PeopleofSuperintelligence

Researcher

Benben Jiang

Also known as 江奔奔

Benben Jiang is an associate professor at Tsinghua University whose research applies AI and machine learning to battery systems and new-energy computing. He earned engineering degrees at Zhejiang University and Tsinghua, completed a joint-training program at MIT, and later worked there as a postdoctoral researcher. His work spans data-driven battery-life prediction, Bayesian charging-protocol design, deep-learning fault detection, fair classification, and active-learning methods for electrolyte discovery. He received Tsinghua's Outstanding Doctoral Dissertation award in 2015 and the Dimitris Chorafas Foundation Prize in Informatics/Computer Science in 2016. As of October 2026, he is deputy director of Tsinghua's Institute of Systems Engineering.

Works
74
Citations
5,079
h-index
20

Publication numbers from OpenAlex, as of 27 October 2026.

Timeline

  1. 2006–2020

    From Zhejiang to MIT

    He earns a B.Eng. in automation from Zhejiang University in 2010 and a Ph.D. in control science and engineering from Tsinghua in 2015. In 2013–2014, he takes part in a China Scholarship Council joint-training program at MIT. He works there as a postdoctoral researcher from 2016 to 2020.

  2. 2015–2016

    Doctoral and Chorafas honors

    Tsinghua recognizes his doctoral dissertation as outstanding in 2015. In 2016, he receives the Dimitris Chorafas Foundation Prize in Informatics/Computer Science.

  3. 2019

    Predicting battery life

    In 2019, he coauthors a Nature Energy paper on data-driven prediction of battery cycle life before capacity degradation.

  4. 2021

    Bayesian battery prediction

    In 2021, he coauthors a Joule paper on Bayesian learning for rapid prediction of lithium-ion battery cycling protocols.

  5. 2023

    Dynamical battery fault detection

    In 2023, he coauthors a Nature Communications paper that develops a dynamical deep-learning framework for electric-vehicle battery anomaly detection.

  6. 2024

    Fair classification with AdaBoost

    In a 2024 arXiv preprint, he coauthors a fair AdaBoost method that uses fairness-aware reweighting for binary classification.

  7. 2025

    BatteryBERT fault detection

    In a 2025 arXiv preprint, he coauthors BatteryBERT, which adapts BERT-style pretraining and masked signal modeling to lithium-ion battery fault detection.

  8. 2026

    Active learning for electrolytes

    In 2026, he coauthors a Nature Communications study that combines deep active learning with knowledge transfer to speed discovery of lithium-metal battery electrolytes.

  9. As of October 2026

    Associate professor and deputy director

    He becomes an associate professor in Tsinghua's Department of Automation in 2023. He also serves as a doctoral supervisor there during 2020–2023 and as of October 2026. As of October 2026, he serves as deputy director of Tsinghua's Institute of Systems Engineering; his research includes AI-driven new-energy computing and large-model theory.

Career

  1. Since 2023Tsinghua UniversityAssociate Professor
  2. Since 2020Tsinghua UniversityDoctoral Supervisor
  3. NowTsinghua UniversityDeputy Director, Institute of Systems Engineering
  4. 2021–2026Beijing National Research Center for Information Science and Technology
  5. 2020–2023Tsinghua UniversityAssistant Professor
  6. 2016–2020Massachusetts Institute of TechnologyPostdoctoral Researcher
  7. 2016–2018Beijing University of Chemical Technology
  8. 2013–2014Massachusetts Institute of TechnologyJoint-training participant

Education

  1. 2015Tsinghua UniversityPh.D. in control science and engineering
  2. 2010Zhejiang UniversityB.Eng. in automation

From the biography and OpenAlex affiliations (the years of papers that list the organization), so some years are approximate.

Checked career sources: au.tsinghua.edu.cn.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

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  1. Machine Learning Accelerates the Prediction and Optimization of Battery Management coe.pku.edu.cn. Checked 27 October 2026.
  2. 江奔奔-清华大学自动化系 au.tsinghua.edu.cn. Checked 27 October 2026.
  3. 自动化系教师江奔奔发文通过机器学习快速预测新能源电池快充性能，实现电池实验成本缩减两个数量级 au.tsinghua.edu.cn. Checked 27 October 2026.
  4. Realistic fault detection of li-ion battery via dynamical deep learning nature.com. Checked 27 October 2026.
  5. Adaptive Boosting with Fairness-aware Reweighting Technique for Fair Classification arxiv.org. Checked 27 October 2026.
  6. BatteryBERT for Realistic Battery Fault Detection Using Point-Masked Signal Modeling arxiv.org. Checked 27 October 2026.
  7. Deep active learning and knowledge transfer for rapid discovery of lithium metal battery electrolytes nature.com. Checked 27 October 2026.
  8. The 4th International conference on New Materials for Environment and Energy (NMEE 2026) icnmee.com. Checked 27 October 2026.