Hae Young Noh

About

Hae Young Noh is a professor in the Department of Civil and Environmental Engineering at Stanford University. Her research introduced the innovative concept of “structures as sensors,” which enables physical structures such as buildings and vehicle frames to be user- and environment-aware by sensing their own structural responses, particularly vibrations. Instead of relying on additional dedicated sensors like cameras or motion detectors, her work leverages the natural vibrations caused by human activities and environmental conditions to infer information about human behaviors, environmental states, and system performance. This approach represents a paradigm shift in how structures are viewed and interact with their surroundings, transforming them from passive objects into active sensing platforms. Her work addresses the traditional view of structures as passive and unchanging, which are monitored using dense sensor networks often complicated by noise from occupants and environmental factors. Noh’s methodology utilizes this “noise” as a valuable source of information, simplifying hardware requirements and enabling long-term, practical deployment. Her research involves high-rate dynamic sensing and multi-source inferencing to analyze structural responses and extract meaningful data about users and environments. Her ultimate goal is to develop structural systems that can serve as general sensing platforms, enhancing sustainability and quality of life through smarter, more…

Research topics

• Computer Science
• Computer Security
• Engineering
• Artificial Intelligence
• Real-time computing
• Construction engineering
• Systems engineering
• Geology
• Seismology
• Forensic engineering

Selected publications

• PAS: Prediction-Based Actuation System for City-Scale Ridesharing Vehicular Mobile Crowdsensing

  IEEE Internet of Things Journal · 2020 · 97 citations

  Vehicular mobile crowdsensing (MCS) enables many smart city applications. Ridesharing vehicle fleets provide promising solutions to MCS due to the advantages of low cost, easy maintenance, high mobility, and long operational time. However, as nondedicated mobile sensing platforms, the first priorities of these vehicles are delivering passengers, which may lead to poor sensing coverage quality. Therefore, to help MCS derive good (large and balanced) sensing coverage quality, an actuation system i…

  DOI

• PhyMDAN: Physics-informed knowledge transfer between buildings for seismic damage diagnosis through adversarial learning

  Mechanical Systems and Signal Processing · 2020 · 78 citations

  Senior authorCorresponding
  DOI

• Adaptive Hybrid Model-Enabled Sensing System (HMSS) for Mobile Fine-Grained Air Pollution Estimation

  IEEE Transactions on Mobile Computing · 2020 · 74 citations

  Fine-grained city-scale outdoor air pollution maps provide important environmental information for both city managers and residents. Installing portable sensors on vehicles (e.g., taxis, Ubers) provides a low-cost, easy-maintenance, and high-coverage approach to collecting data for air pollution estimation. However, as non-dedicated platforms, vehicles like taxis usually prefer gathering at busy areas of a city where it is more likely to pick up riders. This leaves many parts of the city unsense…

  DOI

• Urban sensing using existing fiber-optic networks

  Nature Communications · 2025-03-31 · 16 citations

  articleOpen access

  The analysis of urban seismic signals offers valuable insights into urban environments and society. Yet, accurate detection and localization of seismic sources on a city-wide scale with conventional seismographic network is unavailable due to the prohibitive costs of ultra-dense seismic arrays required for imaging high-frequency anthropogenic sources. Here, we leverage existing fiber-optic networks as a distributed acoustic sensing system to accurately locate urban seismic sources and estimate h…

  PublisherOA PDFDOI

• Characterizing Vehicle-Induced Distributed Acoustic Sensing Signals for Accurate Urban Near-Surface Imaging

  Seismological Research Letters · 2025-04-21 · 9 citations

  article

  Abstract Continuous seismic monitoring of the near-surface structure is crucial for urban infrastructure safety, aiding in the detection of sinkholes, subsidence, and other seismic hazards. Utilizing existing telecommunication optical fibers as distributed acoustic sensing (DAS) systems offers a cost-effective method for creating dense seismic arrays in urban areas. DAS leverages roadside fiber-optic cables to record vehicle-induced surface waves for near-surface imaging. However, the influence…

  PublisherDOI

Recent grants

• CAREER: Structures as Sensors: Elder Activity Level Monitoring through Structural Vibrations

  NSF · $437k · 2020–2024

• CAREER: Structures as Sensors: Elder Activity Level Monitoring through Structural Vibrations

  NSF · $516k · 2017–2020

Frequent coauthors

• Pei Zhang

  University of Michigan–Ann Arbor

  92 shared

• Shijia Pan

  University of California, Merced

  70 shared

• Jonathon Fagert

  Baldwin–Wallace College

  53 shared

• Yiwen Dong

  53 shared

• Mostafa Mirshekari

  Stanford University

  51 shared

• Jingxiao Liu

  Stanford University

  35 shared

• Susu Xu

  Johns Hopkins University

  30 shared

• Mario Bergés

  Carnegie Mellon University

  23 shared

Labs

• Structures as Sensors LabPI

  1-2 sentence research focus

Education

• Ph.D., Civil Engineering

  Stanford University

  1997

• M.S., Civil Engineering

  University of California, Berkeley

  1992

• B.S., Civil Engineering

  University of California, Berkeley

  1990

Similar researchers at Stanford University

• Mark Z. Jacobsonh-133
• Peter K. Kitanidish-73
• William Mitchh-72