Yunfan Shao
Postdoctoral Researcher at LEPMI laboratory Grenoble
Grenoble, Auvergne-Rhône-Alpes, France
Summary
🤩
Rockstar🎓
Top SchoolYunfan Shao is a postdoctoral researcher in Grenoble with nine years’ experience advancing polymer electrolytes and electrochemical energy storage, particularly single-ion polymer electrolytes for post-lithium battery systems. With a PhD in Electrochemistry and an MS in Polymer Science, Yunfan combines deep expertise in polymer chemistry, physics, and organic synthesis with hands-on electrochemical device development. He is proficient with a wide range of analytical and separation techniques (NMR, FTIR, XPS, HPLC, GPC, etc.) and brings practical knowledge of nanomaterials, organic electronics, and 3D printing to materials design. Beyond academia, he has contributed backend and data-processing features to an open-source NLP framework on GitHub, reflecting a versatile skill set that bridges experimental science and computational tooling.
9 years of coding experience
Bachelor of Engineering - BE, Polymer materials, 3.3/4, Bachelor of Engineering - BE, Polymer materials, 3.3/4 at Sichuan UniversityDoctor of Philosophy - PhD, Electrochemistry, Doctor of Philosophy - PhD, Electrochemistry at Université Grenoble AlpesMaster of Science - MS, Polymer Science, 3.75/4, Master of Science - MS, Polymer Science, 3.75/4 at The University of Akron
Github Skills (13)
pytorch10
nlp10
python10
data-structure9
datastructures-algorithms9
algorithm9
data-structures9
algorithms9
deep-learning8
deeplearning-ai8
text-processing8
natural-language-processing8
text-classification5
Programming languages (3)
Github contributions (5)
fastnlp/fastNLP
Sep 2018 - Dec 2020
fastNLP: A Modularized and Extensible NLP Framework. Currently still in incubation.
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:1 release, 191 commits, 36 PRs in 2 years 3 months
Contributions summary:Yunfan's contributions centered on developing features for a modularized and extensible NLP framework. Their work included adding core functionalities related to data processing, specifically supporting the creation of various `Field` types and the implementation of a `Batch` class. The user demonstrated experience in constructing data loading and processing pipelines.
nlpbertword-embeddingsdeep-learningtext-processing
choosewhatulike/fastNLP
Mar 2018 - Oct 2019
FastNLP
Contributions:47 pushes, 21 branches in 1 year 7 months
nlpnatural-language-processing
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