SHIMIZU Shohei

J-GLOBAL         Last updated: Jul 19, 2019 at 02:50
 
Avatar
Name
SHIMIZU Shohei
E-mail
shohei-shimizubiwako.shiga-u.ac.jp
URL
https://sites.google.com/site/sshimizu06/
Affiliation
Shiga University
Section
Faculty of Data science
Job title
Professor
Degree
Doctor of Philosophy in Engineering(Osaka University)
Research funding number
10509871
Twitter ID
sshimizu2006

Research Areas

 
 

Academic & Professional Experience

 
Apr 2016
 - 
Mar 2017
Associate Professor, Center for Education and Research of Data Science, Shiga University
 
Apr 2017
 - 
Mar 2018
Associate Professor, Faculty of Data science, Shiga University
 
Apr 2018
 - 
Today
Professor, Faculty of Data science, Shiga University
 

Education

 
 
 - 
Mar 2001
Faculty of Human Science, Osaka University
 
 
 - 
Mar 2003
Graduate School, Division of Human Science, Osaka University
 
 
 - 
Mar 2006
Graduate School, Division of Engineering Science, Osaka University
 

Committee Memberships

 
Jan 2019
 - 
Today
Elsevier Neurocomputing  Associate editor
 

Awards & Honors

 
Sep 2016
Hayashi Chikio Award (Excellence Award)
 

Published Papers

 
Visualizing Shiga Prefecture using RESAS: cloud-based analysis system with government open big data
Jong chan Lee,Tetsuto Himeno,Shohei Shimizu,Takuma Tanaka,Akimichi Takemura
Proc. 2nd International Conference on Big Data, Cloud Computing, and Data Science (BCD2017)      2017   [Refereed]
A novel principle for causal inference in data with small error variance
Patrick Blobaum, Shohei Shimizu, Takashi Washio
Proc. 25 th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN2017)      Apr 2017   [Refereed]
Error asymmetry in causal and anticausal regression
Patrick Blobaum,Takashi Washio,Shohei Shimizu
Behaviormetrika      Apr 2017   [Refereed]
Estimation of interventional effects of features on prediction
Patrick Blobaum,Shohei Shimizu
Proc. 2017 IEEE Machine Learning for Signal Processing Workshop (MLSP2017)      Sep 2017   [Refereed]
Ricardo Silva,Shohei Shimizu
Journal of Machine Learning Research   18 1-49   Nov 2017   [Refereed]

Misc

 
Ricardo Silva, Shohei Shimizu
   Nov 2015
Learning a causal effect from observational data is not straightforward, as
this is not possible without further assumptions. If hidden common causes
between treatment Tex and outcome Tex cannot be blocked by other measurements,
one possibility is...
TANAKA NAOKI, SHIMIZU SHOHEI, WASHIO TAKASHI
人工知能学会人工知能基本問題研究会資料   95th 27-31   Oct 2014
Naoki Tanaka, Shohei Shimizu, Takashi Washio
   Aug 2014
A large amount of observational data has been accumulated in various fields
in recent times, and there is a growing need to estimate the generating
processes of these data. A linear non-Gaussian acyclic model (LiNGAM) based on
the non-Gaussianity ...
SUZUKI YUZURU, SHIMIZU SHOHEI, WASHIO TAKASHI
人工知能学会人工知能基本問題研究会資料   94th 35-40   Jul 2014
Joe Suzuki, Takanori Inazumi, Takashi Washio, Shohei Shimizu
   Jan 2014
The notion of causality is used in many situations dealing with uncertainty.
We consider the problem whether causality can be identified given data set
generated by discrete random variables rather than continuous ones. In
particular, for non-bina...

Books etc

 
Probabilistic graphical models
Joe Suzuki, Maomi Ueno, Manabu Kuroki, Shohei Shimizu, Shin-ichi Minato, Masakazu Ishihata, YOSHIYUKI KABASHIMA, Kazuyuki Tanaka, Yoichi Motomura, Yoshinori Tamada (Part:Joint Work)
KYORITSU SHUPPAN CO., LTD.   Jul 2016   ISBN:978-4-320-11139-4

Conference Activities & Talks

 
Non-Gaussian methods for causal discovery [Invited]
Shohei Shimizu
International Workshop on Causal Inference   6 Jan 2016   
Statistical estimation of causal directions based on observational data [Invited]
Shohei Shimizu
The 3rd CiNet Conference - Neural Mechanism of Decision Making: Achievements and New Directions   3 Feb 2016   
Causal discovery and non-Gaussianity [Invited]
Shohei Shimizu
Computational Science and Visual Analytics   1 Mar 2016   
Causal discovery [Invited]
Shohei Shimizu
The 3rd Methodology Seminar of the Japanese Society of Social Psychology   16 Mar 2016   
因果探索: 基本から最近の発展までを概説 [Invited]
清水 昌平
第23回情報論的学習理論と機械学習研究会 (IBISML)   17 Mar 2016   

Research Grants & Projects

 
Causal feature learning
ONRG NICOP
Project Year: Jan 2017 - Jan 2020    Investigator(s): Shohei Shimizu