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Hao Yan is an assistant professor in School of Computing, Informatics, and Decision Systems Engineering at Arizona State University. He obtained a bachelor's degree in physics and dual degree in economics from Peking University in 2011. Yan received his master's degree in statistics in 2015, a master's degree in computational science and engineering in 2016 and a doctorate in industrial and systems engineering in Georgia Institute of Technology in 2017.

His research interests include machine learning, data analytics for images, profiles and time series data for change detection and localization, and spatial-temporal analysis.


  • Ph.D Industrial and Systems Engineering, Georgia Institute of Technology 2017
  • M.S. Computational Science and Engineering, Georgia Institute of Technology 2016
  • M.S. Statistics, Georgia Institute of Technology 2015
  • B.S. Economics, Peking University, China 2011
  • B.S. Physcs, Peking University, China 2011
Research Interests

My research interests focus on developing efficient and scalable machine learning and representation learning algorithms for large-scale high-dimensional data with complex heterogeneous data structure to extract information or useful features for the purpose of data fusion for assessment of system performance, early detection of system anomalies, intelligent sampling and sensing for data collection and decision making to achieve optimal system performance. My research lies at the intersection of statistics, machine learning and industrial engineering can be categorized into the following areas:

More specifically, my main research includes

  1. Real time modeling and analysis with large scale high dimensional data: Develop scalable and computational efficient algorithms for real time modeling and analysis of high dimensional data with complex structure (tensor structure, complex spatio-temporal structure, etc.)
  2. Data fusion for modeling of complex systems: Develop data analysis and data fusion techniques to combine information from multiple sensors for process modeling, anomaly detection and quality improvement for complex systems.
  3. Smart adaptive sampling strategy and data reconstruction: Develop smart and adaptive sampling for different systems to reduce the data collection time. Develop quality measurement and data reconstruction techniques using compressive sensing.


Spring 2019
Course NumberCourse Title
IEE 580Practicum
IEE 584Internship
IEE 599Thesis
IEE 605Foundations of Information Sys
IEE 795Continuing Registration
Fall 2018
Course NumberCourse Title
IEE 572Design Engineering Experiments
IEE 584Internship
IEE 599Thesis
Summer 2018
Course NumberCourse Title
IEE 584Internship
Spring 2018
Course NumberCourse Title
IEE 598Special Topics
Fall 2017
Course NumberCourse Title
IEE 572Design Engineering Experiments
Honors / Awards
  • Best Student Paper Award Finalist in Quality, Statistics, and Reliability Section of INFORMS, for the paper “AKMM: Adaptive Sensing for Online Anomaly Detection,” 2016. (The final winner will be selected at the INFORMS Conference in Nov. 2016).
  • QSR Refereed Track Best Paper Award for the paper “Real-time Monitoring and Diagnosis of High-Dimensional Data Streams via Spatio-Temporal Smooth Sparse Decomposition,” Oct 2015
  • Best Student Paper Award in the Industrial and Systems Engineering Research Conference (ISERC) in the Quality Control and Reliability Engineering (QCRE) division, for the paper “Monitoring and Diagnostics of Streaming Images Via Recursive Smooth-Sparse Decomposition,” May 2015
  • Best Student Paper Award in the INFORMS Data Mining Section, for the paper “Image Defect Detection via Smooth Sparse Decomposition,” Nov. 2014