people
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layout: about title: about permalink: / subtitle: “Ludwig Boltzman, who spent much of his life studying statistical mechanics, died in 1906, by his own hand. Paul Ehrenfest, carrying on the work, died similarly in 1933. Now it is our turn to study statistical mechanics. Perhaps it will be wise to approach the subject cautiously.”
profile: align: right image: prof_pic.png image_circular: true # crops the image to make it circular more_info: > <p>4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8503 Japan</p>
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Biography
I am a Ph.D. candidate at Institute of Science Tokyo (formerly Tokyo Institute of Technology), where I am supervised by Prof. Konstantinos Slavakis and advised by Prof. Jun Sakuma. My research focuses on the intersection of quantum computing, machine learning, and AI security.
I obtained my B.S. in Physics from the University of California, San Diego and M.S. in Experimental Particle Physics from KEK
Quantum Software Development
LogosQ is a quantum computing library written in Rust. The library focuses on efficient quantum circuit simulation and optimization. For more information, tutorials, and documentation, please visit https://logosqbook.vercel.app/. If you are interested in optimization and computation in Rust, feel free to check it out!
Quantum Machine Learning
I have recently published research on novel feature extraction algorithms for graphs that can be effectively applied with Variational Quantum Circuits (VQCs) in resource-limited settings. This work bridges classical graph theory with quantum machine learning techniques.
AI Security
My ongoing work includes security analysis and attacks on various fine-tuned and pretrained AI models, including Llama2-7B, Llama3.1-8B-Instruct, Qwen, DeepSeek, and GPT family models. This research aims to understand and improve the robustness of large language models against adversarial attacks.
555 your office number
123 your address street
Your City, State 12345
layout: about title: about permalink: / subtitle: “Ludwig Boltzman, who spent much of his life studying statistical mechanics, died in 1906, by his own hand. Paul Ehrenfest, carrying on the work, died similarly in 1933. Now it is our turn to study statistical mechanics. Perhaps it will be wise to approach the subject cautiously.”
profile: align: right image: prof_pic.png image_circular: true # crops the image to make it circular more_info: > <p>4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8503 Japan</p>
selected_papers: true # includes a list of papers marked as “selected={true}” social: false # includes social icons at the bottom of the page
announcements: enabled: true # includes a list of news items scrollable: true # adds a vertical scroll bar if there are more than 3 news items limit: 5 # leave blank to include all the news in the _news folder
latest_posts: enabled: false scrollable: true # adds a vertical scroll bar if there are more than 3 new posts items limit: 3 # leave blank to include all the blog posts —
Biography
I am a Ph.D. candidate at Institute of Science Tokyo (formerly Tokyo Institute of Technology), where I am supervised by Prof. Konstantinos Slavakis and advised by Prof. Jun Sakuma. My research focuses on the intersection of quantum computing, machine learning, and AI security.
I obtained my B.S. in Physics from the University of California, San Diego and M.S. in Experimental Particle Physics from KEK
Quantum Software Development
LogosQ is a quantum computing library written in Rust. The library focuses on efficient quantum circuit simulation and optimization. For more information, tutorials, and documentation, please visit https://logosqbook.vercel.app/. If you are interested in optimization and computation in Rust, feel free to check it out!
Quantum Machine Learning
I have recently published research on novel feature extraction algorithms for graphs that can be effectively applied with Variational Quantum Circuits (VQCs) in resource-limited settings. This work bridges classical graph theory with quantum machine learning techniques.
AI Security
My ongoing work includes security analysis and attacks on various fine-tuned and pretrained AI models, including Llama2-7B, Llama3.1-8B-Instruct, Qwen, DeepSeek, and GPT family models. This research aims to understand and improve the robustness of large language models against adversarial attacks.