Seiya Tokui is a researcher at Preferred Networks, Inc., Japan, and also a Ph.D student at the University of Tokyo. He received the master’s degree in mathematical informatics at the University of Tokyo in 2012. He is the lead developer of the deep learning framework, Chainer. His current research interests include deep learning, its software design, computer vision, and natural language processing.


Computer Science, the University of Tokyo (Apr. 2016 – current)

  • Superviser: Issei Sato

Mathematical Informatics, the University of Tokyo (Apr. 2010 – Mar. 2012)

  • Research theme: machine learning, nearest neighbor search, natural language processing
  • Master’s thesis: Learning Hash with Sequential Buckets Partitioning
  • Supervisor: Hiroshi Nakagawa

Department of Mathematics, the University of Tokyo (Apr. 2006 – Mar. 2010)

  • Studied topology and combinatorial optimization

Job Experiences

Researcher at Preferred Networks, Inc. (Oct. 2014 – current)

  • Working on machine learning, computer vision, and their applications to IoT fields
  • Developing Chainer, a neural network framework

Researcher/Engineer at Preferred Infrastructure, Inc. (Apr. 2012 – Oct. 2014)

  • Worked on machine learning, natural language processing, and computer vision
  • Was mainly engaged in the Jubatus project


  • pixiv, Inc. (Mar. 2011), worked on improvements of recommender systems
  • Google Japan, Inc. (Aug. 2010 – Sep. 2010), worked at Google Japanese Input team (a.k.a. mozc) and created the calculator feature

Publications and Presentations

Conference Papers (refereed)

Seiya Tokui, Issei Sato, Hiroshi Nakagawa. Locally Optimized Hashing for Nearest Neighbor Search. In Advances in Knowledge Discovery and Data Mining, 19th Paci c-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II, 498–509.

Workshop Papers (refereed)

Seiya Tokui, Kenta Oono, Shohei Hido, Justin Clayton. Chainer: a Next-Generation Open Source Framework for Deep Learning. In Workshop on Machine Learning Systems at Neural Information Processing Systems (NIPS), 2015.

Shohei Hido, Satoshi Oda and Seiya Tokui. Jubatus: An Open Source Platform for Distributed Online Machine Learning. In Big Learning Workshop at Neural Information Processing Systems (NIPS), 2013.


Seiya Tokui, Kenta Oono, Atsunori Kanemura, and Toshihiro Kamishima. Deep Learning Implementations and Frameworks, at the 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2016.


I have written one book on online machine learning with three colleagues in Japanese. I also have had six presentations in domestic conferences/workshops (see Japanese version).

Software Projects

Chainer: a Neural Network Framework (Apr. 2015 – current)

maf: a Build Tool for Parameterized Experimentations (Dec. 2012 – Mar. 2015)

Jubatus: a Distributed Machine Learning Framework (Apr. 2012 – Apr. 2014)


  • Programming: C++/Python (advanced), Go/Javascript/Julia/Ruby (basic)
  • Communication: Japanese (native), English (basic)