Yueran PAN
AI Product Manager for Yuanbao at Tencent AI Researcher in Evaluation & Test Engineering at Midea Research Assistant in Computer Science at Duke Kunshan University & Wuhan University MSc in Data Science, London School of Economics and Political Science BSc in Statistics, Wuhan University Research Project with Microsoft Research Cambridge, UK Data Analysis Intern at Tencent
Welcome to Yueran’s Pages
Hello, My name is Yueran Pan. In Chinese pinyin order, my names’ capital letters are PY and R, which means Python and R, so I am fated to love data science.
Currently, I am a Ph.D. candidate in Computer Science supervised by Ming Li, jointly trained by Wuhan University and Duke Kunshan University. I achieved MSc Data Science with distinction at the London School of Economics and Political Science and BSc Statistics at Wuhan University. I have two-year research experience in SMIIP Lab at Duke Kunshan University. Most of my studies are about applying data analysis methods to help children with autism. I believe that AI would share my love.
Publications and Papers
Responsive Social Smile: A Machine Learning based Multimodal Behavior Assessment Framework towards Early Stage Autism Screening
International Conference on Pattern Recognition (ICPR) 2020
To standardize the process of ASD screening, we design a ”Responsive Social Smile” protocol and the associated experimental setup. Moreover, we propose a machine learning based assessment framework for early ASD screening. By integrating speech recognition and computer vision technologies, the proposed framework can quantitatively analyze children’s behaviors under well-designed protocols.
Repository
https://github.com/sparklingyueran/Responsive-Social-Smile-Facial-Expression-Recognition
A Multimodal Framework for Automated Teaching Quality Assessment of One-to-many Online Instruction Videos
International Conference on Pattern Recognition (ICPR) 2022
Abstract
We design an evaluation protocol and propose a multimodal machine learning framework for automated teaching quality assessment of one-to-many online instruction videos. Our framework evaluates online teaching quality from five aspects, namely Clarity, Classroom interaction, Technical management of online teaching, Empathy, and Time management. Our method includes mid-level behavior feature extraction, high-level interpretable feature extraction, and supervised learning prediction.
Repository
https://github.com/sparklingyueran/Online-Course-Assessment-Framework