About me

Hi! My name is Yuichiro Ueno (上野 裕一郎). I am a master student at Tokyo Institute of Technology. Currently, I am working on High Performance Computing and Deep Learning.

  • Specialized in backend technologies, such as Linux, OpenMP/MPI, CUDA, Networking(InfiniBand, TCP/IP) and Security.
  • 1+ years work experience in C/C++/Python at a deep learning startup company.
  • Three papers accepted by top conferences in high-performance computing and machine learning field.

Work Experience

Internship / Part-time Engineer (Aug. 2017 - Mar. 2019)

Internship (Dec. 2019 - Jan. 2020)

Internship / Part-time Engineer (May 2019 - Jun. 2020)

Research Assistant (May 2019 - current)


Publications (all peer-reviewed)

  • Yuichiro Ueno, Kazuki Osawa, Yohei Tsuji, Akira Naruse, Rio Yokota, Rich Information is Affordable: A Systematic Performance Analysis of Second-order Optimization Using K-FAC, Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Aug. 2020.

  • Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Chuan-Sheng Foo, Rio Yokota, Scalable and Practical Natural Gradient for Large-Scale Deep Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, Jun. 2020.

  • Yohei Tsuji, Kazuki Osawa, Yuichiro Ueno, Akira Naruse, Rio Yokota, Satoshi Matsuoka, Performance Optimizations and Analysis of Distributed Deep Learning with Approximated Second-Order Optimization Method, Proceedings of the 48th International Conference on Parallel Processing: Workshops, No. 21, Aug. 2019.

  • Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Rio Yokota, Satoshi Matsuoka. Large-Scale Distributed Second-Order Optimization Using Kronecker-Factored Approximate Curvature for Deep Convolutional Neural Networks, IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun. 2019.

  • Yuichiro Ueno, Rio Yokota. Exhaustive Study of Hierarchical AllReduce Patterns for Large Messages Between GPUs, 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), May. 2019.


Education

(Candidate of) Master of Engineering in Computer Science (Apr. 2019 - present)

Bachelor of Engineering in Computer Science (Apr. 2017 - Mar. 2019)

Associate Degree of Engineering (Apr. 2012 - Mar. 2017)