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19 Posts
6 Categories
11 Tags
CAI Ming
19 Posts
6 Categories
11 Tags
CAI Ming
Second-year Ph.D student, Department of Data Science, Graduate School of Informatics, Kyoto University
Panjin, Liaoning, China -> Weihai, Shandong -> Yantai -> Dalian, Liaoning -> Kusatsu, Shiga, Japan -> Kyoto.
shishishi.
  My Projects
Tiho
Tiho, an information reminder application for Android.
CodeCraft
2019 codecraft hold by Huawei.
RoutePlanning
A route planning demo based on Q learning.
  My Skills
Python
70%
C++
75%
Machine Learning
50%
Causal Discovery
50%
Generation Algorithm
80%



  Other Skills
Japanese
65%
English
60%




  Resume

CAI MING

+86-186-1516-8238||caiming106[ * ]gmail.com||cai.ming.52d[ * ]st.kyoto-u.ac.jp

change [ * ] to @

Education

2017.09 - 2021.09 Dalian Univerisy of Technology(大连理工大学)

Digital Media Technology

GPA：3.9/5.0 (top 1%)

2019.09 - 2021.09 Ritsumeikan University(立命館大学)

Information Science and Engineering

GPA：4.5/5.0 (top 2%)

2022.04 - 2024.03 Kyoto University(京都大学)

Graduate School of Informatics

2024.04 - present Kyoto University(京都大学)

Graduate School of Informatics

Journal

  • Ming Cai, Penggang Gao, Hisayuki Hara: Learning linear acyclic causal model containing Gaussian disturbances using ancestral relationships, International Journal of Data Science and Analytics 22 (1), 163.
  • Ming Cai, Hisayuki Hara: Learning causal graphs using variable grouping according to ancestral relationship, Computational Statistics, 40(7), 3947-3979.

Conference

  • Cai Ming, 原尚幸: 潜在交絡因子を伴う最もスパースな線形因果DAGの学習, 統計関連学会連合大会, 2026/09.
  • Penggang Gao, Ming Cai, Hisayuki Hara: Causal DAG Identification for Count Data via Poisson-Thinning Structural Equation Models, CMStatistics 2025, 2025/12.
  • Ming Cai, Penggang Gao, Hisayuki Hara: Learning Causal Graphs Involving Latent Variables Using Higher-Order Cumulants, IASC-ARS 2025, 2025/12.
  • 高鵬鋼, Cai Ming, 原尚幸: ポアソンシニング構造方程式を用いたカウントデータの因果DAGの識別, 統計関連学会連合大会, 2025/09.
  • Cai Ming, 高鵬鋼, 原尚幸: 高次キュムラントを用いた潜在変数を含む因果DAG推定のためのピーリングアルゴリズム, 統計関連学会連合大会, 2025/09.
  • Ming Cai, Penggang Gao, Hisayuki Hara: Learning linear acyclic causal model including Gaussian noise using ancestral relationships, CMStatistics 2024, 2024/12.
  • Cai Ming, Penggang Gao, Hao Wang, Hisayuki Hara: 祖先関係を利用したガウスノイズを含む線形非巡回因果モデルの学習, 統計関連学会連合大会予稿集, 2024/09.
  • Ming Cai, Hisayuki Hara: Ancestral Relationship-based Variable Grouping Method for Causal Discovery, 統計関連学会連合大会予稿集, 2023/09.
  • Ming Cai, Takayoshi Kitamura and Tomoko Izumi：A proposal for an evacuation route recommendation method based on multi-objective GA considering the acceptability of evacuees, 10th International Congress on Advanced Applied Informatics (IIAI AAI 2021), SCAI 2021, 2021/07.

MISC

  • Ming Cai, Hisayuki Hara: Learning Sparsest Linear Causal DAGs with Latent Confounders via Higher-Order Cumulants, arxiv, 2026/07.
  • Ming Cai, Penggang Gao, Hisayuki Hara: Causal Discovery for Linear DAGs with Dependent Latent Variables via Higher-order Cumulants , arxiv, 2025/10.
  • 蔡 銘, 北村 尊義, 泉 朋子：避難者の受容性を考慮した多目的GAに基づく避難経路推薦手法の提案，第83回情報処理学会全国大会, 5ZA-02(2ページ), 2021/03.

Project Experience

2019: Huawei CodeCraft

Competition

Project introduction: Traffic scheduling by writing algorithms.

Task: construct Prim's minimum spanning tree and topological sorting by C++.

https://github.com/codecraftkuroyama/codeCraft

2019: Tiho

Project introduction: A campus information notification software for students of Dalian University of Technology.

Task: use the crawler to gather information by node.js and design the database.

https://github.com/DUTCRJS/Manager