Godai Azuma

Master's Student (2nd year)

Department of Mathematical and Computing Science,
School of Computing,
Tokyo Institute of Technology.

E-mail
godai0519 [at] gmail.com
azuma.g.aa [at] m.titech.ac.jp
PGP
BE4D 005E 8903 C338

Education

Present
Department of Mathematical and Computing Science,
School of Computing,
Tokyo Institute of Technology (Master's course).
B. Eng.
Department of Communication Engineering and Informatics,
Faculty of Informatics and Engineering,
The University of Electro-Communications (2018).
Assoc. Eng.
(not a degree)
Department of Computer Science,
National Institute of Technology, Tokyo College (2016).

Societies

Mar. 2019 – Today
A student member of the Operations Research Society of Japan.

Softwares

BayesianNetwork
Framework for learning and reasoning Bayesian Networks, written in C++14.
twit-library
OAuth1.0 client library in C++11, powered by BoostConnect.
BoostConnect
Server/Client framework wrapping Boost.Asio (You should try to use cpp-netlib and Networking TS).
Window Changer
Utility software which associates keyboards with a unique window, and actives associated window when pressing keyboard. This is awarded a prize of "Director-General, Commerce and Information Policy Bureau" in 32th U-20 Programming Contest, Japan.

Refereed Conference Papers

  1. Godai Azuma, Daisuke Kitakoshi, and Masato Suzuki,
    Stepwise Structure Learning Using Probabilistic Pruning for Bayesian Networks: Improving Efficiency and Comparing Characteristics.
    Information Science and Applications 2017 (ICISA 2017),
    Lecture Notes in Electrical Engineering 424 (2017), pp. 533–543.
    DOI: 10.1007/978-981-10-4154-9_62.

Oral presentations

  1. 東 悟大,
    二次制約付二次計画問題のSDP緩和における厳密性判定法の応用とその考察.
    未来を担う若手研究者の集い 2019, Workshop on Optimization and its Applications (OPTA), Operations Research Society of Japan, 7-2, 2019.
  2. 東 悟大, 北越 大輔, and 鈴木 雅人,
    確率的枝刈りを用いたベイジアンネットの構造学習法の高速化.
    電子情報通信学会 2016年総合大会講演論文集 (IEICE2016), D-20-8, 2016, pp. 208.
  3. Daisuke Kitakoshi, Godai Azuma, and Masato Suzuki,
    Improving Learning Speed in Stepwise Structure LearnIing Method for Bayesian Networks by using Probabilistic Pruning.
    IPSJ SIG Technical Report, 2016-ICS-182 (2), 2016, pp. 1–8.
  4. 東 悟大, 北越 大輔, and 鈴木 雅人,
    クラスタリングと確率的枝刈りを用いたベイジアンネットの段階的構造学習法 −確率的枝刈りの性能改善および特性評価−.
    計測自動制御学会 システム・情報部門 学術講演会 2015, SS4-1, 2015, pp. 666–671.

Presentation materials

  1. 闇鍋の中に投げ込むDartの矢, 闇鍋プログラミング勉強会, 03/2012.