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. I am enjoying research life, and I am currently conducting three research topics simultaneously, e.g., natural language processing, medical image based computer vision, computational advertising.
  • 📝 Every day read and implement the cutting-edge deep learning models from the research paper. I have released many re-implementations of models using mainly Chainer and PyTorch. Therefore, based on state-of-the-art cases, I can advise on deep learning base product design.
  • 😄 High technical communicativity. Summarize what I made and what I studied, and spread such information. This shows that I can input and output regularly.


  • 🤖 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 »

  • [2020.07] 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.

  • [2020.01] Interviewed by Nikkan Kogyo Shimbun, Ltd (the article).

  • [2019.08] Got Honorable Mention in YANS 2019.

International Conference

All international conference papers ».

AraDIC: Arabic Document Classification using Image-Based Character Embeddings and Class-Balanced Loss

Proc. of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creative

Proc. of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019. (Acceptance Rate = 20%)

End-to-End Text Classification via Image-based Embedding using Character-level Networks

Proc. of IEEE Applied Imagery Pattern Recognition (AIPR) 2018 Workshop

Skin lesion classification with ensemble of squeeze-and-excitation networks and semi-supervised learning

International Skin Imaging Collaboration 2018.

Domestic Conference

All domestic conference papers ».

(2020). 生存時間分析を用いた広告クリエイティブの停止予測. JSAI 2020.


(2020). 文字単位の解釈可能な潜在表現のdata augmentation. NLP 2020.


(2020). Image-based Character Embedding for Arabic Document Classification. NLP 2020.


(2020). Script-aware embedding を用いた文字表現の獲得. NLP 2020.


(2019). 日本語の文字体系を考慮した文書分類モデルの提案. YANS 2019.

Poster Slides

(2019). Image Based Character Embeddings for Arabic Document Classification. YANS 2019.


(2019). 頑健な皮膚腫瘍診断支援のための body hair augmentation. IPSJ 2019.

Experience 💻


Research Assistant

Hosei University

Apr 2020 – Present


M3, Inc.

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

Gunosy Inc.

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

Piascore, Inc.

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

Hosei University

Apr 2018 – Present

Assisted in several classes at Hosei University:

  • Image Processing
  • Information Theory
  • Operating System
  • Programming Language C/C++
  • Programming Language JAVA


Faber Company Inc.

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

Gunosy Inc.

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.

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

Assisted in several workshops in Tokyo relating to deep learning and CUDA:

  • GTC Japan 2016 DLI
  • NVIDIA Deep Learning Institute 2017 in Takada-no-baba
  • NVIDIA Deep Learning Institute 2017 in Tokyo Midtown
  • GTC Japan 2017 DLI


Works Applications Co., Ltd.

Aug 2015 – Aug 2015
Planned and implemented enterprise resource planning (ERP) packages.

Recent & Upcoming Talks

The Present and Future of Machine Learning for Ad Creatives

In this talk, I will introduce my research topic: the framework of ad creative evaluation based on CVR prediction to support the creation of effective ad creative. Also, I will introduce several research topics related to the latest ad technology and discuss the current status and prospects achieved by the research on ad creative and machine learning techniques.

Text Analytics Symposium 2019

This talk introduces a case study of research and development internship at Gunosy Inc.

CCSE 2019

On Predicting Ad Creative Evaluation using Deep Learning

ICML/KDD 2019 Pre-conference session

Awards & Grants 🏆

All awards and grants »



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’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.