Brief Biography

I am a research engineer at the AutoML group of Preferred Networks Inc. Before joining the group, I was studying under the supervision of Prof. Frank Hutter at the University of Freiburg in Computer Science department. The main interests of my research are practical extensions of blackbox optimization on expensive functions. More specifically, I have been tackling multi-objective optimization, constraint optimization, meta-learning of hyperparameter optimization, and the interpretation of optimizations. For more details, please check my Research Experiences.

Prior to the Master course, I finished the Bachelor at the University of Tokyo, Systems Innovation Faculty supervised by Prof. Chen Yu in March 2020. During my Bachelor, I was engaged in the National Institute of Advanced Industrial Science and Technology (AIST) supervised by Masaki Onishi. The details of my works are available here (My works). Personal ongoing projects are German and functional analysis learning.

Download CV

I was intensively working on extensions of tree-structured Parzen estimator (TPE). For more details, please check my publications.

• A tutorial of tree-structured Parzen estimator
  Paper title: Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance.
  Only preprint exists.
• Constrained optimization for tree-structured Parzen estimator
  Paper title: c-TPE: Tree-Structured Parzen Estimator with Inequality Constraints for Expensive Hyperparameter Optimization.
  Accepted by International Joint Conference on Artificial Intelligence (IJCAI2023).
• Multi-objective optimization for tree-structured Parzen estimator
  Paper title: Multiobjective Tree-Structured Parzen Estimator for Computationally Expensive Optimization Problems.
  Accepted by Journal of Artificial Intelligence Research (JAIR).
• Meta-learning (or transfer learning) for tree-structured Parzen estimator
  Paper title: Speeding Up Multi-Objective Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-Structured Parzen Estimator.
  Accepted by International Joint Conference on Artificial Intelligence (IJCAI2023).

I listed the ones related to only research. For more details, please check my CV.