Yitong Chen is a tenure-track assistant professor in the Department of Electronic Engineering at Shanghai Jiao Tong University. She earned her bachelor’s degree from the Qian Xuesen Class at Tsinghua University in 2019 and her Ph.D. from the Department of Automation at Tsinghua University in 2024. From 2018 to 2019, she was an academic visitor at Harvard Medical School, USA.

Research Interests: High-speed, low-power optoelectronic intelligent computing chips and their applications. She has published multiple papers in top international journals and conferences such as Nature, Science, Nature Photonics, and Science Advances. She was the first internationally to demonstrate an end-to-end performance that is 3,600 times faster and a million times more energy-efficient than leading chips on complex visual intelligence tasks. These results were reported by Xinhua News Agency, People’s Daily, Guangming Daily, Science and Technology Daily, and others. Her work was also selected as an Outstanding Funded Achievement by the National Natural Science Foundation of China.

She leads projects funded by the Ministry of Science and Technology of China and Huawei exploreX, and serves regularly as a reviewer for Nature journals, IEEE Transactions series, APL Photonics, IROS, and other top international venues.

High-speed, low-power optoelectronic intelligent computing chips and their applications

[1] Outstanding Doctoral Dissertation Award by the Chinese Institute of Electronics, 2025

[2] Support Program for Young Talents by the Chinese Institute of Electronics, 2025

[3] Top 10 Scientific Advancements in Chinese Chip Science by Chip magazine, 2024 (First Author)

[4] Top 10 Progresses in Chinese Optics (Applied Research Category), 2024 (First Author)

[5] Top 10 Progresses in Chinese Semiconductors, 2024 (First Author)

[6] Outstanding Doctoral Thesis at Tsinghua University, 2024

[7] Excellent Doctoral Graduate in Beijing, 2024

[8] Top 10 Social Impact Events in Chinese Optics (Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Chinese Academy of Sciences Press, Light: Science & Applications, and ScienceNet), 2023 (First Author)

[9] Special Graduate Scholarship at Tsinghua University, 2023

[1] Y. Chen*, M. Nazhamaiti*, H. Xu*, Y. Meng, T. Zhou, G. Li, J. Fan, Q. Wei, J. Wu, F. Qiao, L. Fang, Q. Dai, All-analog photoelectronic chip for high-speed vision tasks. Nature, 623, 48–57, 2023.

[2] Y. Chen*, T. Zhou*, J. Wu, H. Qiao, X. Lin, L. Fang, Q. Dai, Photonic unsupervised learning variational autoencoder for high-throughput and low-latency image transmission. Science Advances, 2023, 9(7): eadf8437.

[3] Y. Chen, T. Zhou, Y. Guo, J. Wu, L. Fang, Q. Dai, Hardware-implemented Photonic Neural Network for High-throughput and Low-latency Image Transmission, Optica Imaging Congress 2023 (PDP paper, oral).

[4] X. Cheng，Z. Fan，S. Yao，T.Jin，Z. Lv，Y. Lan，R. Bo，Y. Chen，F. Zhang， Z. Shen，H. Wan，Y. Huang，Y. Zhang，Programming 3D curved mesosurfaces using microlattice designs，Science, 379.6638:1225-1232, 2023.

[5] Q. Yang，T. Liu，Y. Xue，H. Wang，Y. Xu，B. Emon，M. Wu，C. Rountree， T. Wei，I. Kandela，C. Haney，A. Brikha，I. Stepien，J. Hornick，R. Sponenburg， C. Cheng，L. Ladehoff，Y. Chen，Z. Hu，C. Wu，M. Han，J. Torkelson，Y. Kozorovitskiy，M. Saif，Y. Huang，J. Chang，J. Rogers，Ecoresorbable and bioresorbable microelectromechanical systems. Nature Electronics, 5, 526–538, 2022.

[6] T. Zhou，X. Lin，J. Wu，Y. Chen，H. Xie，Y. Li，J. Fan，H. Wu，L. Fang，Q. Dai，Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nature Photonics,15, 367–373, 2021.