Susu Xu

About

Susu Xu is an assistant professor in the Department of Civil and Systems Engineering at Johns Hopkins University. Her research focuses on mobile sensing, machine learning, urban computing, smart infrastructure systems, and rapid disaster response. Her current projects include developing learning algorithms and incentive mechanisms to improve the efficiency of urban crowdsensing networks and collaboration, creating physics-informed machine learning algorithms to enable smart and fairness-aware urban infrastructure systems, and utilizing multi-sourced, multi-modal sensing and learning to enhance rapid disaster response systems. Her applications target near-real-time disaster information systems for natural hazards such as earthquakes, hurricanes, and wildfires, as well as spatio-temporal urban sensing and data mining related to air pollution, traffic, and noise, along with large-scale infrastructure monitoring of buildings, bridges, and railway tracks.

Research topics

• Computer Science
• Engineering
• Real-time computing
• Computer Security
• Artificial Intelligence
• Transport engineering
• Construction engineering
• Systems engineering
• Forensic engineering
• Environmental science

Selected publications

• A Survey of Autonomous Driving from a Deep Learning Perspective

  ACM Computing Surveys · 2025-04-18 · 35 citations

  reviewOpen access

  Autonomous driving represents a significant advancement in the transportation industry, enhancing vehicle intelligence, optimizing traffic management, and improving user experiences. Central to these innovations is deep learning, which enables systems to handle complex data and make informed decisions. Our survey explores critical applications of deep learning in autonomous driving, such as perception and detection, localization and mapping, and decision-making and control. We investigate specia…

  PublisherDOI

• Near-real-time earthquake-induced fatality estimation using crowdsourced data and large-language models

  International Journal of Disaster Risk Reduction · 2024-07-17 · 17 citations

  articleSenior authorCorresponding
  PublisherDOI

• Rapid Building Damage Estimates From the M7.8 Turkey Earthquake Sequence via Causality‐Informed Bayesian Inference From Satellite Imagery

  Earthquake Spectra · 2024-11-20 · 9 citations

  articleSenior authorCorresponding

  On February 6, 2023, a major earthquake of 7.8 magnitude and its aftershocks caused widespread destruction in Turkey and Syria, causing more than 55,000 deaths, displacing 3 million people in Turkey and 2.9 million in Syria, and destroying or damaging at least 230,000 buildings. Our research presents detailed city‐scale maps of landslides, liquefaction, and building damage from this earthquake, utilizing a novel variational causal Bayesian network. This network integrates InSAR‐derived change de…

  PublisherDOI

• Multi-Agent Target Pursuit Using Perception Uncertainty-Aware Reinforcement Learning

  2024-12-04 · 5 citations

  articleOpen access

  Existing target pursuit systems are able to coordinate a team of mobile agents to capture or intercept unauthorized targets. Multi-agent reinforcement learning (MARL) further empowers pursuit strategies with the potential to emerge complex behaviors. However, existing solutions lack the ability to handle the perception uncertainty caused by relative position measurement noises, which blurs the understanding of the target's state and complicates the pursuit strategy learning process. This study p…

  PublisherOA PDFDOI

• Optimizing Rapid Seismic Building Damage Assessment: Integrating Enhanced Radar Change Detection Maps with Variational Bayesian Networks

  2024-07-07 · 3 citations

  articleSenior author

  Accurate damage estimation after earthquakes is crucial for effective post-disaster response and recovery. However, earthquakes often trigger various additional hazards, such as landslides and liquefaction, making accurate building damage estimation even more challenging. To date, despite significant research efforts, automated, accurate building-specific damage estimation has not been achieved. Our study tackles this challenge. We integrate multi-sourced global building footprints and InSAR coh…

  PublisherDOI

Recent grants

• Rapid, Scalable, and Joint Assessment of Seismic Multi-Hazards and Impacts: From Satellite Images to Causality-Informed Deep Bayesian Networks

  NSF · $392k · 2024–2025

Frequent coauthors

• Hae Young Noh

  30 shared

• Xinlei Chen

  Peng Cheng Laboratory

  28 shared

• Xuechun Li

  Johns Hopkins University

  14 shared

• Carlee Joe‐Wong

  9 shared

• Shijia Pan

  University of California, Merced

  8 shared

• David J. Wald

  United States Geological Survey

  8 shared

• Chenguang Wang

  Stony Brook University

  7 shared

• Xiao–Ping Zhang

  University Town of Shenzhen

  6 shared

Awards & honors

• NSF CAREER Award
• American Society of Mechanical Engineers (ASME) Structural H…
• International Conference on Machine Learning and Application…
• Champion of NeurIPS Adversarial Vision Challenge
• MIT’s Civil and Environmental Engineering Rising Star award

Similar researchers at Johns Hopkins University

• Somnath Ghoshh-62
• Michael Shieldsh-45
• James Guesth-37