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Prof. TAN Matthias Hwai-yong (陳怀勇博士)

BEng(UTM), MEng(NUS), PhD(Georgia Tech)

Associate Professor

Contact Information

Office:  LAU-16-264
Phone: (+852) 34425651
Email: matthtan@cityu.edu.hk
Web: Google Scholar

Research Interests

  • Statistical learning
  • Uncertainty Quantification
  • Design and Analysis of Physical and Computer Experiments
  • Robust Parameter Design
  • Engineering and Industrial Statistics
  • Bayesian statistics
Matthias Hwai Yong Tan received his B.Eng. degree in mechanical-industrial engineering from the Universiti Teknologi Malaysia, an M.Eng. degree in industrial and systems engineering from the National University of Singapore and a Ph.D. degree in industrial and systems engineering from Georgia Institute of Technology. His research interests include uncertainty quantification and applied statistics. In particular, his research aims to develop rigorous statistical methods for engineering simulation models with the goal of solving engineering uncertainty quantification problems. This often involves the use of a statistical model for time consuming simulations such as solving time-dependent 3D PDE's via the finite element method, solving the Navier-Stokes equation via the finite volume method, and computing the expectation of a simulator output with respect to noise factor inputs.

Academic Appointments
July 1, 2019 - present : Associate Professor at School of Data Science, CityU
July 1, 2018 - June 30, 2019 : Assistant Professor at School of Data Science, CityU
Aug 1, 2013 - June 30, 2018 : Assistant Professor at the Department of Systems Engineering and Engineering Management, CityU

For prospective students
I am looking for students with EXCELLENT academic record in the area of engineering, mathematics, or statistics to do a PhD. If interested, please send me your CV by email.

External Grants Obtained in the Capacity of PI
1. Early Career Scheme (ECS), Project No 9048005, Research Grants Council of Hong Kong
2. General Research Fund (GRF), Project No 11226716, Research Grants Council of Hong Kong
3. General Research Fund (GRF), Project No 11201117, Research Grants Council of Hong Kong
4. General Research Fund (GRF), Project No 11205118, Research Grants Council of Hong Kong
5. General Research Fund (GRF), Project No 11201519, Research Grants Council of Hong Kong
6. General Research Fund (GRF), Project No 11209622, Research Grants Council of Hong Kong

