2010年-2015年 清华大学自动化系，获工学博士学位
2013年-2014年 美国麻省理工学院(MIT)，国家留学基金委联合培养项目
2006年-2010年 浙江大学控制科学与工程系，获工学学士学位
2023年-至今 清华大学自动化系，副教授，博士生导师
2020年-2023年 清华大学自动化系，助理教授，博士生导师
2016年-2020年 美国麻省理工学院(MIT)，博士后
TC member, IFAC Technical Committee on Chemical Process Control
TC member, IEEE IES Technical Committee on Data-Driven Control and Monitoring
TC member, IEEE CSS Technical Committee on Process Control
Guest Editors, Machines; Frontiers in Chemical Engineering; Results in Control and Optimization
Science Advances, Joule, Automatica, IEEE TCST, IEEE TASE, IEEE TII, IEEE CDC, ACC, IFAC World Congress等国际期刊和会议审稿人
1. 计算能源智能：机器学习、人工智能与新能源领域前沿交叉研究方向。研究融合机器学习、人工智能、控制理论等技术，以新能源动力储能电池设计为载体，致力于探索能源智能的预测、诊断、控制和优化新方法。
2. 可信机器学习及应用：研究综合领域知识与数据驱动的公平表征、公平建模和公平决策的新方法及其在人工智能治理、计算能源智能等领域中的综合应用。
1. 国家自然科学基金委面上项目，综合电化学知识和机器学习的新能源电池智能管理理论与方法，项目负责人，2023-2026
2. 清华-丰田国际联合基金，基于机器学习的锂金属电解液预测与界面优化技术，项目负责人，2021-2024
3. 国家科技部重点研发计划，面向价值链网络的云制造协同平台研究，课题负责人，2022-2025
4. 国家自然科学基金重大项目，含氢多能源供需系统建模与智能性设计方法，课题骨干，2022-2026
5. MIT-Stanford联合攻关项目“Accelerated Materials Design and Discovery Program”, Data-Driven Design of Li-ion Batteries, 项目骨干, 2017-2020
Intelligent Computing Innovators of China, DeepTech, 2022
Outstanding Reviewer for Journal of Process Control, 2018
瑞士乔诺法(Chorafas)青年研究奖, 2016
北京市优秀毕业生, 2015
清华大学优秀博士论文, 2015
详见：http://www.benbenjiang.net
主要期刊论文
1. Benben Jiang, William E. Gent, Fabian Mohr, Supratim Das, Marc D. Berliner, Michael Forsuelo, Hongbo Zhao, Peter M. Attia, Aditya Grover, Patrick K. Herring, Martin Z. Bazant, Stephen J. Harris, Stefano Ermon, William C. Chueh, Richard D. Braatz. Bayesian learning for rapid prediction of lithium-ion battery cycling protocols. Joule, 2021. (Tsinghua Press Release: www.tsinghua.edu.cn/info/1175/88412.htm) (IF=41.2, Cover Article)
2. Kristen Severson, Peter Attia, Norman Jin, Nicholas Perkins, Benben Jiang, Zi Yang, Michael H. Chen, Muratahan Aykol, Patrick K. Herring, Dimitrios Fraggedakis, Martin Z. Bazant, Stephen J. Harris, William C. Chueh, Richard D. Braatz. Data-driven prediction of battery cycle life before capacity degradation. Nature Energy, 4: 383–391, 2019. (IF=46.4, Cover Article)
3. Benben Jiang, Marc Berliner, Kun Lai, Patrick Asinger, Hongbo Zhao, Patrick Herring, Martin Z. Bazant, Richard D. Braatz. Fast charging design for Lithium-ion batteries via Bayesian optimization. Applied Energy, 307, 118244, 2022.
4. Guijun Ma, Songpei Xu, Benben Jiang, Cheng Cheng, Xin Yang, Yue Shen, Tao Yang, Yunhui Huang, Han Ding, Ye Yuan. Real-time personalized health status prediction of lithium-ion batteries using deep transfer learning. Energy & Environmental Science, 2022
5. Benben Jiang, Xizhe Wang. Constrained Bayesian optimization for minimum-time charging of Lithium-ion batteries. IEEE Control Systems Letters. 6, 1682–1687, 2022.
6. Benben Jiang, Lu Qiugang. Fault detection in industrial systems using maximized divergence analysis approach. IEEE Access, 10, 60674–60681, 2022.
7. Marc D. Berliner, Hongbo Zhao, Supratim Das, Michael Forsuelo, Benben Jiang, William C. Chueh, Martin Z. Bazant, and Richard D. Braatz. Nonlinear identifiability analysis of the porous electrode theory model of lithium-ion batteries. Journal of The Electrochemical Society, 168, 090546, 2021.
