Heng Qin

Assistant Professor at Chinese Academy of Sciences

England, United Kingdom
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Summary

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Senior
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Top School
Heng Qin is an Assistant Professor and researcher with 10 years’ experience bridging nanotechnology, plasmonics, and blockchain-informed financial systems. With a PhD from Imperial College London and prior lab roles in thin films and condensed matter physics, he combines deep experimental physics expertise with applied R&D in smart trading systems powered by deep reinforcement learning. His industry experience as an investment manager at a Singapore blockchain fund gives him practical insight into crypto ecosystems and product-market dynamics. Based in England, he translates rigorous academic methods into deployable AI-driven trading strategies, a blend that is uncommon among academics in photonics.
code10 years of coding experience
job2 years of employment as a software developer
bookMaster of Physics, Condensed Matter and Materials Physics, Master of Physics, Condensed Matter and Materials Physics at The University of Manchester
bookDoctor of Philosophy (PhD), Nanotechnology, Doctor of Philosophy (PhD), Nanotechnology at Imperial College London
bookBachelor of Science - BS, Condensed Matter and Materials Physics, Bachelor of Science - BS, Condensed Matter and Materials Physics at China University of Petroleum 中国石油大学(华东)
languagesEnglish, Chinese
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Github Skills (33)

seq9
bioinformatics9
modification9
rna-seq9
genomics8
transcriptomics8
sequencing8
file-format8
genome-assembly7
libtorch6
rscript6
machine-learning6
python6
seurat6
deep-learning5

Programming languages (6)

C++RCHTMLPerlPython

Github contributions (5)

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weir12/DENA

Aug 2021 - Oct 2022

Deep learning model used to detect RNA m6a with read level based on the Nanopore direct RNA data.
Contributions:33 commits, 3 PRs, 29 pushes in 1 year 1 month
deep-learningnanopore
q1134269149/DENA

Aug 2021 - Dec 2021

Deep learning model used to detect RNA m6a with read level based on the Nanopore direct RNA data.
Contributions:6 PRs, 18 pushes, 3 branches in 3 months
deep-learning
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