Toshiaki Koike-Akino | Environmental Modeling | Best Researcher Award
Distinguished Research Scientist | Mitsubishi Electric Research Laboratories | United States
Toshiaki Koike-Akino is a distinguished research scientist at Mitsubishi Electric Research Laboratories whose extensive work bridges quantum computing, artificial intelligence, optical communication, and information theory. His research encompasses quantum machine learning, quantum algorithms, and quantum information systems, with applications extending to intelligent sensing, signal processing, and secure communication networks. He has made notable contributions to the development of low-power electronic design automation, FPGA systems, and mixed reality technologies such as virtual and augmented reality for robotic manipulation. In the domain of optical communications, his studies on coded modulation, equalization, and error correction codes have significantly enhanced high-speed data transmission efficiency and reliability. Koike-Akino’s pioneering efforts in photonic integrated circuits, nano-photonic devices, and meta-surface designs demonstrate his expertise in inverse design through deep learning and optimization algorithms. His work in optical sensing, including quantum and bio-sensing, has advanced methods for high-resolution imaging and tomography. Furthermore, his research in information theory and network coding has contributed to improving network capacity, secrecy, and cooperative communication systems. Across his career, Koike-Akino has authored more than 60 journal papers and over 230 conference publications, many of which are award-winning or invited works, reflecting a strong international reputation in interdisciplinary research that integrates quantum technology, AI, and photonics for next-generation communication and computing systems.
Fearuted Publications:
Koike-Akino, T., Popovski, P., & Tarokh, V. (2009). Optimized constellations for two-way wireless relaying with physical network coding. IEEE Journal on Selected Areas in Communications, 27(5), 773–787.
Tahersima, M. H., Kojima, K., Koike-Akino, T., Jha, D., Wang, B., & Lin, C. (2019). Deep neural network inverse design of integrated photonic power splitters. Scientific Reports, 9(1), 1368.
Kumar, A., Marks, T. K., Mou, W., Wang, Y., Jones, M., Cherian, A., Koike-Akino, T., Liu, X., & Feng, J. (2020). LUVLi face alignment: Estimating landmarks' location, uncertainty, and visibility likelihood. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 8236–8246).
Millar, D. S., Koike-Akino, T., Arık, S. Ö., Kojima, K., Parsons, K., Yoshida, T., Suzuki, M., & Sugihara, T. (2014). High-dimensional modulation for coherent optical communications systems. Optics Express, 22(7), 8798–8812.
Fehenberger, T., Millar, D. S., Koike-Akino, T., Kojima, K., & Parsons, K. (2019). Multiset-partition distribution matching. IEEE Transactions on Communications, 67(3), 1885–1893.
Millar, D. S., Maher, R., Lavery, D., Koike-Akino, T., Pajovic, M., Alvarado, A., & Bayvel, P. (2016). Design of a 1 Tb/s superchannel coherent receiver. Journal of Lightwave Technology, 34(6), 1453–1463.