Hao Yan
Associate Professor,
School of Computing and Augmented Intelligence
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Mail code: 8809Campus: Tempe
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Long Bio
Hao Yan is an associate professor in the School of Computing and Augmented Intelligence 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 develops machine learning methods for high-dimensional, spatio-temporal, and sequential data from engineered and societal systems. Current interests include reinforcement learning (distributional and flow-based value learning), generative models (flow matching and diffusion models for anomaly detection and surrogate modeling), causal structure learning, uncertainty-aware and physics-informed learning, and tensor and spatio-temporal data analytics. Applications include manufacturing, energy and hydrogen infrastructure, transportation, aviation safety, disaster response, and medical imaging.
Education
- 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. Physics, Peking University, China 2011
Research Website URL
Google Scholar URL
Research Interests
My research develops efficient and scalable machine learning methods for high-dimensional, spatio-temporal, and sequential data arising in complex engineered and scientific systems, with the goal of enabling reliable monitoring, decision making, and optimization. My work lies at the intersection of machine learning, statistics, and industrial and systems engineering. My main research areas include:
- Reinforcement learning and sequential decision making: offline and goal-conditioned reinforcement learning, Bayesian optimization, and adaptive sampling for data-efficient decision making.
- Generative models: flow-based and diffusion-style generative models, including flow matching and shape-constrained generation for images and spatio-temporal data.
- Anomaly detection and process monitoring: detection, localization, and diagnosis of anomalies in complex, high-dimensional systems.
- Causal machine learning: causal discovery and inference for system modeling and decision support.
- Tensor-based algorithms: scalable tensor and low-rank methods for data fusion and modeling of heterogeneous, high-dimensional data.
Courses
2026 Fall
| Course Number | Course Title |
|---|---|
| IEE 795 | Continuing Registration |
| IEE 799 | Dissertation |
| DSE 795 | Continuing Registration |
2026 Spring
| Course Number | Course Title |
|---|---|
| IEE 795 | Continuing Registration |
| IEE 792 | Research |
| IEE 799 | Dissertation |
| DSE 795 | Continuing Registration |
| IEE 577 | Data Sci Sys Decision Analyt |
| IEE 790 | Reading and Conference |
2025 Fall
| Course Number | Course Title |
|---|---|
| IEE 795 | Continuing Registration |
| CSE 599 | Thesis |
| IEE 580 | Practicum |
| IEE 784 | Internship |
| IEE 792 | Research |
| DSE 795 | Continuing Registration |
| IEE 605 | Foundations of Information Sys |
2025 Spring
| Course Number | Course Title |
|---|---|
| IEE 795 | Continuing Registration |
| IEE 792 | Research |
| IEE 799 | Dissertation |
| DSE 799 | Dissertation |
| DSE 795 | Continuing Registration |
| DSE 792 | Research |
| DSE 580 | Practicum |
| IEE 577 | Data Sci Sys Decision Analyt |
2024 Fall
| Course Number | Course Title |
|---|---|
| IEE 795 | Continuing Registration |
| IEE 580 | Practicum |
| IEE 784 | Internship |
| IEE 792 | Research |
| IEE 799 | Dissertation |
| IEE 599 | Thesis |
| IEE 584 | Internship |
| DSE 580 | Practicum |
| DSE 792 | Research |
| DSE 799 | Dissertation |
| IEE 640 | Prob. & Stochastic Processes |
| IEE 790 | Reading and Conference |
| DSE 792 | Research |
2024 Summer
| Course Number | Course Title |
|---|---|
| IEE 795 | Continuing Registration |
| IEE 584 | Internship |
| IEE 795 | Continuing Registration |
| IEE 584 | Internship |
2024 Spring
| Course Number | Course Title |