Peer Reviewed Journal Papers
1. Tan, M.H.Y. and Ng, S.H.* (2009). “Estimation of the Mean and Variance Response Surfaces when the Means and Variances of the Noise Variables are Unknown,” IIE Transactions, 41(11), 942-956.
2. Tan, M.H.Y.* and Wu, C.F.J. (2012). “Generalized Selective Assembly,” IIE Transactions, 44(1), 27-42. (Feature Article: IE Magazine 2011, 43(10), page 50)
3. Tan, M.H.Y.* and Wu, C.F.J.* (2012). “Robust Design Optimization with Quadratic Loss Derived From Gaussian Process Models,” Technometrics, 54(1), 51-63.
4. Tan, M.H.Y.* and Shi, J. (2012). “A Bayesian Approach for Interpreting Mean Shifts in Multivariate Quality Control,” Technometrics, 54(3), 294-307.
5. Tan, M.H.Y.* and Wu, C.F.J.* (2013). “A Bayesian Approach for Model Selection in Fractionated Split Plot Experiments with Applications in Robust Parameter Design,” Technometrics, 55(3), 359-372.
6. Tan, M.H.Y. (2013). “Minimax Designs for Finite Design Regions,” Technometrics, 55(3), 346-358.
7. Sun,Y., Heo, Y., Tan, M.H.Y., Xie, H., Wu, C.F.J., and Augenbroe, G.* (2014). “Uncertainty Quantification of Microclimate Variables in Building Energy Models,” Journal of Building Performance Simulation, 7(1), 17-32.
8. Tan, M.H.Y. (2014). “Bounded Loss Functions and the Characteristic Function Inversion Method for Computing Expected Loss,” Quality Technology and Quantitative Management, 11(4), 401-421.
9. Tan, M.H.Y. (2015). "Sequential Bayesian Polynomial Chaos Model Selection for Estimation of Sensitivity Indices," SIAM/ASA Journal on Uncertainty Quantification, 3(1), 146-168.
10. Tan, M.H.Y. (2015). "Stochastic Polynomial Interpolation for Uncertainty Quantification with Computer Experiments," Technometrics, 57(4), 457-467.
11. Tan, M.H.Y. (2015). "Robust Parameter Design with Computer Experiments Using Orthonormal Polynomials," Technometrics, 57(4), 468-478.
12. Simmons, B.*, Tan, M.H.Y., Wu, C.F.J, and Augenbroe, G. (2015). “Determining the Cost Optimum Among a Discrete Set of Building Technologies to Satisfy Stringent Energy Targets,” Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 29(4), 417-427.
13. Tan, M.H.Y. and Zhang, Z.* (2016). “Wind Turbine Modeling with Data-driven Methods and Radially Uniform Designs,” IEEE Transactions on Industrial Informatics, 12(3), 1261-1269.
14. Tan, M.H.Y. (2016). “Monotonic Quantile Regression with Bernstein Polynomials for Stochastic Simulation,” Technometrics, 58(2), 180-190.
15. Han, M. and Tan, M.H.Y.* (joint first author) (2016). “Integrated Parameter and Tolerance Design with Computer Experiments”, IIE Transactions, 48(11), 1004-1015.
16. Tan, M.H.Y. (2017). “Polynomial Metamodeling with Dimensional Analysis and the Effect Heredity Principle,” Quality Technology and Quantitative Management, 14(2), 195-213.
17. Tan, M.H.Y. (2017). “Monotonic Metamodels for Deterministic Computer Experiments,” Technometrics, 59(1), 1-10.
18. Han, M. and Tan, M.H.Y.* (joint first author) (2017). “Optimal Robust and Tolerance Design for Computer Experiments with Mixture Proportion Inputs”, Quality and Reliability Engineering International, 33(8), 2255-2267.
19. Tan, M.H.Y. (2018). "Gaussian Process Modeling of a Functional Output with Information from Boundary and Initial Conditions and Analytical Approximations", Technometrics, 60(2), 209-221.
20. Tan, M.H.Y. (2018). “Gaussian Process Modeling with Boundary Information,” Statistica Sinica, 28(2), 621-648.
21. Tan, M. H.Y.* and Li, G. (2019). "Gaussian Process Modeling Using the Principle of Superposition", Technometrics, 61(2), 202-218.
22. Li, G., Tan, M. H.Y.*, and Ng, S.H. (2018). "Metamodel-based Optimization of Stochastic Computer Models for Engineering Design under Uncertain Objective Function", IISE Transactions, 51(5), 517-530.
23. Tabatabaei, M.*, Lovison, A, Tan, M.H.Y., Hartikainen, M., and Miettinen, K. (2018). “ANOVA-MOP: Anova Decomposition for Multiobjective Optimization,” SIAM Journal on Optimization, 28(4), 3260-3289.
24. Li, G.*, Ng, S.H., and Tan, M.H.Y. (2018). “Bayesian Optimal Designs for Efficient Estimation of the Optimum Point with Generalised Linear Models,” Quality Technology and Quantitative Management, 17(1), 89-107.
25. Han, M., Liu, X., Huang, M., and Tan, M.H.Y.* (2019). “Integrated Parameter and Tolerance Optimization of a Centrifugal Compressor Based on a Complex Simulator,” Journal of Quality Technology, 52(4), 404-421.
26. Hong, L., Tan, M.H.Y., and Ye, Z.* (2019). “Nonparametric Link Functions with Shape Constraints in Stochastic Degradation Processes: Application to Emerging Contaminants,” Journal of Quality Technology, 52(4), 370-384.
27. Tan, M.H.Y. (2019). "Gaussian Process Modeling of Finite Element Models with Functional Inputs," SIAM/ASA Journal on Uncertainty Quantification, 7(4), 1133-1161.
28. Tan, M.H.Y. (2020). "Bayesian Optimization of Expected Quadratic Loss for Multiresponse Computer Experiments with Internal Noise," SIAM/ASA Journal on Uncertainty Quantification, 8(3), 891-925.
29. Sheng, C., Tan, M.H.Y.*, and Zou, L. (2020). "Maximum Expected Entropy Transformed Latin Hypercube Designs," Journal of Applied Statistics (just-accepted).
30. Jiang, F., Tan, M.H.Y.*, and Tsui, K.L. (2020). "Multiple-target Robust Design with Multiple Functional Outputs," IISE Transactions (just-accepted).
31. Jiang, F., and Tan, M.H.Y.* (2021). "Shifted Log Loss Gaussian Process Model for Expected Quality Loss Prediction in Robust Parameter Design," Quality Technology and Quantitative Management (just-accepted).
32. Li, Z. and Tan, M.H.Y.* (2021). "A Gaussian Process Emulator Based Approach for Bayesian Calibration of a Functional Input," Technometrics (just-accepted).
33. Ye, W. and Tan, M.H.Y.* (2021)."Multi-fidelity Gaussian process modeling with boundary information," Applied Stochastic Models in Business and Industry (just-accepted).

