Biography

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 .

Interests
  • 🤖 Natural Language Processing
  • 💻 Computer Vision
  • 🏥 Medical Image Processing
  • 📃 Computational Advertising
Education
  • 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 »

International Journal and Conference 📝

All journal and conference papers ».

Domestic Conference in Japanese 📝

All domestic conference papers »

Recent & Upcoming Talks 🎙️

All recent and upcoming talks »

CCSE 2019
On Predicting Ad Creative Evaluation using Deep Learning
ICML/KDD 2019 Pre-conference session

Awards & Grants 🏆

All awards and grants »

Experience 💻

 
 
 
 
 
Research Internship
Aug 2021 – Sep 2021
  • Supervisor: Dr. Kyosuke Nishida
  • Topic: Vision-and-langage document understanding
 
 
 
 
 
Collaborative Researcher
Jul 2021 – Present
This is a special research position for doctoral students adopted as JSPS Research fellow (DC1, DC2, PD).
 
 
 
 
 
Research Internship
May 2021 – Jun 2021
  • Supervisor: Yuki Iwazaki, Researcher
  • Topic: Multi-modal advertising (Ad) and landing page (LP) understanding
  • Conducting fundamental research to evaluate and analyze ad creatives and landing pages. I have written a paper and prepared for a presentation for an international conference based on the results during my internship.
 
 
 
 
 
Research Fellowship for Young Scientists (DC2)
Apr 2021 – Present
  • 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 is based on the table.

 
 
 
 
 
AI x Ad Consultant
Feb 2021 – Mar 2021
  • Surveyed methods for automatically creating/generating ad creatives
    • Developing and evaluating prototypes using the method
    • Integrating the method into the system, including testing and operating
  • Advised on the improvement of advertising creative creation methods
    • Participate in meetings (about 1h) at least once a week
  • Wrote a articles about AI x Adtech in Japanese:
 
 
 
 
 
Research Assistant
Apr 2020 – Present
 
 
 
 
 
Internship
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
 
 
 
 
 
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
 
 
 
 
 
Internship
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 – Apr 2021
 
 
 
 
 
Internship
Aug 2015 – Aug 2015
Planned and implemented enterprise resource planning (ERP) packages.

Projects

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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.

chainer-FocalLoss

Implementation of Focal loss in Chainer.

chainer-RICAP

Implementation of RICAP in Chainer.

LSUV.pytorch

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

libtorch-gin-api-server

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

nvhtop

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

chainer-PyramidNet

Implementation of PyramidNet in Chainer.

chainer-InceptionResNetV2

Implementation of InceptionResNetV2 in Chainer.

chainer-IMSAT

Implementation of IMSAT in Chainer.

chainer-center-loss

Implementation of Center Loss in Chainer.