Takeshi Ikenaga is a Professor at Waseda University’s School of Fundamental Science and Engineering and Graduate School of Information, Production and Systems . He earned his Ph.D. in Information & Computer Science from Waseda University in 2001, following B.E. and M.E. degrees in Electrical Engineering (1988–1990). His career spans roles at NTT LSI Laboratories (1990–2002), Kitakyushu Foundation for Advancement of Industry, Science and Technology (FAIS) (1999–2002), and visiting researcher at the University of Massachusetts (1999–2000). Research Interests : Application-specific SoCs for video/image processing, including compression (H.264/AVC, H.265/HEVC), filters (super-resolution, noise reduction), recognition systems (feature detection, object tracking), and communication (UWB, LDPC). He also works on many-core processor design, ultra-low-delay vision systems, and sports analytics (volleyball, figure skating) with real-time 3D pose estimation and ball tracking. Awards : Recipient of the Furukawa Sansui Award (Waseda University, 1988) IEICE Research Encouragement Award (1992) Multiple Best Paper/Presentation Awards (2006–2022) at conferences including DAC/ISSCC, LSI IP Design, ISOCC, ISPACS, and CVIT APSIPA Distinguished Lecturer Certificate (2015) Waseda University Presidential Teaching Award (2020)
Kei Hirose serves as Professor and Division Leader of the Division of Industrial and Mathematical Statistics, with concurrent appointments in the Division of Strategic Liaison and Division of Fujitsu Mathematical Modeling for Decision Making. Division Leader: Industrial and Mathematical Statistics Concurrent Roles: Strategic Liaison, Fujitsu Mathematical Modeling for Decision Making His research pioneers sparse estimation techniques for high-dimensional data analysis, focusing on multivariate methods including factor analysis and Gaussian graphical modeling. He develops computationally efficient algorithms for parameter estimation while investigating theoretical properties of sparse models, with implementations distributed via R packages. Applications prominently include genomic data analysis such as gene expression datasets, bridging statistical theory with practical computational biology challenges. Scientific recognition: No awards or fellowships specified in source material Academic mentorship and funding details were not documented in the provided text. Similarly, laboratory infrastructure, research teams, and future research trajectories remain unspecified in the available information.
Satoru Hayamizu is a Professor at Waseda University 's Green Computing Systems Research Organization , with a career spanning over four decades. His research focuses on Audio-Visual Speech Recognition , Machine Learning , and Medical Informatics , as evidenced by 126 publications and an h-index of 18. Education: The University of Tokyo (PhD in Mechanical Engineering) Prior affiliations: Gifu University (2002-), National Institute of Advanced Industrial Science and Technology (1981-2001) Research Interests include: Audio-visual speech recognition with sparse representation and DNN techniques Development of low-cost CNN-based road condition detection systems Swallowing function evaluation using acoustic and image processing Human behavior analysis for service operation estimation Research Trends reveal consistent work in multimodal signal processing (2006-2024), deep learning applications (2012-2024), and medical diagnostic systems (2006-2017). His publications show integration of sparsity modeling (2012-2021), industrial equipment diagnostics (2018-2021), and social impact technologies (2013-2024). Research Projects funded by Japan Society for the Promotion of Science include: Swallowing timing estimation (2018-2021) Multimodal silent speech recognition (2016-2020) ICT-based piano learning systems (2013-2016) Keyword display mechanisms (2010-2012) Labs & Collaborations include partnerships with Satoshi Tamura (co-author on 12+ papers), Hidekazu Fukai , and Chiyomi Miyajima . His work bridges academic research and industrial applications , particularly in manufacturing AI (2024 book) and Timor-Leste infrastructure monitoring.
Dr. Masato Inoue is a Professor at the Faculty of Science and Engineering , School of Advanced Science and Engineering at Waseda University. He holds a Doctor of Medical Science from Kyoto University. Education: 2003 - Kyoto University Graduate School of Medicine 2003 - Kyoto University His research spans multiple disciplines at the intersection of Medical Informatics , Bioinformatics , and Statistical Mechanics . Key areas include: Medical Imaging : Developing Bayesian super-resolution algorithms and Prior Ensemble Learning for improved MRI reconstruction Voice Analysis : Creating innovative voice quality quantification systems for clinical diagnostics Genetic Analysis : Advancing haplotype inference methods and gene network modeling Signal Processing : Applying statistical mechanics to diverse problems from coding theory to neuroscience His recent publications (2021-2012) demonstrate consistent contributions to medical imaging algorithms , voice disorder classification , and genetic data analysis . Notable collaborations include work with Kyoto University researchers , Swedish medical institutions , and cross-disciplinary teams in bioengineering.
