The chart shows the distribution of publications by all Research.com ranked scientists in the field of Environmental Sciences in 2026. The highlighted bar marks where Tatsuya Yokota sits on this spectrum.
This scientist: 254 publications — 78th percentile
78% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 690 publications or more.
The chart shows the D-index (discipline H-index) distribution of Environmental Sciences scientists ranked by Research.com in 2026. The highlighted bar marks where Tatsuya Yokota sits on this spectrum.
This scientist: 40 D-Index — 19th percentile
19% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 126 D-Index or more.
Tatsuya Yokota is affiliated with the National Institute for Environmental Studies in Japan. Their research spans multiple interdisciplinary fields including Engineering, Computer Science, and Medicine, with a focus on areas such as Computer Vision and Pattern Recognition, Computational Mathematics, Computational Mechanics, Radiology, Nuclear Medicine and Imaging, and Biomedical Engineering.
The main topics Yokota has explored include tensor decomposition and applications, image and signal denoising methods, sparse and compressive sensing techniques, artificial intelligence in cancer detection, advanced neuroimaging techniques and applications, traffic prediction and management techniques, and advanced image processing techniques.
Yokota has contributed to several publications across respected venues. Notable papers include:
Their research outputs are also frequently published in forums such as arXiv (Cornell University), the Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), and IEEE journals and conferences.
Yokota collaborates regularly with several co-authors, notably Hidekata Hontani, Andrzej Cichocki, Qibin Zhao, Qiquan Shi, and Hiromu Takayama, reflecting a network that spans expertise in signal processing, tensor methods, and machine learning applications.
Christian Frankenberg;Joshua B. Fisher;John Worden;Grayson Badgley
T. Yokota;Y. Yoshida;Nawo Eguchi;Y. Ota
Y. Yoshida;Y. Ota;Y. Ota;N. Eguchi;N. Kikuchi
Y. Yoshida;N. Kikuchi;I. Morino;O. Uchino
I. Morino;O. Uchino;M. Inoue;Y. Yoshida
M Buchwitz;Markus Reuter;O Schneising;Hartmut Boesch
S. Maksyutov;H. Takagi;V. K. Valsala;V. K. Valsala;M. Saito
Alexander A. Kokhanovsky;Vladimir P. Budak;Celine Cornet;Minzheng Duan
Yoshifumi Ota;Akiko Higurashi;Teruyuki Nakajima;Tatsuya Yokota
S. Guerlet;S. Guerlet;A. Butz;D. Schepers;S. Basu
Yasuhiro Sasano;Makoto Suzuki;Tatsuya Yokota;Hiroshi Kanzawa
Frédéric Chevallier;Shamil Maksyutov;Philippe Bousquet;François-Marie Bréon
Sergey Oshchepkov;Andrey Bril;Tatsuya Yokota;Isamu Morino
Rajesh Janardanan;Shamil Maksyutov;Tomohiro Oda;Tomohiro Oda;Makoto Saito
A.V. Nikitin;O.M. Lyulin;S.N. Mikhailenko;V.I. Perevalov
H. Takagi;T. Saeki;T. Oda;M. Saito
M. Inoue;I. Morino;O. Uchino;Y. Miyamoto
M. Reuter;H. Bösch;H. Bovensmann;A. Bril
T. E. Taylor;C. W. O'Dell;D. M. O'Brien;N. Kikuchi
Sergey Oshchepkov;Andrey Bril;Tatsuya Yokota;Paul O. Wennberg
T. Yokota;H. Nakajima;T. Sugita;H. Tsubaki
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