Atsushi Nitanda

Principal Scientist and Investigator
A*STAR Centre for Frontier AI and Research (A*STAR CFAR)

Supported by JST PRESTO (Math-structure area, Oct. 2019 -- Mar. 2023)

Ph.D. in Information Science and Technology. The University of Tokyo. Supervisor: Taiji Suzuki

Research interests: Stochastic Optimization, Mean-field Optimization, Machine Learning, Deep Learning

E-mail: atsushi_nitanda [at]

Address: 1 Fusionopolis Way, #16-16, Connexis (North Tower), Singapore 138632

A*STAR scholarship and internship

A*STAR offers a variety of scholarships and internship opportunities for students who want to study at A*STAR and universities in Singapore.  For details, see the call for applicants for scholarships

For Singaporean / local university students:

For international students: (Japanese students are also welcome to apply!)

Please contact me if you are interested in these opportunities under my supervision.

     * denotes alphabetical ordering below

Conference Papers (Refereed)

Journal Articles

Technical Reports


Atsushi Nitanda. Note: Noise Conditions and Convergence Analysis of SGD under Polyak-Lojasiewicz Inequality. 2022. [link]

Doctor Thesis

Efficient Machine Learning from Gradient Method Perspective in Finite and Infinite Dimensional Spaces

Professional Activities

Reviewer: NIPS, NeurIPS, ICML, AISTATS, ICLR, IJCAI, ICPR, JMLR, IEEE TNNLS/TSP/SPL, IEICE, Neural Networks, Signal Processing.
Selected as a top reviewer at NeurIPS 2019 and ICML 2020. Selected as a TMLR expert reviewer in 2023.

Action Editor: TMLR (Jul. 2023-).

Editorial Board Reviewer: JMLR (Aug. 2020-).

Editorial Board: IEICE Trans. (Jun. 2023-).

Committee: IBISML (Jun. 2022-).

Program Committee: IBIS (2020).



Atsushi Nitanda is a Principal Scientist at A*STAR CFAR. Prior to his current position, he was an Associate Professor at the Kyushu Institute of Technology and an Assistant Professor at the University of Tokyo. Previously, he worked at NTT DATA Mathematical Systems Inc. (MSI) as a researcher. He obtained his Ph.D. in Information Science and Technology from the University of Tokyo in 2018. His research interests include stochastic optimization, mean-field optimization, statistical learning theory, kernel method, and deep learning. He received the Outstanding Paper Award at ICLR in 2021 and the Dean’s Awards for doctoral and master's theses from the University of Tokyo in 2019 and 2009.