Guannan He
Assistant Professor
- Tel: 010-82524866
- Office: Room 1004, Wangkezhen Building, 5 Yiheyuan Road, Haidian District, Beijing
- Email: gnhe at pku dot edu dot cn
- Google Scholar
- GitHub
- ORCID
- ResearchGate
Research Area
- Methods: Optimization, Data Science, Control, Policy Analysis
- Applications: Energy and Transportation Systems, Hydrogen, Battery
News
- 2024.06.06: I am honored to receive the 2024 IEEE PES Prize Paper Award, for my paper “Hydrogen Supply Chain Planning with Flexible Transmission and Storage Scheduling”. This award recognizes the most outstanding papers published during the past three years in an IEEE PES Transaction. Two papers are selected each year in average for the award since 1966. Paper Link
- 2024.03.20: Our paper “Realistic fault detection of li-ion battery via dynamical deep learning” has been selected as a featured article in the Nature Communications Editor’s highlights.
- 2024.03.06: I am honored to be recognized by IISE as part of its ESD Outstanding Young Investigator Award in 2024!This award recognizes IISE Energy Systems Division members under the age of 35 for their technical contribution to energy systems.
- 2024.01.04: Our paper “A cross-scale framework for evaluating flexibility values of battery and fuel cell electric vehicles” has been published in Nature Communications.
- 2023.09.23: Our paper “Realistic fault detection of li-ion battery via dynamical deep learning” has been published in Nature Communications.
- 2023.09.04: Our paper “Inherent spatiotemporal uncertainty of renewable power in China” has been published in Nature Communications.
Appointments
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Assistant Professor | Department of Industrial Engineering and Management, College of Engineering, Peking University |
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Postdoctoral Associate | Massachusetts Institute of Technology |
Education
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PhD | Engineering and Public Policy, Carnegie Mellon University |
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Master | Electrical Engineering, Tsinghua University |
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Bachelor of Science | Electrical Engineering, Economics (2nd degree), Tsinghua University |
Teaching
- Undergraduate: Introduction to Machine Learning
- Graduate: Carbon Neutrality and Energy Internet