Bio


Hae Young Noh is an associate professor in the Department of Civil and Environmental Engineering. Her research introduced the new concept of “structures as sensors” to enable physical structures (e.g., buildings and vehicle frames) to be user- and environment-aware. In particular, these structures indirectly sense humans and surrounding environments through their structural responses (i.e., vibrations) by inferring the desired information (e.g., human behaviors, environmental conditions, heating and cooling system performance), instead of directly measuring the sensing targets with additional dedicated sensors (e.g., cameras, motion sensors). This concept brought a paradigm shift in how we view these structures and how the structures interact with us.
Traditionally, structures that we inhabit (such as buildings or vehicles) are considered as passive and unchanging objects that we need to monitor and control, utilizing a dense set of sensors to collect information. This has often been complicated by “noise” caused by the occupants and environments. For example, building vibrations induced by indoor and outdoor environmental and operational conditions (e.g., people walking around, traffic outside, heating system running, etc.), have been often seen as noise that needs to be removed in traditional building science and structural engineering; however, they are a rich source of information about structure, users, environment, and resources. Similarly, in vehicle engineering, researchers and engineers have been investigating control and dynamics to reduce vehicle vibration for safety and comfort. However, vibrations measured inside vehicles contain information about transportation infrastructure, vehicle itself, and driver.
Noh's work utilizes this “noise” to empower the structures with the ability to perceive and understand the information about users and surroundings using their own responses, and actively adopt and/or interact to enhance their sustainability and the occupants’ quality of life. Since she utilizes the structure itself as a sensing medium, information collection involves a simpler set of hardware that can be easily maintained throughout the structural lifetime. However, the analysis of data to separate the desired information becomes more challenging. This challenge is addressed through high-rate dynamic sensing and multi-source inferencing. Ultimately, her work aims to allow structural systems to become general sensing platforms that are easier and more practical to deploy and maintain in a long-term.
At Stanford University, Noh received her PhD and MS degrees in the CEE department and her second MS degree in Electrical Engineering. Noh earned her BS in Mechanical and Aerospace Engineering at Cornell University.

Academic Appointments


  • Associate Professor, Civil and Environmental Engineering

Honors & Awards


  • Best Student Paper Award, ASCE Engineering Mechanics Institute Dynamics Committee (ASCE EMI) (2019)
  • Best Demo Award, ACM Systems for Energy-Efficient Buildings, Cities, and Transportation (ACM BuildSys) (2019)
  • Best Poster Award, IEEE/ACM Information Processing and Sensor Network (IPSN) (2019)
  • Best Paper Award, IEEE International Conference on Machine Learning and Applications (ICMLA) (2018)
  • Best Student Paper Award, ASCE Engineering Mechanics Institute Dynamics Committee (ASCE EMI) (2018)
  • Best Poster Runner-Up, ACM Embedded Networked Sensor Systems (ACM SenSys) (2018)
  • CIT Dean’s Early Career Fellow, Carnegie Mellon University (2018)
  • NSF CAREER Award, National Science Foundation (NSF) (2017)
  • Google Faculty Research Award, Google (2017)
  • People’s Choice Paper Award, ACM Systems for Energy-Efficient Built Environments (ACM BuildSys) (2017)
  • Best Poster Award & Best Poster Runner-Up, ACM Embedded Networked Sensor Systems (ACM SenSys) (2016)
  • MobiSys 2016 Junior Faculty/Postdoc Grants, ACM Mobile Systems, Applications, and Services (ACM MobiSys) (2016)
  • Best Poster Award, IEEE/ACM Information Processing and Sensor Network (IPSN) (2015)
  • Google Faculty Research Award, Google (2014)
  • Berkman Faculty Development Fund, Carnegie Mellon University (2013-2015)
  • John A. Blume Fellowship, - (2010-2011)
  • Samsung Scholarship Foundation Merit-Based Scholarship, Samsung Scholarship Foundation (2006-2010)
  • American Society of Civil Engineers Essay Contest Award, American Society of Civil Engineers (2010)

Professional Education


  • PhD, Stanford University, Civil and Environmental Engineering (2011)
  • MS, Stanford University, Electrical Engineering (2011)
  • MS, Stanford University, Civil and Environmental Engineering (2008)
  • BS, Cornell University, Mechanical and Aerospace Engineering (2005)

2019-20 Courses


Stanford Advisees


  • Doctoral (Program)
    Jingxiao Liu

All Publications


  • Application of a time series based damage detection algorithm to the Taiwanese benchmark experiment 10th International Conference on Application of Statistics and Probability in Civil Engineering Noh, H., Nair, K. K., Kiremidjian, A. S., Loh, C. TAYLOR & FRANCIS LTD. 2007: 551–552