Introduction

I am an assistant professor at Johns Hopkins University starting in 2024. Before that, I was an assistant professor joint at the Department of Civil Engineering and the Department of Computer Science, Stony Brook University. I have been a Postdoctoral Research Fellow at Stanford University during 2020-2021, and a Machine Learning Researcher at Qualcomm AI research during 2019-2020. I received Ph.D. in Advanced Infrastructure Systems and M.S. in Machine Learning from Carnegie Mellon University in 2019, and Bachelor's degree from Tsinghua University in 2014.

The foci of my research include mobile sensing, machine learning, urban computing, and smart infrastructure systems. I work on developing and deploying crowdsensing systems, machine learning algorithms, and incentive mechanisms to help monitor and understand real-time urban dynamics for enabling a sustainable and equitable smart city. I am particularly interested in

(1) developing learning algorithms and incentive mechanisms to improve efficiency of urban crowdsensing networks;

(2) developing collaborative and physics-informed machine learning algorithms for enabling smart and fairness-aware urban infrastructure systems, especially on transportation systems and buildings;

(3) incorporating dedicated sensing data (e.g., remote sensing data) and crowdsourced data (social media data) for enhancing rapid disaster response systems.

Target applications include near-real-time disaster information systems for natural hazards (earthquakes, hurricanes, wildfires), spatio-temporal urban sensing and data mining (air pollution, traffic, noise), large-scale infrastructure monitoring (buildings, bridges, and railway tracks).

[Google Scholar], and [Github].

I'm currently looking for Ph.D. students and Postdoc scholars in disaster information systems, remote sensing, and statistical machine learning. Please feel free to contact me at susuxu at jhu dot edu.