Howdy! Thank you for visiting our webpage! We are committed to advancing resilient, equitable, and smart urban socio-technological systems, particularly infrastructure systems and human communities within evolving and uncertain environments. 

On the methodology side, our work lies at the intersection of data science / machine learning, decision-making and optimization under uncertainty, and network science. In terms of applications, our focus extends to disaster management, community and infrastructure resilience, as well as their equity in a changing climate. We engage with a diverse range of systems, including air and road transportation systems, potable and storm water systems, supply chain networks, and networked monitoring systems, etc.

2024

06/01: Amir Shahlaee joined us as a PhD student. Welcome!

02/19: Sheetal Pandhare (CE master student) and Dhaval Dinesh Kawa (IE master student) joined the team as a graduate research assistant. Welcome y'all to the team!

02/15: Our lab received a grant (PI) from the North Central Texas Council of Governments. The project, titled "Integrated Network Design and Demand Estimation of Advanced Air Mobility",  focuses on designing an integrated network for advanced air mobility in the Dallas Fort Worth region while estimating the demand for such service. The project will provide valuable insights towards the implementation of urban air mobility to foster more efficient and potentially more equitable urban mobility.

02/01: Ram Gajjala (CS master student) joined the team as a graduate research assistant. Welcome!

01/31: Dr. Yu joined ASCE Committee on Future Weather and Climate Extremes and presented his work on infrastructure resilience against natural disasters to the committee.

01/05: Our lab received a grant (Co-PI) from TxDOT, alongside Dr. Mattingly as the PI and Dr. Hyun as another Co-PI. This project, titled "App-based Crowd Sourcing of Bicycle and Pedestrian Conflict Data", aims at developing a crowd-sourced data app to better understand the conflicts between vulnerable road users like pedestrians and cyclists and identify hotspots for conflict risks. Once widely adopted, the app will enable communities to accurately identify dangerous sites and then enhance transportation safety within their neighborhoods.

2023

10/17: Dr. Yu organized a session titled "Network Analytics for Smart and Resilient Urban Systems" at INFORMS 2023 Annual Meeting. Dr. Yu presented his work "Reconstructing Sparse Multiplex Networks with Applications to Covert Networks" during this session.

09/15: Hari Lalichetti (EE master student) and Yash Modi (CS master student), joined the team as a graduate research assistant. Welcome ya'll to the team!

2023

04/24: Our lab received a grant (PI) from TxDOT. This project, titled "Data-Driven Prioritization of Roadway Segments for Treatment during Severe Weather Events", will leverage machine learning, transportation network analysis, and operations research to improve roadway safety,  equity,  and efficiency during severe weather conditions.