Yasushi Nagata is a Professor at Waseda University's School of Creative Science and Engineering. With a Doctor of Engineering from Osaka University, he specializes in statistical quality control, Taguchi methods, and multivariate analysis. Key affiliations: Japan Society for Quality Control Japan Statistical Society Japan Behaviormetrics Society Research Interests: His work focuses on advancing Taguchi's robust parameter design, Mahalanobis-Taguchi systems, and statistical anomaly detection. He has developed novel methods for handling missing data, high-dimensional datasets, and non-normal distributions in quality control contexts. Scientific Awards: He has received multiple Best Paper Awards at ANQ Congresses (2017-2024) Deming Prize for Individuals (2019) Waseda University Teaching Awards (2018, 2022) Nikkei Quality Control Literature Prizes Publications: His recent studies examine non-stationary extreme precipitation data, robust parameter design for multilevel systems, and EM-λ algorithm integration with MT methods. These works bridge theoretical statistics with practical engineering applications.
Tota Suko is an Associate Professor at the School of Social Sciences, Waseda University, specializing in statistical learning theory and business analytics. With a Ph.D. in Engineering from Waseda University, Dr. Suko leads the Suko Seminar (Management Science Seminar) where students learn to solve business problems using mathematical and management science approaches, primarily through data science techniques including statistical analysis and machine learning. Dr. Suko's research spans multiple domains in statistics and data science. His primary interests include Bayesian statistics, statistical learning theory, business statistics, data mining, and information theory. He has developed methods for analyzing survey data with selection bias, detecting poor responses in questionnaires, and parameter estimation in regression models. His work bridges theoretical statistics with practical applications in business analytics, e-commerce, and even nanotechnology through collaborations with physics researchers. Dr. Suko's recent publications demonstrate a strong trend toward practical applications of statistical methods. His work on generative AI for criminal case law analysis shows innovative application of AI in legal domains, while his research on questionnaire quality control addresses fundamental issues in survey methodology. He has also made significant contributions to theoretical aspects of statistical learning, particularly in the areas of label noise, selection bias, and parameter estimation under non-ideal data conditions. Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research projects Waseda Data Science Consortium industry-academia collaborations Research on nanoscale semiconductor prediction models Development of methods for low-quality data analysis Dr. Suko actively supervises both undergraduate and graduate students through the Suko Seminar. Students work on individual or team projects, participate in data analysis competitions, and present their research at academic conferences. He has developed educational approaches including full-on-demand content for data science education and modularized online statistical teaching materials. Dr. Suko leads the Suko Seminar (Management Science Seminar), which actively collaborates with companies and research institutions. His research team works on diverse projects including analysis of purchasing and browsing histories on e-commerce sites, prediction modeling for nanoscale conduction, and development of methods for low-quality data analysis. The seminar emphasizes both theoretical understanding and practical application of data science techniques to solve real-world business problems.
Shunsuke Horii is an Associate Professor at the Center for Data Science, Waseda University. His research spans information theory, coding theory, statistical learning theory, and data science applications. He actively collaborates with industry through initiatives like the Waseda Data Science Consortium. Education: Ph.D. in Science and Engineering from Waseda University (2009), Master's from Waseda University Graduate School of Science and Engineering (2004). Research Focus: Addresses causal effect estimation in data science using Bayesian decision theory, sparse modeling, and optimization techniques like ADMM and variational inference. Develops efficient algorithms for multiuser communication, matrix completion, and privacy-preserving distributed computing. Teaching: Instructs courses on statistics literacy, data science, and programming with Python/R across multiple academic quarters. Grants: Leads projects funded by Japan Society for the Promotion of Science, including causal inference frameworks, product recommendation systems, and business analytics. Publications: 21 papers with 61 Scopus citations, focusing on LP decoding, Bayesian hierarchical models, and statistical causal analysis.
Shunichi Nomura is an Associate Professor at the Faculty of Commerce, Graduate School of Accountancy, with a PhD in Statistical Science from The Graduate University for Advanced Studies (Sokendai). His research bridges statistical methodology with applications in earthquake forecasting and actuarial science. Education: PhD in Statistical Science (Sokendai). He develops Bayesian space-time models for earthquake recurrence prediction and applies data mining techniques to insurance risk and healthcare data. His work integrates mathematical statistics with seismological observations to improve probabilistic hazard assessments. Recent publications focus on nonstationary renewal processes, slip rate inversion, and sparse estimation methods like group fused lasso. These studies span geophysics, actuarial mathematics, and machine learning applications. Scientific Awards: Actuarial Excellent Paper, The Japanese Actuarial Society (2017). He leads JSPS-funded research projects on spatiotemporal earthquake modeling and Bayesian prediction frameworks. His teaching includes advanced statistics and data science courses for actuarial students.
