently, artificial intelligence in urban infrastructure. As an NSF NRT fellow, Connor’s work will integrate ethics into AI research for smart cities. More specifically, Connor hopes to lead conversations around fairness, justice and safety in terms of the technological future of our urban environments.

"Street Function Representation Learning on Long Term Traffic Flow Prediction"

Huihai Wang is a Ph.D. Student in the Community and Regional Planning (CRP) program at the University of Texas at Austin with a concentration in robotics, computer vision and deep learning in transportation and built environment evaluation. Huihai’s research involves integration of computer vision algorithms and autonomous robotics in urban built environment evaluation and mapping, as well as traffic flow and traffic behavior recognition from 2D traffic videos and 3D Lidar sensors.

Welcome Remarks

As the Dean of the University of Texas School of Architecture, Heather is a nationally recognized leader with a history of success and impact — both in industry and academia. Her practice converges in the media, arts, and cultural fields, and she explores the relationship between research and practice — reimagining the discipline through related writing, exhibition, and collaboration. She is a passionate educator who teaches the Practices course for first-year students, as well as advanced design studios at both the undergraduate and graduate levels. 
A registered architect, Heather is the co-design principal of the practice Axi:Ome in St Louis. She has also worked with Bohlin Cywinski Jackson in Pennsylvania, Marks Barfield in London, and Robert Luchetti Associates in Massachusetts. 
Prior to her tenure as the Sam and Marilyn Fox Professor in the Sam Fox School of Design & Visual Arts at Washington University, she was an assistant professor at Virginia Tech. She has also held visiting professor appointments at Konkuk University in South Korea and Aristotle University of Thessaloniki in Greece. She earned a Bachelor of Architecture degree from Virginia Tech and a Master of Architecture degree from Harvard University.