Welcome to Slavakis Lab
We are a research team (est. April 2021) at the Tokyo Institute of Technology that designs computational methods for signal processing and machine learning.
Our designs aim at a wide variety of applications and themes. Examples are:
- biomedical imaging;
- nonparametric regression;
- manifold learning;
- network time-series analysis;
- reinforcement learning and dynamic programming;
- online learning and adaptive filtering;
- optimization with a focus on nonexpansive and monotone mappings; and
- quantum signal processing and machine learning.
信号処理と機械学習の研究を行なっている。信号やデータの背景に 潜む幾何学的構造を抽出し、これを最大限活用することによって、 これまでの信号処理と機械学習を凌駕する柔軟なフレームワークの 構築を目指している。広い領域への応用を視野に入れているが、現 在、特に医用イメージング、脳ネットワーク、適応信号処理、確率 近似と強化学習への応用に注力し、研究を進めている。
We are part of the Department of Information and Communications Engineering and the Human Centered Science and Biomedical Engineering Major of the Tokyo Institute of Technology.
We are a small team, but we wish to grow! We are looking for passionate new PhD/MSc/BSc students as well as PostDocs to join the team (more info) !
We are currently looking for ambitious MSc/PhD students to promote the Lab’s research also to the theme of data fusion and multimodal learning from biomedical data.
News
July 2024:
Thien’s recent work on multilinear kernel regression and imputation, and its applications to imputation of time-varying graph signals and dMRI data, will appear in the IEEE Open Journal of Signal Processing.
April 2024:
Yuki and Minh’s work on novel nonparametric Bellman mappings for reinforcement learning, and their application to robust adaptive filtering, has been submitted for publication and appears also as a preprint at arXiv and TechRxiv.
March 2024:
Minh and Thien graduated from the MSc course, were admitted to the PhD course and will continue to be members of our Lab. Congrats!
March 2024:
Saruul graduated from the BSc course. Congrats! All the best for your future career Saruul!
February 2024:
Thien’s recent work on multilinear kernel regression and imputation, and its applications to imputation of time-varying graph signals and dMRI data appears as a preprint at arXiv and TechRxiv.
December 2023:
Thien’s work on multilinear kernel regression and imputation, and Yuki’s work on proximal Bellman mappings and reinforcement learning will be presented at IEEE ICASSP 2024, Seoul, Korea, 14-19 April, 2024.
December 2023:
Mr. Kotaro Yoshida joins the Lab as a BSc student! Welcome aboard Kotaro!
October 2023:
New paper on estimation of magnetic-nanoparticle distributions appears in Journal of Magnetism and Magnetic Materials.
October 2023:
Thien’s work on multilinear kernel regression and imputation, and Yuki’s work on proximal Bellman mappings and distributed reinforcement learning will be presented at the Signal Processing (SIP) Symposium, Kyoto, Japan, November 6-8, 2023.