Shunsuke Kitada

Shunsuke Kitada

Research Scientist working on Vision & Language with Deep Learning

Research Scientist @Computer Vision Lab, LINE Corp.

23th Floor Yotsuya Tower, 1-6-1 Yotsuya, Shinjuku-ku, Tokyo 160-0004, Japan

Project Researcher @Hosei University

S603, 3-7-2, Kajino-cho, Koganei-shi, Tokyo 184-8584, Japan

Intelligent information processing laboratory


Shunsuke Kitada (北田 俊輔 in Japanese) is a Research Scientist at Computer Vision Lab (CVL), LINE Corp. His research interest is now on computational advertising with a focus on automatic geneartion/evaluation and assistive technology for multi-modal ad creatives.

Previously, he got his Ph.D. in 2023 at major in applied informatics, graduate school of science and engineering, Hosei University under the supervision of Prof. Hitoshi Iyatomi. His project was to improve prediction performance and model interpretability through attention mechanisms from basic and applied research perspectives.

His personality traits can be summarized as follows:

  • ❤️ Love research and development. He is enjoying research life, and he is currently focusing on three research topics simultaneously, e.g., natural language processing, medical image-based computer vision, computational advertising.
  • 📝 Every day read and implement the SoTA models. He has released many re-implementations of models using mainly Chainer and PyTorch. Therefore, based on state-of-the-art cases, he can advise on deep learning-based product design.
  • 😄 High technical communicativity. Summarize what he made and what he studied, and spread such information. This shows that he can input and output regularly.

The resume is available in PDF .

  • 🤖 Natural Language Processing
  • 💻 Computer Vision
  • 🏥 Medical Image Processing
  • 📃 Computational Advertising
  • PhD in Engineering, 2023

    Graduate School of Science and Engineering, Hosei University

  • MSc in Engineering, 2020

    Graduate School of Science and Engineering, Hosei University

  • BSc in Engineering, 2018

    Hosei University

Conference Paper (Refereed) 📝

All conference papers »

Domestic Conference / Presentation in Japan 🇯🇵

All domestic conference papers »

Recent & Upcoming Talks 🎙️

All recent and upcoming talks »

Experience 💻

Research Internship
Aug 2022 – Sep 2022 Yotsuya, Tokyo, Japan
  • Supervisor: Mr. Masayoshi Kondo and Dr. Yoshihisa Ijiri
  • Topic: Vision-and-language models for advertising
  • Worked on establishing a new technology for automatic generation of advertising videos using images
Research Internship
Aug 2021 – Sep 2021 Yokosuka-shi, Kanagawa, Japan
  • Supervisor: Dr. Kyosuke Nishida
  • Topic: Vision-and-language document understanding
  • Worked on establishing a new pre-training method for document image understanding.
Collaborative Researcher
Jul 2021 – Dec 2022 Shibuya-ku, Tokyo, Japan
Research Internship
May 2021 – Jun 2021 Shibuya-ku, Tokyo, Japan
  • Supervisor: Yuki Iwazaki, Researcher
    • Topic: Multi-modal advertising (Ad) and landing page (LP) understanding
  • Investigated fundamental research to evaluate and analyze ad creatives and its landing pages, presented at academic symposium in Japan.
Research Fellowship for Young Scientists (DC2)
Apr 2021 – Mar 2023

Working on towards robust and interpretable deep learning models and its evaluation.

  • Research title: KAKENHI-PROJECT-21J14143
    • Development of Perturbation Robust and Interpretable Deep Learning Models and Evaluation of Their Interpretability (ja: 摂動に頑健で解釈可能な深層学習モデルの開発とその解釈性の評価)
  • Document review & interview area
    • Informatics (ja: 情報学)
  • Screening section
    • Human informatics and related fields (ja: 人間情報学およびその関連分野)
  • Subsection
    • Intelligent Informatics and Related Fields (ja: 知能情報学関連)
  • Area of specialization
    • Natural language processing (ja: 自然言語処理)

Application information is based on the page of OIST Groups. Keywords of each field are based on the table.

AI x Ad Consultant
Feb 2021 – Mar 2021 Bunkyo-ku, Tokyo, Japan
  • Surveyed methods for automatically creating/generating ad creatives
    • Developed and evaluated prototypes using the method
    • Integrated the method into the system, including testing and operating
  • Advised on the improvement of advertising creative creation methods
    • Participated in meetings (about 1h) at least once a week
  • Wrote an article about AI x Adtech in Japanese:
Research Assistant
Apr 2020 – Mar 2023 Koganei-shi, Tokyo, Japan
Part-time Job
Jul 2020 – Aug 2020 Shinjuku-ku, Tokyo, Japan
Teaching Assistant
Apr 2018 – Mar 2023 Koganei-shi, Tokyo, Japan
Jun 2019 – Jul 2019 Minato-ku, Tokyo, Japan
  • Worked on quantifying and visualizing doctors' interest from browsing history.
  • Built a system for recommending articles for doctors based on the analysis from scratch
Deep Learning Advisor
Jun 2018 – Aug 2018 Shibuya-ku, Tokyo, Japan
  • Advised how to solve practical problems of existing services that use machine learning algorithms and deep learning models, showing examples of the kind of problems recent deep learning models are capable of solving.
Research Internship
Aug 2018 – Feb 2023 Shibuya-ku, Tokyo, Japan
Mar 2018 – Mar 2018 Minato-ku, Tokyo, Japan
Implemented machine learning models that accurately capture semantic features of a document in a document similarity (Engineer internship 3days / ¥100,000).
Part-time Job
Mar 2017 – Jul 2018 Minato-ku, Tokyo, Japan
  • Worked on improving the user experience and participated in improving the logic of the article distribution.
  • Worked on analyzing log data through visualization of multiple KPIs and contributing to the growth of the service.
Teaching Assistant for Deep Learning Hands-On Training Lab
Jan 2016 – Apr 2021
Part-time Job
Feb 2016 – Jun 2018 Bunkyo-ku, Tokyo, Japan
Worked on dealing with large-scale nonstructural patent information in various forms, e.g., pre-processing, crawling, scraping, and analyzing these data.
Fundamental Information Technology Engineer
Jun 2016 – Present
Fundamental Information Technology Engineer Examination is a yardstick for measuring IT knowledge and skills as a team member by asking a range of questions about algorithm, network, database, information security, practical programming, etc.
Aug 2015 – Aug 2015
Planned and implemented enterprise resource planning (ERP) packages.

Projects 📂


Implementation of LSUV (Layer-sequential unit-variance) in PyTorch.