Hayakawa Ryo:Introduction of our tenure-track faculties|TUAT's tenure-track program

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Hayakawa Ryo

Affiliation	Institute of Engineering
Division	Division of Advanced Information Technology and Computer Science
Research field	Signal Processing
Keyword(S)	Mathematical optimization, Image processing, wireless signal processing
Url	https://web.tuat.ac.jp/~hayakawa-lab/

Research experience	・Apr. 2017-Mar. 2020: JSPS Research Fellow (DC1) ・Apr. 2020-Sep. 2023: Assistant Professor, Graduate School of Engineering Science, Osaka University ・Oct. 2023-Present : Associate Professor, Institute of Engineering, Tokyo University of Agriculture and Technology
Educational background	・Mar. 2015: Bachelor of Engineering, Kyoto University ・Mar. 2017: Master of Informatics, Kyoto University ・Mar. 2020: Doctor of Informatics, Kyoto University
Awards	* The latest information is shown at the member's website. ・Feb. 2020: The 16th IEEE Kansai Section Student Paper Award ・Nov. 2019: APSIPA ASC 2019 Best Special Session Paper Nomination Award ・Mar. 2018: The 33rd Telecom System Technology Student Award
Selected papers and publications	* The latest information is shown at the member's website. ・R. Hayakawa, “Asymptotic performance prediction for ADMM-based compressed sensing,” IEEE Transactions on Signal Processing, vol. 70, pp. 5194-5207, 2022. ・R. Hayakawa and K. Hayashi, “Asymptotic performance of discrete-valued vector reconstruction via box-constrained optimization with sum of L1 regularizers,” IEEE Transactions on Signal Processing, vol. 68, pp. 4320-4335, Aug. 2020. ・S. Takabe, M. Imanishi, T. Wadayama, R. Hayakawa, and K. Hayashi, “Trainable projected gradient detector for massive overloaded MIMO channels: Data-driven tuning approach,” IEEE Access, vol. 7, pp. 93326-93338, Jul. 2019. ・R. Hayakawa and K. Hayashi, “Performance analysis of discrete-valued vector reconstruction based on box-constrained sum of L1 regularizers,” in Proceedings of the 44th IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP 2019), Brighton, UK, May 2019. ・R. Hayakawa and K. Hayashi, “Reconstruction of complex discrete-valued vector via convex optimization with sparse regularizers,” IEEE Access, vol. 6, pp. 66499-66512, Dec. 2018. ・R. Hayakawa and K. Hayashi, “Discreteness-aware approximate message passing for discrete-valued vector reconstruction,” IEEE Transactions on Signal Processing, vol. 66, no. 24, pp. 6443-6457, Dec. 2018. ・R. Hayakawa and K. Hayashi, "Discreteness-aware decoding for overloaded non-orthogonal STBCs via convex optimization," IEEE Communications Letters, vol. 22, no. 10, pp. 2080-2083, Oct. 2018. ・R. Hayakawa, A. Nakai, and K. Hayashi, “Distributed approximate message passing with summation propagation,” in Proceedings of the 43rd IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP 2018), Calgary, Canada, Apr. 2018. ・R. Hayakawa and K. Hayashi, “Convex optimization-based signal detection for massive overloaded MIMO systems,” IEEE Transactions on Wireless Communications, vol. 16, no. 11, pp. 7080-7091, Nov. 2017.

Research Description

Signal processing is a technology used to manipulate signals like sound, images, and radio waves for the conversion, extraction, and interpretation of information. Since signals in various applications often have unique structures, it's necessary to utilize the properties of the signals being processed.
We conduct research on the mathematics and applications of signal processing. Our main focus includes image processing and wireless signal processing, developing methods to restore signals such as sparse signals, discrete-valued signals, and images from observational data. We are also interested in the theoretical evaluation of performance improvements through the use of signal properties, including analysis on the accuracy and conditions for complete reconstruction of signal recovery methods. Our mathematical tools primarily include techniques known as mathematical optimization and probabilistic inference. Recently, we have been studying the integration of mathematical optimization and machine learning techniques. Our goal is to build foundational technologies that enable us to easily design excellent signal processing algorithms with interpretability and stability for each application.

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About TUAT's tenure-track program

I think it's a wonderful system that allows young researchers to independently lead their own laboratories from an early stage. It's extremely helpful to receive startup funding to set up the laboratory infrastructure. The consideration given to reducing the burden of teaching and administration also creates an environment conducive to focusing on research.

Future aspirations

In addition to advancing my own research, I am committed to education and research to develop talents who can contribute to society through our laboratory. To broaden the scope of our research, I am eager to actively engage in collaborations with individuals both within and outside our university.