8. Benben Jiang, Bofan Zhu. Dynamic Bhattacharyya bound-based approach for fault classification in industrial Processes. IEEE Transactions on Industrial Informatics, 18, 397–404, 2021.
9. Qiugang Lu, Benben Jiang*, Eranda Harinath. Fault diagnosis in industrial processes by maximizing pairwise Kullback-Leibler divergence. IEEE Transactions on Control Systems Technology, 29, 780–785, 2019.
10. Benben Jiang, Yi Luo, Qiugang Lu. Maximized mutual information analysis based on stochastic representation for process monitoring. IEEE Transactions on Industrial Informatics, 15, 1579–1587, 2019.
11. Benben Jiang, Zifeng Guo, Qunxiong Zhu, Gao Huang. Dynamic minimax probability machine-based approach for fault diagnosis using pairwise discriminate analysis. IEEE Transactions on Control Systems Technology, 27, 806–813, 2019.
12. Qiugang Lu, Benben Jiang*, R. Bhushan Gopalunia, Philip D. Loewend, Richard D. Braatz. Sparse canonical variate analysis approach for process monitoring. Journal of Process Control, 71, 90–102, 2018.
13. Qiugang Lu, Benben Jiang, R. Bhushan Gopalunia, Philip D. Loewend, Richard D. Braatz. Locality preserving discriminative canonical variate analysis for fault diagnosis. Computers & Chemical Engineering, 17, 309–319, 2018.
14. Benben Jiang, Richard D. Braatz. Fault detection of process correlation structure using canonical variate analysis-based correlation features. Journal of Process Control, 58, 131–138, 2017.
15. Benben Jiang, Xiaoxiang Zhu, Dexian Huang, Joel A. Paulson, Richard D.Braatz. A combined canonical variate analysis and fisher discriminant analysis (CVA-FDA) approach for fault diagnosis. Computers & Chemical Engineering, 77:1–9, 2015.
16. Benben Jiang, Xiaoxiang Zhu, Dexian Huang, Richard D. Braatz. Canonical variate analysis-based monitoring of process correlation structure using causal feature representation. Journal of Process Control, 32, 109–116, 2015.
17. Leo H. Chiang, Benben Jiang, Xiaoxiang Zhu, Dexian Huang, Richard D. Braatz. Diagnosis of multiple and unknown faults using the causal map and multivariate statistics. Journal of Process Control, 28, 27–39, 2015.
18. Benben Jiang, Fan Yang, Wei Wang, Dexian Huang. Simultaneous identification of bi-directional path models based on process data. IEEE Transactions on Automation Science and Engineering, 12, 666–679, 2015.
19. Benben Jiang, Dexian Huang, Xiaoxiang Zhu, Fan Yang, Richard D. Braatz. Canonical variate analysis-based contributions for fault identification. Journal of Process Control, 26, 17–25, 2015.
20. Benben Jiang, Fan Yang, Wei Wang, Dexian Huang. Simultaneous identification of bi-directional paths in closed-loop systems with colored noise. Automatica, 58, 139–142, 2015.
主要会议论文
1. Benben Jiang, Weike Sun, Richard D. Braatz. An information-theoretic framework for fault detection evaluation and design of optimal dimensionality reduction methods. IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, Poland, Aug. 2018: 1311–1316.
2. Weike Sun, Benben Jiang, Richard D. Braatz. Concurrent canonical variate analysis for process operating condition deviations and dynamic anomalies monitoring. AIChE Annual Meeting, Pittsburgh, USA, Aug. 2018. (Finalist for the CAST Directors Best Student Presentation Award)
3. Benben Jiang, Zifeng Guo, Gao Huang. Pairwise discriminate analysis based minimax probability machine approach for fault diagnosis. IEEE Conference on Control Technology and Applications, Hawaii, USA, Aug. 2017: 1486–1491.
4. Benben Jiang, Qunxiong Zhu, Xiaoxiang Zhu. A generalized instrumental variable method based on matrix decomposition for simultaneous identification of bi-directional paths in closed-loop systems. IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems, Norway, June 2016: 1115–1120.
5. Benben Jiang, Fan Yang, Dexian Huang, Wei Wang. Extended-AUDI method for simultaneous determination of causality and models from process data. American Control Conference, Washington, USA, June 2013: 2491–2496.
6. Benben Jiang, Fan Yang, Yongheng Jiang, Dexian Huang. An extended AUDI algorithm for simultaneous identification of forward and feedback paths in closed-loop systems. IFAC Symposium on Advanced Control of Chemical Processes, Singapore, July 2012:396–401.
(*通讯作者)
人才培养
课题组长期招收计算能源智能、可信机器学习及应用等方向博士后，详见 http://www.benbenjiang.net
