Hideaki Takahashi
日本語版はこちらI am a first-year PhD student in Computer Science department at Columbia University, advised by Prof. Junfeng Yang. I also work closely with Prof. Suman Jana. My research interests revolve around AI, security, privacy, and systems. My current work focuses on Zero Knowledge Proofs.
I received a Bachelor of Arts and Science in Informatics from the University of Tokyo, where I was advised by Prof. Alex Fukunaga, focusing on privacy-preserving AI planning. During my undergraduate studies, I spent a gap year as a research intern at Tsinghua University under the supervision of Prof. Yang Liu and Prof. Jingjing Liu, investigating security vulnerabilities in Federated Learning.
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.
Education
- Columbia University - Sep. 2024 ~
Ph.D. Student (Computer Science) - The University of Tokyo - Apr. 2019 ~ Mar. 2024
Bachelor of Arts and Science (Informatics)
Papers (peer-reviewed)
- [AAMAS'24] On the Transit Obfuscation Problem. Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems - 2024
Hideaki Takahashi*, Alex Fukunaga - [ICLR'24] VFLAIR, A Research Library and Benchmark for Vertical Federated Learning. Proceedings of the International Conference on Learning Representations - 2024
Zou, Tianyuan, Zixuan Gu, Yuanqin He, Hideaki Takahashi, Yang Liu, Guangnan Ye and Ya-Qin Zhang - [CVPR'23] Breaching FedMD, Image Recovery via Paired-Logits Inversion Attack. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition - 2023
Hideaki Takahashi*, Jingjing Liu, and Yang Liu.
Preprints
- Eliminating Label Leakage in Tree‑based Vertical Federated Learning. arXiv:2307.10318 - 2023
Hideaki Takahashi*, Jingjing Liu, and Yang Liu - Difficulty of Detecting Overstated Dataset Size in Federated Learning. Technical Report of DPS, 10, Information Processing Society of Japan. - 2021
Hideaki Takahashi*, Kohei Ichikawa, and Keichi Takahashi
Softwares
- AIJack
Security risk simulator for machine learning (more than 300 stars on GitHub and used in 8 papers) - rhoevm
Symbolic EVM execution engine written in Rust to find vulnerabilities within Ethereum smart contracts - Gymbo
Symbolic execution engine using gradient descent to solve path constraints - My*
This series aims to implement various algorithms, compilers, and solvers from scratch.
- MyDisassembler: Disassembler for X86-64 implemented from scratch in C++.
- MyCompiler: Toy compiler from a simple language to LLVM-IR implemented from scratch in Haskell.
- MyPlanner: PDDL Solver implemented from scratch in C++.
- MyOptimizer: Implementations of popular optimization and search algorithms.
Research Experience
- Fukunaga Lab, The University of Tokyo - Undergraduate Student
Apr. 2023 - Mar. 2023
Conducted research on the transit obfuscation problem, which is a new task of privacy-preserving AI planning, under the supervision of Prof. Alex Fukunaga. - 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 Analysis, 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 - Jul. 2024
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
- Scholarship, Funai Overseas Scholarship - 2024
Granted two years of tuition and stipend - 45th/616 teams (silver medal), Kaggle, Google - Fast or Slow? Predict AI Model Runtime - 2023
Compiler Optimization, Automated Algorithmic Configuration - 67th/875 teams (bronze medal), Kaggle, Hungry Geese - 2021
AI Agent, Reinforcement Learning - 52nd/788 teams (bronze medal), Kaggle, Santa 2020 - 2021
AI Agent, Reinforcement Learning, Multi-Armed Bandit - 51st/1138 teams (silver medal), Kaggle, Google Research Football with Manchester City F.C. - 2020
AI Agent, Reinforcement Learning - 88th/1390 teams (bronze medal), Kaggle, Cornell Birdcall Identification - 2020
Audio Classification, Signal Processing
Service
- Reviewer
- IEEE Transactions on Network Science and Engineering (Impact Factor: 6.7):
- IEEE Transactions on Medical Imaging (Impact Factor: 10.0): - OSS Contributor
- PySyft: Platform for secure and private Deep Learning
- Nebula: Distributed graph database