Haotian Song (宋昊天)
ORCID | Google Scholar
Education experience
- 2021/01 - 2021/07 College of Physics & Astronomy, University of Manchester, UK
- 2020/08 - 2020/12 Department of Physics, Texas A&M University, USA
- 2017/09 - 2020/06 School of Physics Xi'an Jiao University, China
- Tsien Hsue-shen Talented Program (top 10%), Bachelor of Science in Physics (Honors)
- 2014/05 - 2017/06 Anhui LiXin 1st High School, China
- 2019/07 - 2019/08 Summer School at School of Physics Peking University
Awards
- 06/2021 Outstanding Graduate Thesis Award (Top 1%)
- 07/2021 Everest Scholarship, Xi’an Jiaotong University
- 05/2020 Tsien Hsue-shen Academic Research Award
- 10/2020 Academic Research Award, Xi’an Jiaotong University
- --/2020 Provincial Award for China Undergraduates Innovation and Entrepreneurship Competition
- 07/2019 First Prize of the 5th Chinese Undergraduate Physics Experiment Competition
- 11/2019 Outstanding Student Cadre, Xi'an Jiaotong University
- 11/2019 Third-Class Scholarship, Xi’an Jiaotong University
- 12/2018 First Prize of the Contemporary Undergraduate Mathematical Contest in Modeling in ShaanXi province
- 11/2018 Outstanding Student, Xi'an Jiaotong University
- 11/2018 Second-Class Scholarship, Xi’an Jiaotong University
- 10/2018 Everest Scholarship in 2018, Xi'an Jiaotong University
Research Areas
Publications
- Z. Zuo†, H. Song, H. Xue, “Population synthesis on ultra-luminous X-ray sources with an accreting neutron star: Wind Roche-lobe overflow cases”. A&A 649, L2 (2021)
- H. Song, X. Nie, H. Su, H. Chen, Y. Zhou, X. Zhao, T. Peng†, M. O. Scully, “0.8% Nyquist noise-free computational ghost imaging via non-experimental deep learning”, submitted to Scientific Report, arXiv:2108.07673
- T. Peng†, H. Song, Z. Zhang†, and M. O. Scully, “Deep-learned speckle patterns and its application to ghost imaging”
- H. Song, Z. Zuo†, “Ultra-luminous X-Ray sources with wind Roche lobe overflow in Ring galaxies”, Graduate thesis, Xi'an Jiaotong University
Contact me
Github: wonderingmark123
E-mail: haotian.song@student.manchester.ac.uk
QQ: 490785554
Telephone:(+86)13720537361