Andi Wang joined the University of Wisconsin-Madison in Aug 2024, as an assistant professor in the Department of Industrial and Systems Engineering. Before that, he was an assistant professor at The School of Manufacturing Systems and Networks, Ira A. Fulton Schools of Engineering, Arizona State University.

Andi Wang’s research mission is to develop principled approaches and methodologies of data science and machine learning to address the special challenges for their application in engineering processes and systems, through predictive analytics, monitoring, diagnostics, design optimization, and model-based control.

Methodologies: Representation Learning, Multitask Learning, Heterogeneous Data Fusion, Distributed Optimization, Bayesian Optimization, Anomaly Detection
Applications: Nuclear Engineering, Additive Manufacturing, Semiconductor Manufacturing, Steel Rolling

Research interests

Multi-sensor data fusion for manufacturing processes and systems for performance and quality prediction, mode classifications, root cause diagnostics, and process adjustment.
Surrogate modeling and design optimization for multi-stage/multiphysics engineering systems.
Distributed and federated learning targeted at engineering and industrial applications.

Awards

2023 IISE Transactions, Best Application Paper Award
2023 DAIS Division of IISE, Best Paper Award Winner
2023 QCRE Division of IISE, Best Paper Award Finalist (Nominated)
2022 IISE Transactions, Feature Article
2021 IISE Transactions, Best Paper Award
2020 Quality, Statistics and Reliability Section of INFORMS, Best Paper Award Finalist (Nominated)
2020 QCRE Division of IISE, Best Student Paper Award Finalist (Nominated)
2019 Data Mining Section of INFORMS, Best Paper Award Finalist (Nominated)
2018 SME Education Foundation, E. Wayne Kay Graduate Scholarship
2017 IISE Transactions, Feature Article

Recent publications

Wang, A. (2024). Manufacturing Data Fusion: A Case Study with Steel Rolling Processes. In Multimodal and Tensor Data Analytics for Industrial Systems Improvement (pp. 281–295). Springer International Publishing Cham.
Xie, X., Xian, X., Li, D., & Wang, A. (2024). Adversarial Client Detection via Non-parametric Subspace Monitoring in the Internet of Federated Things. IISE Transactions(just-accepted), 1--23.
Guo, S., Ko, H., & Wang, A. (2024). Applications and prospects of machine learning for aerosol jet printing: A review. IISE Transactions, 56(10), 1038--1057.
Jiang, M., Wang, A., Li, Z., & Tsung, F. (2023). A unified probabilistic framework for spatiotemporal passenger crowdedness inference within urban rail transit network. In 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE) (pp. 1–8).
Li, Z., Yan, H., Zhang, C., Wang, A., Ketter, W., & Tsung, F. (2023). Graph-aware Tensor Topic Models for Individualized Passenger Travel Pattern Clustering. In IISE Annual Conference and Expo.
Xu, H., Du, J., & Wang, A. (2023). Ano-SuPs: Multi-size anomaly detection for manufactured products by identifying suspected patches. arXiv preprint arXiv:2309.11120.
Luan, X., Zou, Q., Li, J., & Wang, A. (2023). Efficient online cross-covariance monitoring with incremental SVD: An approach for the detection of emerging dependency patterns in IoT systems. arXiv preprint arXiv:2310.13124.
Wang, A., Yan, H., & Du, J. (2023). Interpretation and visualization of distance covariance through additive decomposition of correlations formula. arXiv preprint arXiv:2305.14767.
Miao, H., Wang, A., Li, B., Chang, T., & Shi, J. (2023). Process modeling with multi-level categorical inputs via variable selection and level aggregation. IISE Transactions, 55(4), 363--376.
Li, Z., Yan, H., Zhang, C., Wang, A., Ketter, W., Sun, L., & Tsung, F. (2023). Tensor Dirichlet process multinomial mixture model for passenger trajectory clustering. arXiv preprint arXiv:2306.13794.

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