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Dr. TAN Matthias Hwai-yong (陳怀勇博士)

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

Associate Professor

Contact Information

Office:  YEUNG-P6614
Phone: (+852) 34425651
Email: matthtan@cityu.edu.hk
Web: Google Scholar

Research Interests

  • Statistics
  • Uncertainty Quantification in Computer Simulations
  • Design and Analysis of Physical and Computer Experiments
  • Robust Parameter Design
  • Engineering and Industrial Statistics
  • Statistical Learning
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 or statistics to do a PhD. If interested, please send me your CV by email. Please click on the link below for more information on my research, which contains illustrations from a collaborative computer experiments project led by me
:
https://drive.google.com/file/d/17-ebON7YdsW7pG6IKZgZ52NPP6AofIUd/view?usp=sharing

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

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.
http://www.tandfonline.com/doi/abs/10.1080/07408170902735418#.U-XfN_mSxqU
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)
http://www.tandfonline.com/doi/abs/10.1080/0740817X.2010.551649#.U-Xfb_mSxqU
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.
http://www.tandfonline.com/doi/abs/10.1080/00401706.2012.648866#.U-XfhPmSxqU
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.
http://www.tandfonline.com/doi/abs/10.1080/00401706.2012.694789#.U-Xfm_mSxqU
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.
http://www.tandfonline.com/doi/abs/10.1080/00401706.2013.778790#.U-XgCvmSxqU
6. Tan, M.H.Y. (2013). “Minimax Designs for Finite Design Regions,” Technometrics, 55(3), 346-358.
http://www.tandfonline.com/doi/full/10.1080/00401706.2013.804439#.U-XgIfmSxqU
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.
http://www.tandfonline.com/doi/abs/10.1080/19401493.2012.757368#.U-Xf8vmSxqU
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.
http://www.tandfonline.com/doi/abs/10.1080/16843703.2014.11673353
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.
http://epubs.siam.org/doi/abs/10.1137/130931175
10. Tan, M.H.Y. (2015). "Stochastic Polynomial Interpolation for Uncertainty Quantification with Computer Experiments," Technometrics, 57(4), 457-467.
http://amstat.tandfonline.com/doi/full/10.1080/00401706.2014.950431
11. Tan, M.H.Y. (2015). "Robust Parameter Design with Computer Experiments Using Orthonormal Polynomials," Technometrics, 57(4), 468-478.
http://amstat.tandfonline.com/doi/full/10.1080/00401706.2014.969446#abstract
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.
http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=9994238&fileId=S0890060415000414
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.
http://ieeexplore.ieee.org/abstract/document/7414473/
14. Tan, M.H.Y. (2016). “Monotonic Quantile Regression with Bernstein Polynomials for Stochastic Simulation,” Technometrics, 58(2), 180-190.
http://amstat.tandfonline.com/doi/abs/10.1080/00401706.2015.1027066#.VXoyPc-qqko
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.
http://www.tandfonline.com/doi/abs/10.1080/0740817X.2016.1167289
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.
http://www.tandfonline.com/doi/full/10.1080/16843703.2016.1208491
17. Tan, M.H.Y. (2017). “Monotonic Metamodels for Deterministic Computer Experiments,” Technometrics, 59(1), 1-10.
http://www.tandfonline.com/doi/full/10.1080/00401706.2015.1105759
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.
https://onlinelibrary.wiley.com/doi/10.1002/qre.2188
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.
https://www.tandfonline.com/doi/full/10.1080/00401706.2017.1345702
20. Tan, M.H.Y. (2018). “Gaussian Process Modeling with Boundary Information,” Statistica Sinica, 28(2), 621-648.
http://www3.stat.sinica.edu.tw/statistica/oldpdf/A28n24.pdf
21. Tan, M. H.Y.* and Li, G. (2019). "Gaussian Process Modeling Using the Principle of Superposition", Technometrics, 61(2), 202-218.
https://amstat.tandfonline.com/doi/abs/10.1080/00401706.2018.1473799?journalCode=utch20#.XceJYjMzY2w
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.
https://www.tandfonline.com/doi/abs/10.1080/24725854.2018.1504355
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.
https://epubs.siam.org/doi/abs/10.1137/16M1096505
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.
https://www.tandfonline.com/doi/full/10.1080/16843703.2018.1542965
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.
https://www.tandfonline.com/doi/full/10.1080/00224065.2019.1611358
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.
https://www.tandfonline.com/doi/full/10.1080/00224065.2019.1611353
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.
https://epubs.siam.org/doi/abs/10.1137/17M1112942
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.
https://epubs.siam.org/doi/abs/10.1137/19M1272676?casa_token=nZgqtgdqotEAAAAA:i64xa803bzUTwtf2qSC0M1FzXHnfMexT4Iw5frcJDC2PzbnphBM3VIP-XI9CgTVpUky5aG9E-UU
29. Sheng, C., Tan, M.H.Y.*, and Zou, L. (2020). "Maximum Expected Entropy Transformed Latin Hypercube Designs," Journal of Applied Statistics (just-accepted).
https://www.tandfonline.com/doi/full/10.1080/02664763.2020.1786674?casa_token=BcfCQl2Vm38AAAAA%3AO-gd_yzkCIJmqmR_Yix_8ZZPtLbF8OXfYSutIkW46cL1h6CAq6Dgon7moqfxCElmYyICrgXQ5ZWv
30. Jiang, F., Tan, M.H.Y.*, and Tsui, K.L. (2020). "Multiple-target Robust Design with Multiple Functional Outputs," IISE Transactions (just-accepted).
https://www.tandfonline.com/doi/full/10.1080/24725854.2020.1823532?casa_token=uVBdWmn3QDEAAAAA%3AU1tzjiLxrS9qS_SUwnlcXkbhy1r-7l3oC7CXRnnn6soMwQtjiVm5g-DdeaWOon6ElD2kkGaneY5O
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).

