Chen Yow Ru

Chen Yow Ru

Master of Engineering Student at The University of Tokyo, research in Natural Language Processing and Deep Learning

Matsuo Lab, The University of Tokyo

Biography

Graduated with a Bachelor of Science degree from Northwestern University, Chen Yow Ru is now devoted to AI research at Matsuo Lab advised by Professor Yutaka Matsuo. His research interests include natural language processing, computer vision, web mining, and deep learning. He speaks fluent English, Japanese, Taiwanese, Chinese, and conversational Arabic. He has been awarded Technical Recognition at Taiwan OpenStack Application Hackathon in 2016 and has been an AI engineer for Matsuo Institute since 2021.

Download my resumé.

Interests
  • Artificial Intelligence
  • Natural Language Processing
  • Journalism
Education
  • MEng in Technology Management for Innovation, 2023

    The University of Tokyo

  • BS in Journalism, 2019

    Northwestern University

Skills

Python

95%

Deep Learning

95%

Photography

85%

Experience

 
 
 
 
 
SenseTime(株式会社センスタイムジャパン)
AI Researcher
Aug 2022 – Present Tokyo, Japan
  • Researched autonomous driving techniques that apply both Transformer and Temporal structures
  • Implemented ideas from top research conferences and fine-tuned models to get higher accuracy
 
 
 
 
 
Matsuo Institute (株式会社松尾研究所)
AI Engineer
Sep 2021 – Jul 2022 Tokyo, Japan

Responsibilities include:

  • Led a team of 4 to successfully renew an over $1 million dollar business contract with clients
  • Analyzed clients' yearly revenue data and recommended ways to improve sales by 78%
  • Proposed, designed, and built Machine Learning and NLP models from scratch
 
 
 
 
 
IZA (株式会社IZA)
Data Scientist
May 2022 – Jun 2022 Tokyo, Japan
  • Developed MongoDB database to integrate with data visualizing platforms
  • Extracted keyphrases from error messages using large language models
 
 
 
 
 
Ghelia Inc. (ギリア株式会社)
Machine Learning Intern
Aug 2021 – Aug 2021 Tokyo, Japan
  • Researched and implemented multiple NLP models such as attention and transformer.

Accomplish­ments

Formulated informed blockchain models, hypotheses, and use cases.
See certificate

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