HARUYA UMEMOTO
ML Engineer
Biography
Haruya Umemoto studied computer science at university/graduate school and conducted research using machine learning and deep learning. His research interests include reinforcement learning and natural language processing (NLP), and he has recently developed an understanding of research in the field of imaging. After graduating, he joined an AI venture, where he was in charge of research and development of large-scale language models, dialogue systems, etc., with a focus on NLP. In his current position, he focuses on providing AI solutions using his own knowledge, and is also involved in setting up other AI teams and product development. He is interested not only in the ML area but also in IT in general, and has skills in front-end, back-end and cloud computing.
NEWS
Recently, I have been supporting the launch of generative AI projects and consulting. Please contact me from the Contact in the upper right corner. (If there is no response, it's possible that it might have been overlooked, so please contact me via X or LinkedIn.)
Skills
Language: Python, Dart, TypeScript, Rust, Go, C++, Java, JavaScript
クラウド: Google Cloud(LOVE), Firebase, AWS, Azure
Framework: PyTorch, Docker, Flutter, React, Next.js, Terraform
Specialization: ML, DL, RL, NLP, Generative AI, Data analysis, Dialogue system
Prizes/Certifications
2023 - Ledge.ai CHALLENGE Generative AI Hackason Outstanding Award 2022 - GCP Professional Data Engineer 2020 - JSAI Outstanding Paper Award 2020 - Komoda Advanced Science Academic Award 2020 - The Highest Grade Point Award 2020 - Outstanding Presentation of Master Thesis Award 2017 - Data analysis&Simulation Hackason 2017 Second prise (SIG-DOCMAS) 2015 - Award of Aoyama gakuin University (Hack U 2015 at Aoyama gakuin University)
Career
2021/10, ML Engineer, TC3 2020/04, ML Engineer, Arithmer 2020/03, Graduated shcool of Aoyama gakuin university, Master of engineering 2018/08, Internship, Cookpad 2018/03, Aoyama gakuin University, Bachelor of engineering
Pubilications
Slides
- 【勉強会資料】全力解説!Transformer
- 【Cookpad Intern】レシピの分散表現を用いたアレンジ度の算出
- 【WSSIT2019】食材名の分散表現学習を用いた料理レシピの栄養推定手法
- 【WSSIT2018】料理レシピの分散表現を用いた代替食材の発見手法
- 【WSSIT2017】過去の変動に対する類似検索を用いた短時間USD/JPY為替レート予測
- 【研究室論文紹介】Distributed prioritized experience replay
- 【研究室論文紹介】Using an Artificial Financial Market for studying a Cryptocurrency Market
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