Hiroki Tanabe

ディスプレイ広告のCVR予測モデル開発業務に従事。Hadoopクラスタ上でHiveによるデータ集計、Jupyter Notebookによる新規機械学習モデル開発・実験、Pythonによる機械学習モジュールの開発、Argoworkflow・Airflow・Screwdriverなどを用いたCI/CDによるDevOps・MLOpsパイプラインの構築、本番環境でのA/Bテストなどを一気通貫に経験。

ゲーム実況・歌ってみたを投稿しています。累計500万PV、2万登録者。

Awarded to excellent Ph.D. students, this fellowship offers the fellows an opportunity to focus on a freely chosen research topic based on their own innovative ideas. This program's recruitment phase has a very narrow selection process with a selection ratio of 20%.

博士(情報学) 連続最適化/多目的最適化の研究に従事

Developed a coding test tool in a four-person engineering team to improve efficiency in HR. Communicated with the HR staff and asked about their recruiting problems. Designed the prototype with Miro. Implemented authentication by Google account, uploading source codes, and grading each submission, using Vue.js and Firebase.

Estimated heterogeneous treatment effect on earnings of Yahoo! Japan Auction of a recommendation system by using Machine Learning with Python on Jupyter Notebook. Analyzed seven days of preprocessed traffic data of Yahoo! Japan Auction, including the click data of the recommendation banner, in the form of Pandas DataFrame. Implemented X-L

Built a new Ruby on Rails web application from scratch. It enables users to record and rate recipes they have used. Using the Lean Startup Methodology, developed the MVP (Minimum Viable Product) that satisfies the value hypothesis and utilized a feedback loop through user testing. Presented a prototype of the product by using Figma. Imple

Designed and developed some machine learning models for the manufacturing and agricultural industry, mainly through analyzing a small number of image datasets, typically using Python. In particular, developed models for surface defect detection through various techniques, including dictionary learning and fused lasso and hyperspectral ima

Chosen as the valedictorian for the degree conferment ceremony.