معرفی
Hiroyuki Kasai is a Full Professor at the School of Fundamental Science and Engineering, Waseda University, where he leads research in signal processing, machine learning, and optimization. He holds a B.Eng. (1996), M.Eng. (1998), and Dr.Eng. (2000) in Electronics, Information, and Communication Engineering from Waseda University. His career includes positions as Associate Professor and Professor at the University of Electro-Communications (2007-2019), Senior Policy Researcher at Japan's Cabinet Office (2011-2013), and visiting roles at Technical University of Munich and British Telecom.
His research spans:
- Fundamental methodologies: Riemannian optimization, stochastic gradient algorithms, tensor decomposition
- Applied domains: Network analysis, multimedia systems, environmental sound processing, video coding
- Emerging areas: Low-rank modeling, manifold learning, and large-scale anomaly detection
His publications focus on efficient algorithms for high-dimensional data, with recent work emphasizing Riemannian manifold optimization and real-time tensor analysis. This includes development of open-source tools like SGDLibrary (MATLAB) and McTorch (PyTorch) for optimization tasks.
Awards include:
- IEEE ICCE Best Paper Award (2011)
- Yamashita Memorial Award (2003)
- Ericsson Young Scientist Award (2001)
- 電気通信普及財団賞 (2015)
- IEICE Service Recognition Award (2010)
He maintains memberships in IEEE, IEICE, IPSJ, and JSIAM, and has contributed to over 100 peer-reviewed publications with significant citation impact (h-index 27 via Google Scholar).