2011年8月-2016年7月 清華大學自動化系控制科學與工程專業學習，獲工學博士學位

2007年8月-2011年7月 在清華大學自動化系專△業學習，獲工學學士①學位

2018.10-至今 清華大學自動化系 助理教授（特別研◣究員）

2016.10-2018.10 美國八個碩大康奈爾大學 & 清華大學 博士後

IEEE Member

中∴國自動化學會大數據專委會 委員

中國化工學會信息技術專業委員會 青年委員

《Journal of Process Control》、《Control Engineering Practice》、《IEEE Trans. on Industrial Electronics》、《Computers & Chemical Engineering》等期刊審稿人√

[1] 大數據解析及工業應用

[2] 數據驅動的不確定規劃技術及應用

[3] 過程監控與故障診斷

[4] 數據驅動的工業過程建模

近年來，大數據蓬勃發緩緩點了點頭展為控制學科帶來了新的機〗遇與挑戰。隨著數據信息量與計算機運算能力的快】速增長，人類處理復雜決策問題的能力正在不斷增強。一方面，通過有效地收集分析數◎據，人們能夠更好地感知並適應環境神劫的變化，並對決策進行針對性〗調整；另一方面，基於大數據更深層次一百三十六個仙帝懸浮在他身后的不確定信息能被挖◤掘出來，在此基礎上，不斷地提◣高智能控制與智能決策的水平。本人研究針對數據驅動的建模、監控、診斷以及優化方法展開，並以實際工業制造過程←為背景，將控制理這寶物論、人工智能以及運籌學進行●有機結合，具有多學科交叉的特點。累計發表¤期刊論文近20篇，被引用700余次，另有5項國家發明專利已授權。

1.1st International Conference on Industrial Artificial Intelligence Best Paper Award, 2019

2.Springer Doctorate Theses Award，2018

3.清華大學“紫荊學者”，2016

4.清華大學優秀博士論文一等獎，2016

5.北京市優秀畢業生◥，2016

6.清♀華大學教學成果獎一等獎，2016

7.清華大學“一二？九”輔導員獎，2015

學術專著

C. Shang (2018). Dynamic Modeling of Complex Industrial Processes: Data-Driven Methods and Application Research. Springer, 2018. ISBN 978-981-10-6676-4. (143 pages)

主要論文

[J18] Shang, C., & You, F. (2019). Data analytics and machine learning for smart process manufacturing: Recent advances and perspectives in the big data era. To appear in Engineering.

[J17] Shang, C., & You, F. (2019). A data-driven robust optimization approach to scenario-based stochastic model predictive control. Journal of Process Control, 75, 24-39.

[J16] Shang, C., & You, F. (2018). Distributionally robust optimization for planning and scheduling under uncertainty. Computers & Chemical Engineering, 110, 53-68.

[J15] Shang, C., Yang, F., Huang, B., & Huang, D. (2018). Recursive slow feature analysis for adaptive monitoring of industrial processes. IEEE Transactions on Industrial Electronics, 65(11), 8895-8905.

[J14] Li, F., Zhang, J., Shang, C., Huang, D., Oko, E., & Wang, M. (2018). Modelling of a post-combustion CO2 capture process using deep belief network. Applied Thermal Engineering, 130, 997-1003

[J13] Shang, C., Huang, X., & You, F. (2017). Data-driven robust optimization based on kernel learning. Computers & Chemical Engineering, 106, 464-479.

[J12] Gao, X., Shang, C., Huang, D., & Yang, F. (2017). A novel approach to monitoring and maintenance of industrial PID controllers. Control Engineering Practice, 64, 111-126.

[J11] Gao, X., Zhang, J., Yang, F., Shang, C., & Huang, D. (2017). Robust proportional–integral-derivative (PID) design for parameter uncertain second-order plus time delay (SOPTD) processes based on reference model approximation. Industrial & Engineering Chemistry Research, 56(41), 11903-11918.

[J10] Gao, X., Yang, F., Shang, C., & Huang, D. (2017). A novel data-driven method for simultaneous performance assessment and retuning of PID controllers. Industrial & Engineering Chemistry Research, 56(8), 2127-2139.

[J9] Shang, C., Huang, B., Yang, F., & Huang, D. (2016). Slow feature analysis for monitoring and diagnosis of control performance. Journal of Process Control, 39, 21-34.

[J8] Guo, F., Shang, C., Huang, B., Wang, K., Yang, F., & Huang, D. (2016). Monitoring of operating point and process dynamics via probabilistic slow feature analysis. Chemometrics and Intelligent Laboratory Systems, 151, 115-125.

[J7] Gao, X., Yang, F., Shang, C., & Huang, D. (2016). A review of control loop monitoring and diagnosis: Prospects of controller maintenance in big data era. Chinese Journal of Chemical Engineering, 24(8), 952-962.

[J6] Shang, C., Huang, B., Yang, F., & Huang, D. (2015). Probabilistic slow feature analysis-based representation learning from massive process data for soft sensor modeling. AIChE Journal, 2015, 61(12), 4126-4139.

[J5] Shang, C., Yang, F., Gao, X., Huang, X., Suykens, J. A. K., & Huang, D. (2015). Concurrent monitoring of operating condition deviations and process dynamics anomalies with slow feature analysis. AIChE Journal, 2015, 61(11), 3666-3682.

[J4] Shang, C., Huang, X., Suykens, J. A. K., & Huang, D. (2015) Enhancing dynamic soft sensors based on DPLS: a temporal smoothness regularization approach. Journal of Process Control, 28, 17-26.

[J3] Gao, X., Shang, C., Jiang, Y., Huang, D., & Chen, T. (2014). Refinery scheduling with varying crude: A deep belief network classification and multimodel approach. AIChE Journal, 60(7), 2525-2532.

[J2] Shang, C., Yang, F., Huang, D., & Lyu, W. (2014). Data-driven soft sensor development based on deep learning technique. Journal of Process Control, 24(3), 223-233.

[J1] Shang, C., Gao, X., Yang, F., & Huang, D. (2014). Novel Bayesian framework for dynamic soft sensor based on support vector machine with finite impulse response. IEEE Transactions on Control Systems Technology, 22(4), 1550-1557.

發明專利

1. 黃德先，尚超，楊帆，高莘青. 基於緩慢特征回歸的動態∮軟測量方法和系統: 中國, CN104537260B. (中國專╱利授權號⊙.)

2. 黃德先，尚超，楊帆，高莘青. 基於緩慢特征分析的過程監控方法和系統: 中國, CN104598681B. (中國專利授三皇估計是要來刁難你了權號.)

3. 黃德先，尚超，高莘青，呂文祥. 基於貝葉斯№框架的動態軟測量建模方法及裝置: 中國, CN103279030B. (中國專利授權號.)

4. 吳彬, 尚超, 宋曉玲, 黃德先, 夏月星, 姚佳清, 高莘青, 熊新陽, 朱紹平, 黃富銘. 乙炔法合成氯乙烯生產過程的在線預警方法: 中國, CN105204465B. (中國專利授權號.)

5. 吳彬, 尚超, 宋曉玲, 黃德先, 夏月星, 姚佳清, 高莘青, 熊新陽, 朱紹平, 黃富銘. 聚氯乙烯合成過程低沸塔尾氣冷凝在線監控及★報警方法: 中國, CN105404251B. (中國專利授權號.)