|---|---|
| IEE 795 | Continuing Registration |
| IEE 792 | Research |
| DSE 792 | Research |
| DSE 799 | Dissertation |
| IEE 605 | Foundations of Information Sys |
| DSE 799 | Dissertation |
2023 Fall
| Course Number | Course Title |
|---|---|
| IEE 795 | Continuing Registration |
| IEE 580 | Practicum |
| IEE 784 | Internship |
| IEE 792 | Research |
| IEE 799 | Dissertation |
| IEE 599 | Thesis |
| IEE 584 | Internship |
| DSE 580 | Practicum |
| DSE 792 | Research |
| IEE 640 | Prob. & Stochastic Processes |
| IEE 790 | Reading and Conference |
| DSE 792 | Research |
2023 Summer
| Course Number | Course Title |
|---|---|
| IEE 795 | Continuing Registration |
| IEE 795 | Continuing Registration |
2023 Spring
| Course Number | Course Title |
|---|---|
| IEE 795 | Continuing Registration |
| IEE 580 | Practicum |
| IEE 784 | Internship |
| IEE 792 | Research |
| IEE 799 | Dissertation |
| IEE 584 | Internship |
| DSE 792 | Research |
| IEE 790 | Reading and Conference |
| IEE 577 | Data Sci Sys Decision Analyt |
| IEE 792 | Research |
2022 Fall
| Course Number | Course Title |
|---|---|
| IEE 795 | Continuing Registration |
| IEE 474 | Quality Control |
| IEE 784 | Internship |
| IEE 792 | Research |
| IEE 799 | Dissertation |
| IEE 599 | Thesis |
| IEE 584 | Internship |
| DSE 580 | Practicum |
2022 Summer
| Course Number | Course Title |
|---|---|
| IEE 795 | Continuing Registration |
| IEE 795 | Continuing Registration |
2022 Spring
| Course Number | Course Title |
|---|---|
| IEE 795 | Continuing Registration |
| IEE 580 | Practicum |
| IEE 599 | Thesis |
| IEE 792 | Research |
| IEE 799 | Dissertation |
| IEE 584 | Internship |
| STP 599 | Thesis |
| IEE 577 | Data Sci Sys Decision Analyt |
| CSE 598 | Special Topics |
2021 Fall
| Course Number | Course Title |
|---|---|
| IEE 795 | Continuing Registration |
| IEE 474 | Quality Control |
| IEE 784 | Internship |
| IEE 792 | Research |
| IEE 799 | Dissertation |
| IEE 599 | Thesis |
| IEE 584 | Internship |
| IEE 580 | Practicum |
| IEE 584 | Internship |
Honors / Awards
- Best Conference Paper Award, IEEE International Conference on Automation Science and Engineering (CASE), for the paper “Multi-modal Generative Modeling of Event Sequences and Time Series for Solar PV Systems,” 2025
- Best Paper Award Finalist, International Conference on Quality, Statistics, and Reliability (ICQSR), for the paper “Exact Multistage Bayesian Optimization,” 2025
- Best Refereed Paper Competition Finalist, Quality, Statistics, and Reliability (QSR) Section of INFORMS, for the paper “Multi-physics Guided Generative Diffusion Models with Manufacturing Applications,” 2024
- Best Track Paper Competition, Data Analytics and Information Systems (DAIS) Division of IISE, for the paper “Interpretation and visualization of distance covariance with the additive decomposition of correlations formula,” 2023
- Best Paper Award, IEEE Conference on Automation Science and Engineering (CASE), for the paper “Long-Short Term Spatiotemporal Tensor Prediction for Passenger Flow Profile,” 2020
- IISE Transactions Best Paper Award, Focus Issue on Quality and Reliability Engineering, for the paper “Weakly correlated profile monitoring based on sparse multi-channel functional principal component analysis,” 2019
- American Society for Quality (ASQ) Brumbaugh Award, for the paper “Multiple Profiles Sensor-Based Monitoring and Anomaly Detection,” Journal of Quality Technology, 2019
- Best Paper Award, Data Mining Section of INFORMS (Theoretical Track), for the paper “Multiple Tensor-on-Tensor Regression: An Approach for Modeling Processes with Heterogeneous Sources of Data,” 2018
- Best Paper Award, Data Mining Section of INFORMS (Applied Track), for the paper “Dynamic Multivariate Functional Data Modeling via Sparse Subspace Learning,” 2017
- 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