Shunsuke Kitada (北田 俊輔 in Japanese) is a Ph.D student at Major in applied informatics, graduate school of science and engineering, Hosei University under the supervision of Prof. Hitoshi Iyatomi. His research interests include deep learning-based natural language processing, computer vision, medical image processing, and computational advertising.

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, Current

    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

Recent News 😀

All news »

  • [2020.12] Hosei University Research Grant for Doctoral Course Adopters in 2020 (480,000 JPY) (detail).

  • [2020.10] Accepted our paper to AACL-IJCNLP2020 Student Research Workshop.

  • [2020.07] Got JASSO Scholarship for Top 10% Excellent Master Students from Japan Student Services Organization.

  • [2020.06] Invited talk on Organized Session in The 34th Annual Conference of the Japanese Society for Artificial Intelligence.

  • [2020.04] Accepted our paper to ACL2020 Student Research Workshop.

Experience 💻

AI x Ad Consultant
Feb 2021 – Present
  • Surveying methods for automatically creating/generating ad creatives
    • Developing and evaluating prototypes using the method
    • Integrating the method into the system, including testing and operating
  • Advising on the improvement of advertising creative creation methods
    • Participate in meetings (about 1h) at least once a week
Research Assistant
Apr 2020 – Present
Jun 2019 – Jul 2019
Quantified and visualized doctors' interest from browsing history. Based on these analyses, I built a system for recommending articles for doctors from scratch.
Research Internship
Aug 2018 – Present
Conducted fundamental research to generate advertisement automatically. I have written a paper and prepared for a presentation for an international conference based on the results during my internship.
Deep Learning Advisor
Jun 2018 – Aug 2018
I have advised how to solve practical problems of existing services that use machine learning algorithms and deep learning models. Additionally I have shown examples of the kind of problems recent deep learning models are capable of solving.
Teaching Assistant
Apr 2018 – Present

Assisted in several classes at Hosei University:

  • Image Processing
  • Information Theory
  • Operating System
  • Programming Language C/C++
  • Programming Language JAVA
Mar 2018 – Mar 2018
Engineer internship 3days / ¥100,000. I implemented machine learning models that accurately capture semantic features of a document in a document similarity.
Part-time job
Mar 2017 – Jul 2018
I have worked on improving the user experience and participated in improving the logic of the article distribution. Also visualized multiple KPIs and contributed to service growth through data analysis.
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.
Part-time job
Feb 2016 – Jun 2018
Dealt with large-scale nonstructural patent information in various forms, e.g., pre-processing, crawling, scraping, and analyzing these data.
Teaching Assistant for Deep Learning Hands-On Training Lab
Jan 2016 – Jan 2017
Aug 2015 – Aug 2015
Planned and implemented enterprise resource planning (ERP) packages.

Awards & Grants 🏆

All awards and grants »

  • [2020.12] Hosei University Research Grant for Doctoral Course Adopters in 2020 (480,000 JPY) (detail).

  • [2020.07] JASSO Scholarship for Top 10% Excellent Master Students (2,112,000 JPY) from Japan Student Services Organization.

  • [2019.08] Honorable Mention in Young Researcher Association for NLP Studies (YANS) 2019.

  • [2019.07] CEO Special Award in the second half of FY2019 at Gunosy Inc. (detail).

  • [2019.06] Hosei University 100th year anniversary scholarship for master students (PDF).



AllenNLP Eraser

Collection of AllenNLP DatasetReaders for ERASER

Paper Survey

Paper Survey

📚Survey of previous research and related works on machine learning (especially Deep Learning) in Japanese.

Hosei University Soft Tennis Club Hp

Hosei University Soft Tennis Club Hp

🎾 Hosei University Soft Tennis Club’s home page repository.


Implementation of Focal loss in Chainer.


Implementation of RICAP in Chainer.


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


High-speed Deep learning API Server with Libtorch (C++) and Gin (Golang)


A tool for enriching the output of nvidia-smi forked from peci1/nvidia-htop.


Implementation of PyramidNet in Chainer.


Implementation of InceptionResNetV2 in Chainer.


Implementation of IMSAT in Chainer.


Implementation of Center Loss in Chainer.