Peinuan Qin

Peinuan Qin is currently a third-year Master's student in Software Engineering at the University of Melbourne. In 2020, he conducted research on knowledge distillation with Professor Rong Pan from Sun Yat-sen University. From 2021 to 2022, he participated in the "ReadyGo" project, a collaboration between the Chinese Academy of Sciences and CAS-Ruiyi, under the supervision of Researcher Xiangmin Fan. From 2022 to 2023, he have been working on Natural Language Processing (NLP) research with Professor Haitao Zheng at Tsinghua University. In 2023, he joined Professor Yi-chieh Lee's lab: AI4SG at NUS for HCI-related research.

His research interest include human-computer interaction (HCI), Natural Language Processing (NLP), and Human-Centered AI.


2021-2023 University of Melbourne
Master Degree of Software Engineering
2016-2020 China University of Geosciences (Wuhan)
Bachelor Degree of Electronic Information Engineering


Research Experiences

March 2023-present National University of Singapore
Position: Research Assistant
Theme: Human-Computer Interaction.
  • Updating ...
Nov 2022-present Tsinghua University
Position: Research Assistant
Theme: Generation of movie commentary & Punctuation addition task for complex texts.

Tech stack: Python / Pytorch

  • Collected 359405 commentaries and processed to build a 173M dataset. Took GPT-2 as backbone and combined title, summary of movies for movie commentary generation.
  • Proposed a comprehensive end-to-end trained Chinese corpus punctuation addition model. Collected and processed 2G+ Chinese texts in over 15 major categories and trained the model.
  • The generated narration fits the main content of the film and flows logically. Built a preliminary punctuation addition model for multi-category texts (still updating). Thesis is in preparation.
Nov 2021-Jun 2022 Chinese Academy of Sciences
Position: Research Assistant
Deep learning based motion assessment system development (ReadyGo).

Tech stack: Python / Pytorch

  • Collected patients’ motion data with Kinect, followed by data smooth and filtering.
  • Proposed a gait annotation method. Led the annotation, data cleaning & visualization tools development.
  • Proposed a sequential-based gait quantitive assessment algorithm with rich spatial-temporal features and gait semantic segmentation.
  • Proposed Key Semantic Positioning Accuracy to evaluate model ability more comprehensively instead of relying only on precision, recall, and f1-score.
  • Attained precise gait parameters and decreased the error rate of parameter calculation to 3.17%. Produced Deep Learning Assisted Gait Parameter Assessment of Neurodegenerative Diseases: Model Development and Validation and submitted to JMIR. ReadyGo have received Chinese national medical device registration and is used for commercial purposes.
Nov 2021-Jun 2022 Sun Yat Sen University
Position: Research Assistant
Knowledge distillation method engineering.

Tech stack: Python / Pytorch / Keras

  • Investigated the influencing factors of knowledge distillation. Conducted many experiments on distillation structure and mode. Proposed to model and fuse features from different levels and distilled the aggregation information and generated a research report MFFD: Multi-scale feature fusion distillation.
  • Proposed a lightweight shared-mask attention mechanism and a multi-level attention structure and produced a journal paper MLAN: Multi-Level Attention Network.
Winter 2018 UC Berkeley
Position: Research Assistant
The application of traditional machine learning in brainwave classification & network security.

Tech stack: Python

  • Studied and practiced traditional machine learning algorithms and evaluation methods.
  • Peformed domain analysis (with FFT) and denoising for brainwave signals.
  • Extracted brainwave features and reduced features by PCA.
  • Visualized signal and features and analysed confusion matrix to measure the classification model effect.
  • Improved the classification result by 5% on given dataset compared to the baseline.

Project Experience


Tech stack: Python / Pytorch

    Built a motion assessment platform based on deep learning models and the patient’s 3D motion data collected by Kinect. The device is already in use in major hospitals in China, helping middle-aged and elderly people with neurodegenerative diseases with contactless early diagnosis and rehabilitation.

Agile Development

Tech stack: HTML5 / CSS / JavaScript / MongoDB / Python / Vue / Django / Nodejs

    Built and deployed a disease data website as an agile team scrum master. In this program, a Nodejs server is taken for normal requests and a Django server for chart generation. The front end was based on element-UI & Vue. Agile concept is used to drive projects forward, manage risk and build usable products iteratively.

Engineering Skills

  • Python
  • Java
  • C
  • HTML5, CSS, JavaScript
  • SQL, MongoDB
  • Vue, ElementUI
  • Nodejs, Django
  • Data Crawling, Processing, Analysis
  • Feature Engineering, Visualization
  • Pytorch, Keras