Yasuhiro Oikawa is a Professor at the School of Fundamental Science and Engineering, Faculty of Science and Engineering, Waseda University. He holds a Doctor of Engineering degree from Waseda University and has been actively contributing to acoustics, signal processing, and optical measurement techniques. His research spans Sound field visualization using parallel phase-shifting interferometry Phase-aware audio signal processing algorithms Acoustic calibration and microphone sensitivity analysis Real-time sound event localization systems His recent publications focus on advanced time-frequency analysis, optical methods for sound measurement, and deep learning applications in acoustics. Key trends include Improving resolution in spectrogram-based signal processing Integration of physical models with neural networks Development of wearable acoustic sensor arrays Scientific awards recognizing his work include The Fumio Okano Best 3D Paper Awards CSS2018 Best Paper Award Acoustical Society of Japan Contribution Award Institute of Electronics, Information and Communication Engineers Human Communication Award He has served as a committee member for the Acoustical Society of Japan and is affiliated with organizations such as ACM, IEEE, and Acoustical Society of America. His research has been supported by collaborations with institutions like Technical University of Denmark and applications in robotics, museum exhibits, and consumer electronics.
Nakahiro Yoshida is a Professor at the Graduate School of Mathematical Sciences, University of Tokyo, specializing in Theoretical Statistics and Probability Theory. He leads the YUIMA Project, developing computational frameworks for stochastic differential equations. His affiliations include memberships in the Mathematical Society of Japan, Japan Statistical Society, International Statistical Institute, and Bernoulli Society, with editorial roles for several academic journals. Research Interests: His work spans asymptotic expansion theory, limit theorems, and statistical inference for stochastic processes, with applications in finance and insurance mathematics. Key areas include: Martingale expansions and Malliavin calculus Non-synchronous covariance estimation for high-frequency data Quasi-likelihood analysis for jump diffusions Stochastic numerical methods and computational statistics Statistical learning theory for econometrics and biostatistics Publication analysis reveals consistent focus on asymptotic expansion techniques for Wiener functionals and martingales (2023), adaptive estimation methods (2021), financial applications of stochastic processes (2017), and foundational work on quasi-likelihood analysis (2011-2013). Awards & Honors: Mathematical Society of Japan Autumn Prize (2024) Hiroshi Fujiwara Prize for Mathematical Sciences (2019) Japan Statistical Society Award (2009) Research Achievement Award, Japan Statistical Society (2007) Analysis Prize, Mathematical Society of Japan (2006) He leads the YUIMA Project Team, developing open-source software for stochastic differential equation analysis, with applications in quantitative finance and computational statistics.
HONTANI Hidekata is a Professor at Nagoya Institute of Technology's Department of Information Engineering, Media Informatics Field, and Graduate School of Engineering, Media Informatics Program. He is also affiliated with the Advanced Medical Physics and IT Research Center and the Center for Research and Development in Higher Engineering-Education. His career includes academic roles at institutions like Yamagata University and The University of Tokyo. Doctorate in Engineering from The University of Tokyo (2000) Professional memberships in IEEE, SICE, IEICE, and IPSJ Research focuses on Medical Image Processing and Computational Anatomy , with recent advancements in deep learning applications for pathology image analysis, TMS electromagnetic modeling, and spatiotemporal cancer dynamics. He pioneered techniques for counterfactual image generation in lymphoma pathology and adaptive sparse regularization for signal processing. Notable trends in his publications (2017-2024) include AI-driven histopathology , generative models for medical imaging, and tumor microenvironment analysis using multi-scale MRI-pathology fusion. His work bridges machine learning , computational anatomy , and clinical applications . Scientific Awards : Multiple Japan Society of Medical Imaging and Information Sciences awards (2017-2024), Cum Laude Poster Award at SPIE Medical Imaging (2018) Grants : Principal Investigator for JSPS KAKENHI projects on lymphoma subtyping (2022-2025), 3D tumor modeling (2018-2021), and computational anatomy (2014-2019) He leads the Advanced Medical Physics and IT Research Center and contributes to academic societies as a committee member in organizations including IEICE and Japan Society of Medical Imaging and Information Sciences.
