Person

Jie Chen

This person is no longer with EPFL

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Related publications (12)

Please note that this is not a complete list of this person’s publications. It includes only semantically relevant works. For a full list, please refer to Infoscience.

Ion-Dipole Interaction Enabling Highly Efficient CsPbI3 Perovskite Indoor Photovoltaics

Michael Graetzel, Shaik Mohammed Zakeeruddin, Di Zhang, Meng Li, Xiaoyan Wang, Haizhou Lu, Xingyu Gao, Ziyun Wang, Jie Chen

Metal halide perovskites are ideal candidates for indoor photovoltaics (IPVs) because of their easy-to-adjust bandgaps, which can be designed to cover the spectrum of any artificial light source. However, the serious non-radiative carrier recombination und ...

Wiley-V C H Verlag Gmbh2023

Search for relativistic fractionally charged particles in space

Songmei Wu, Johan Juul Chang, Zhirui Xu, Jennifer Maria Frieden, Siyuan Wang, Junqiu Liu, Jian Wang, Yi Zhang, Yong Yang, Hao Liu, Ming Gao, Cheng Zhao, Xuan Li, Arshia Ruina, Chiara Perrina, Andrii Kotenko, Wenyu Zhang, Xiao Wang, Yi Wang, Wei Li, Jie Chen, Shuai Liu, Yao Zhang

More than a century after the performance of the oil drop experiment, the possible existence of fractionally charged particles (FCPs) still remains unsettled. The search for FCPs is crucial for some extensions of the Standard Model in particle physics. Mos ...

American Physical Society2022

Convex Combination of Diffusion Strategies Over Networks

Ali H. Sayed, Jie Chen

Combining diffusion strategies with complementary properties enables enhanced performance when they can be run simultaneously. In this article, we first propose two schemes for the convex combination of two diffusion strategies, namely, the power-normalize ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

People doing similar research (32)

Roberto Castello is a senior scientist and group leader at the EPFL Laboratory of Solar Energy and Building Physics. Physicist by training, he has extensive experience in collecting, classifying and interpreting large datasets using advanced data mining techniques and statistical methods. He received his MSc (2007) in Particle Physics and PhD (2010) in Physics and Astrophysics from the University of Torino. He worked as a postdoctoral researcher at the Belgian National Research Fund (2011-2014) and at the CERN Experimental Physics Department (2015-2017) as a research fellow and data scientist. He is primary author of more than 20 peer-reviewed publications and he presented at major international conferences in the high energy physics domain. In 2018 he joined the Solar Energy and Building Physics Laboratory (LESO-PB) to work on data mining and Machine Learning techniques for the built environment and renewable energy. His main research interests are: spatio-temporal modeling of renewable energy potential, energy consumption forecasting techniques, anomaly detection, and computer vision techniques for automated classification in the built environment. He leads the group of Urban Data Mining, Intelligence and Simulation at LESO-PB and he is a member of the NRP75 Big Data project (HyEnergy) of the Swiss National Science Foundation. He is a member of the Swiss Competence Centre for Energy Research (SCCER) and deputy leader of the working group on Leveraging Ubiquitous Energy Data. He has served as a scientific committee member, workshop organizer and speaker at international conferences (ICAE 2020, Applied Machine Learning Days 2019 and 2020, CISBAT 2019 and 2021 and SDS2020). Since 2017 he is member of the Geneva 2030 Ecosystem network, promoting the United Nations agenda towards the realization of the Sustainable Development Goals (SDGs).

Alessandro Degano

Xin Chen

Yiming Li