• Python: 5 years of experience, focusing on multi-objective optimization research in NEAT (NeuroEvolution of Augmenting Topologies) during my graduate studies.
• C++: 2 year of experience, used for developing simulations in reinforcement learning research.
• C# and XAML: Experience in developing Windows applications(MVVM) as a part-time job.
• Frontend: Participated in several projects using React, JavaScript, CSS, HTML, and Next.js. Rich experience in hackathons, notably developing a web application for visualizing Chrome history, emphasizing interactive user experiences.

• Deepened my practical knowledge in data analysis and database design through the Data Science Course (GCI) hosted by Matsuo Lab at the University of Tokyo, including business reform proposals using actual corporate data.

• Engaged in multiple internships learning the IT solution provision process(NTT Advanced Technology,TIS Corporation,NTT Docomo,IBM Japan,American Museum of Natural History Computer Visualization Group,Recruit ,Nomura Research Institute) and contributed to the development of factory production management software(DigitWorks Co., Ltd).

• AWS(Cloudfront,S3,API Gateway,lambda): Deployed a web application with a microservices architecture on AWS using Docker Compose during a hackathon. Managed multiple containers (API, database, frontend, etc.) collectively, facilitating efficient launch and shutdown processes.

Combining NEAT (NeuroEvolution of Augmenting Topologies) with NSGA-II (Non-dominated Sorting Genetic Algorithm II) creates an efficient method for evolving neural networks that optimally balance multiple objectives, such as performance and complexity. NEAT's ability to dynamically evolve network structures is paired with NSGA-II's capability to handle multiple optimization goals simultaneously, leading to a set of diverse, high-quality solutions on the Pareto front. This hybrid approach is especially useful in applications requiring trade-offs between competing objectives, offering a range of optimal solutions for different needs.