Peer Reviewed Conference Papers
1. Sun,Y., Heo, Y., Xie, H., Tan, M.H.Y., Wu, C.F.J., and Augenbroe, G. (2011). “Uncertainty Quantification of Microclimate Variables in Building Energy Simulation,” Proceedings of Building Simulation 2011: 12th Conference of the International Building Performance Simulation Association, Sydney, Australia, 2423-2430.
2. Simmons, B., Tan, M.H.Y., Wu, C.F.J, Yu, Y., and Augenbroe, G. (2013). “Finding the Cost Optimal Mix of Building Energy Technologies that Satisfies a Set Operational Energy Reduction Target,” Proceedings of Building Simulation 2013: 13th Conference of the International Building Performance Simulation Association, Chambéry, France, 1852-1859.
3. Li, G., Zhang, J., Wang, Y., Liu, C., Tan, M.H.Y., Lin, Y., Zhang, W., Feng, J., and Zhang, T. (2020). “Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts,” Advances in Neural Information Processing Systems 33 (NeurIPS 2020).

Book Chapters and Others
1. Wu, C.F.J. and Tan, M.H.Y. (2013). “Youden Address: Quality Technology in the High-Tech Age,” ASQ Statistics Division Newsletter, 32(1), 13-17.
2. Li, Z., & Tan, M.H.Y.* (2022). Improving Gaussian process emulators with boundary information. In A. Steland & K.L. Tsui (Eds.), Artificial intelligence, big data and data science in statistics: Challenges and solutions in environmetrics, the natural sciences and technology. Cham: Springer Nature Switzerland AG.

You are welcome to request any of my papers or Matlab code from me by email.

Slides for some of my presentations
1. Opening Up the Black Box Gaussian Process Modeling Using Information from Partial Differential Equation Models
2. Gaussian process modeling with boundary information
3. Gaussian process modeling and optimization of simulators for physical systems
4. Interpolating and Monotonic Polynomial Models for Computer Experiments
5. Monotonic Metamodels for Deterministic Computer Experiments
6. Minimax Designs for Finite Design Regions
7. Generalized Selective Assembly
8. Bounded Loss Functions and the Characteristic Function Inversion Method for Computing Expected Loss
9. A Bayesian Approach for Interpreting Mean Shifts in Multivariate Quality Control

Former PhD students
1. HAN Mei – Sep 2014 to July 2018, Oral defense on 5 July 2018.
Currently: Associate professor at Nanjing University of Aeronautics and Astronautics

2. LI Zhaohui (Chinese Academy of Sciences, Joint PhD) – Sep 2018 to May 2020, Oral defense on 27 May 2020.
Currently: Assistant Professor at Chinese Academy of Sciences

3. JIANG Fan - Sep 2016 to Feb 2021, Oral defense on 25 Jan 2021.
Currently: Lecturer at Beijing Information Science and Technology University

4. SHENG Chong (Nankai University, Joint PhD) - Sep 2018 to May 2021, Oral defense on 21 May 2021.
Currently: Assistant research fellow at PLA Academy of Military Sciences

5. YE Wenxing - Sep 2018 to May 2023, Oral defense on 20 March 2023.
Currently: Assistant professor at Foshan University



Former postdoctoral research assistant
1. WANG Yan - Dec 2017 to June 2018
Currently: Associate professor at Beijing University of Technology

2. ZOU Lu - July 2018 to July 2019

Former Master's thesis students
1. Rooh Ullah
Currently: Principal Consultant at Explora

2. Li Shuchang

Former visiting PhD students
1. Mohammad Tabatabaei
Currently: Data Scientist at Unity Technologies

Reviewer duties: 28 SCIE journals, 1 conference proceeding, 1 edited book, and grant proposals for a national funding agency.

External examiner for the Bachelor of Science (Hons) Statistical Computing and Operations Research program at Universiti Tunku Abdul Rahman, Malaysia: https://fsc.utar.edu.my/External_Examiners.php

Last update date : 23 Jan 2024