Hideaki Takahashi

Hideaki Takahashi

Hello everyone,
I recently graduated from the University of Tokyo with a Bachelor of Arts and Science in Informatics (class of 2024). My research interests revolve around AI, security, privacy, and systems.

During my undergraduate studies, I was fortunate to work under Prof. Alex Fukunaga, focusing on theoretical aspects of privacy-preserving path planning. I successfully published my senior thesis at AAMAS. Additionally, I spent a gap year as a research intern at Tsinghua University under the supervision of Prof. Yang Liu and Prof. Jingjing Liu, investigating privacy vulnerabilities in Federated Learning, resulting in a first-authored CVPR paper.

I am also passionate about open-source development. My project, AIJack, which aims to attack, defend, and debug machine learning models, has gained traction with over 300 stars on GitHub and has been widely used in research.
Semantic Scholar  Google Scholor  GitHub 

Education

  • The University of Tokyo - Apr. 2019 ~ Mar. 2024
    Bachelor of Arts and Science (Informatics). Major GPA 3.91/4.0 (Overall: 3.83/4.0)

Papers (peer-reviewed)


Preprints


Softwares

  • AIJack

    AIJack is an easy-to-use open-source simulation tool for testing the security of your AI system against hijackers. It provides advanced security techniques like Differential Privacy, Homomorphic Encryption, K-anonymity, Debugging, and Federated Learning to guarantee protection for your AI. With AIJack, you can test and simulate defenses against attacks such as Evasion, Poisoning, Model Inversion, Backdoor, and Free-Rider. AIJack also provides a simple DBMS for SQL-based methods.
  • Gymbo

    Gymbo is a Proof of Concept for a Gradient-based Symbolic Execution Engine implemented from scratch. Building on recent advancements that utilize gradient descent to solve SMT formulas, Gymbo leverages gradient descent to discover input values that fulfill each path constraint during symbolic execution.
  • My*
    This series aims to implement various algorithms, compiliers and solvers from scratch.
    - MyCompiler: Toy compiler from a simple language to LLVM-IR implemented from scratch in Haskell.
    - MyPlanner: PDDL Solver implemented in C++ from scratch.
    - MyOptimizer: Implementations of popular optimization and search algorithms.

Research Experience

  • Institute for AI Industry Research, Tsinghua University - Federated Learning & Privacy Computing Intern
    Jan. 2022 ‑ Feb. 2023
    Conducted research on federated learning and privacy computing under the supervision of Prof. Yang Liu and Prof. Jingjing Liu.
  • Laboratory for Software Design and Analyis, Nara Institute of Science and Technology - Visiting Student
    Aug. 2021 ‑ Sep. 2021
    Conducted research on the free‑rider problem of federated learning under the supervision of Prof. Kohei Ichikawa and Prof. Keichi Takahashi.

Industry Experience

  • Apple Inc. - Technical Internship: AIML/Software Engineer
    Feb. 2024 - present
    Working on AIML/software engineering.
  • UTokyo Economic Consulting Inc. - Research Assistant
    Oct. 2020 - present
    Working on research and social implementations of econometrics and machine learning.
  • RECRUIT - Data Science Intern
    Aug. 2020 - Sep. 2020
    Worked on a location‑based restaurant recommendation iOS app.
  • M3, Inc. - Data Analysis Intern
    Feb. 2020 - Jun. 2020
    Worked on a data analysis project in the field of medical surveys.
  • FRONTEO, Inc. - Research Intern
    Sep. 2019 ‑ Mar. 2020
    Worked on the detection of anomaly documents with NLP and network analysis.

Honors