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Hiroyuki KASAI

Dr. Eng., Professor (full), WASEDA University, Tokyo, Japan
Department of communications and computer engineering,
(Graduate) school of fundamental science and engineering

Hiroyuki KASAI (HK) is a Professor at Faculty of Science and Engineering, WASEDA University, Tokyo, Japan. He received his B.Eng., M.Eng., and Dr.Eng. degrees in Electronics, Information, and Communication Engineering from WASEDA University in 1996, 1998, and 2000, respectively. He was a research associate at Global Information and Telecommunication Institute (GITI), WASEDA University, during 1998-2002. He was a visiting researcher at British Telecommunication BTexacT Technologies, the U.K., during 2000-2001. He joined Network Laboratories, NTT DoCoMo, Japan, in 2002. He was an associate professor at The University of Electro-Communications (UEC), Tokyo, during 2007-2019, and he was appointed as a professor at UEC in 2019. He was a senior policy researcher at Council for Science, Technology and Innovation Policy (CSTP), Cabinet Office of Japan, during 2011-2013. He was a visiting researcher at Technical University of Munich (TUM), Germany, during 2014-2015. Since September 2019, he has been in his current position.

笠井 裕之

早稲田大学 理工学術院 教授
早稲田大学 基幹理工学部 情報通信学科
早稲田大学 大学院基幹理工学研究科 情報理工・情報通信専攻

2000年〜2001年英国・ブリティッシュテレコム研究所 BTexacT Technologies・訪問研究員
2002年〜2007年株式会社 NTT ドコモ・ネットワーク研究所・研究員



Research interests

My research interests generally include optimization, machine learning and learning-based signal processing with those applications in communication & network systems, image & video processing, and other data analysis fields. Specifically, I am interested in learning and optimization for large-scale structured data and parameters, e.g., non-linear optimization algorithms on Riemannian manifolds, and its applications.


Selected publications

  1. HK, P.Jawanpuria, and B.Mishra, “Riemannian adaptive stochastic gradient algorithms on matrix manifolds,” ICML, 2019.
  2. HK and B.Mishra, “Inexact trust-region algorithm on Riemannian manifolds,” NeurIPS (formerly NIPS), 2018.
  3. HK, H.Sato, and B.Mishra, “Riemannian stochastic recursive gradient algorithm,” ICML, 2018.
  4. HK and B.Misrha, “Low-rank tensor completion: a Riemannian manifold preconditioning approach,” ICML, 2016.
  5. HK, “Fast online low-rank tensor subspace tracking by CP decomposition using recursive least squares from incomplete observations,” Neurocomputing, 2019.
  6. HK, W.Kellerer, and M.Kleinsteuber, “Network volume anomaly detection and identification in large-scale networks based on online time-structured traffic tensor tracking,” IEEE Transactions on Network and Service Management, 2016.

Contact me

Email: hiroyuki.kasai at waseda.jp (Please replace the word “at” with “@”.)