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.
http://ibpsa.org/proceedings/BS2011/P_1758.pdf
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.
http://www.ibpsa.org/proceedings/BS2013/p_1201.pdf
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).
https://proceedings.neurips.cc/paper/2020/hash/657b96f0592803e25a4f07166fff289a-Abstract.html

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.

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
https://drive.google.com/open?id=1rD1WJeB0L_vkoMAEcVmeYrKPo7TIaa_g
https://drive.google.com/open?id=1pZ_HMubMetBHkOxMZS0xWm1JcfOeqDja
2. Gaussian process modeling with boundary information
https://drive.google.com/file/d/1EI0W0mkrRpEPwqqNQz2_cb_MH18xEP68/view?usp=sharing
3. Gaussian process modeling and optimization of simulators for physical systems
https://drive.google.com/open?id=1GVI5vQMyf6Cq8tjf6hqSl87w7LcIrVcd
4. Interpolating and Monotonic Polynomial Models for Computer Experiments
https://drive.google.com/open?id=1d4IGCxmAnKlD7K66jbT21UTTa0N2o4pI
5. Monotonic Metamodels for Deterministic Computer Experiments
https://drive.google.com/file/d/1yCaiYiT1RpGwLkHWfjrS1yzV1DDnrSod/view?usp=sharing
6. Minimax Designs for Finite Design Regions
https://drive.google.com/open?id=1GilxA9erPbYzgJNGmONa32wC5Nl5tdC8
7. Generalized Selective Assembly
https://drive.google.com/open?id=1giOxdOn74qxnaBNJ5dHX4tbGUv_slP3-
8. Bounded Loss Functions and the Characteristic Function Inversion Method for Computing Expected Loss
https://drive.google.com/open?id=1vYBsvYNp00o4HpyZ16td4HGcrxOURPgs
9. A Bayesian Approach for Interpreting Mean Shifts in Multivariate Quality Control
https://drive.google.com/open?id=1Hkqes2EcEXNUG5eU4Ri35E0Fizqt8l9g

Former PhD students
1. Han Mei – Sep 2014 to July 2018, Oral defense on 5 July 2018.
2. Li Zhaohui (Chinese Academy of Sciences, Joint PhD) – Sep 2018 to May 2020, Oral defense on 27 May 2020.
3. Jiang Fan - Sep 2016 to Feb 2021, Oral defense on 25 Jan 2021.

Former Master's thesis students
1. Rooh Ullah

Former visiting PhD students
1. Mohammad Tabatabaei




Last update date : 03 Mar 2021