جستجوی استاد راهنمای دانشگاهی

• Data science
• Machine learning
• Business analytics
• +۱۰ مورد دیگر

Masayuki Goto is a Professor in the Department of Industrial Systems Engineering, School of Creative Science and Engineering at Waseda University, Japan, where he has served since 2011. He earned his Doctor of Engineering from Waseda University and leads research integrating statistical science, machine learning, information theory and management engineering to solve business-analytics, marketing, AI ethics and industrial optimisation problems. Education: Doctor of Engineering, Waseda University Research Interests: His work spans data science, machine learning, business analytics, statistical learning theory, generative AI, deep neural networks, natural language processing, network analysis and information theory, with recent emphasis on trustworthy AI and synthetic data generation. Publication Trends: Over 2024-2025 his group has published extensively on deep learning for tabular data, vision-language models, recommender systems, causal inference and ethical AI, demonstrating a shift toward generative-AI-driven business analytics and interpretable models. Scientific Awards: Best Paper Award, CIE51 2024 Outstanding Paper Award, APIEMS 2023 Best Paper Award, APIEMS 2022 Best Paper Award, 20th ANQ Congress 2022 2022 PC Conference Best Paper Award Best Paper Award, JASMIN 2021 Best Paper Award, 19th ANQ Congress 2021 Encouragement Award, AAMSA 2021 IDR User Forum 2020 Enterprise & DBSJ Special Awards Best Paper Award, APIEMS 2019 Best Paper Award, ANQ Congress 2018 World CIST'18 Best Paper Award Best Paper Award, ANQ Congress 2017 JSPS Grant Review Commendation 2016 JIMA Distinguished Research Award 2015 Best Paper Award, Journal of JIMA 2015 IPSJ National Convention Best Paper Awards (2015 & 2012) Advising & Grants: He has mentored a large cohort of graduate students evidenced by co-authorship on over 100 recent papers. He has served as PI on numerous JSPS KAKENHI grants and industry projects focused on data-driven management, AI marketing and ethical AI frameworks. Labs & Teams: He heads the Goto Laboratory within the Waseda Institute for Advanced Study, leading interdisciplinary projects on business AI, data-ethics education and industrial optimisation.

• Statistical Science
• Quality Control
• Robust Parameter Design
• +۳ مورد دیگر

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 Sukoمشاهده پروفایل

دانشیار

• Bayesian statistics
• Statistical learning theory
• Business statistics
• +۳ مورد دیگر

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.

• Information Theory
• Coding Theory
• Statistical Learning Theory
• +۳ مورد دیگر

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.

• Data Science
• Artificial Intelligence
• Statistical Causal Inference
• +۳ مورد دیگر

Shohei SHIMIZU is a Professor at Osaka University 's Department of Reasoning for Intelligence and a Specially Appointed Professor at Shiga University 's Faculty of Data Science . With a career spanning multiple institutions and roles, he has been pivotal in advancing causal discovery methodologies. His research focuses on Statistical Causal Inference Machine Learning for Observational Data Reliability of AI Systems Industry-Academia Collaborations Recent work includes differentiable causal discovery and counterfactual explanations for complex AI models, emphasizing applications in biomedical research and financial analysis . Key trends in his publications show a strong emphasis on non-Gaussian models , hidden variables , and scalable algorithms , with recent integrations of large language models and open-source Python tools . Students and Collaborations Ph.D. students: Yuji Sakamoto, Kentaro Tagawa, Yi Jiang, Yoshimitsu Morinishi, Daigo Fujiwara Collaborations: RIKEN AIP Causal Inference Team, JST CREST Project, Tokyo Institute of Technology

• Applied mathematics
• Statistics
• Information theory
• +۸ مورد دیگر

Toshiyasu Matsushima is a Professor at Waseda University's Faculty of Science and Engineering, School of Fundamental Science and Engineering. He holds a Doctor of Engineering degree from Waseda University and has maintained an active research career spanning several decades. His educational background includes: 1991: Graduate School, Division of Science and Engineering, Waseda University 1978: Faculty of Science and Engineering, Waseda University Professor Matsushima's research spans multiple disciplines in applied mathematics and information science. His primary research interests include information theory, coding theory, statistical science, learning theory, intelligent informatics, data science, and information security. His work bridges theoretical foundations with practical applications in data compression, machine learning, privacy-preserving technologies, and communication systems. His recent publications demonstrate consistent innovation in Bayesian decision theory, context tree modeling, private information retrieval systems, decision tree algorithms, and image processing techniques. The research shows a clear trajectory toward optimizing algorithms for data analysis while addressing contemporary challenges in privacy-utility trade-offs and handling noisy data environments. Professor Matsushima is an active member of numerous professional societies including the Japanese Society for Industrial and Applied Mathematics, Applied Statistics Society, Operations Research Society of Japan, Quality Control Society, Artificial Intelligence Society, Information Processing Society, Information Theory and its Applications Society, IEEE, and the Institute of Electronics, Information and Communication Engineers. With over 190 publications and a Scopus h-index of 10, his scholarly contributions have established him as a significant figure in information theory and its applications. His research continues to influence both theoretical developments and practical implementations in data science and information engineering.

• Image coding
• Machine learning
• Data science
• +۳ مورد دیگر

Yuta Nakahara is an Assistant Professor (tenure-track) at Waseda University's Center for Data Science, where he has been working since 2019. His research focuses on the intersection of information theory, machine learning, and data science, with particular emphasis on Bayesian decision theory and its applications to image compression and decision tree modeling. Dr. Nakahara received his Doctorate from Waseda University's Graduate School of Fundamental Science and Engineering, Department of Pure and Applied Mathematics (2016-2019), following a Master's degree from the same institution (2014-2016) and a Bachelor's degree from Waseda University's School of Fundamental Science and Engineering, Department of Applied Mathematics (2010-2014). His research interests span image coding, machine learning, data science, lossless image compression, error correcting codes, and information theory. Nakahara's work particularly focuses on developing probabilistic models for image generation and compression, with an emphasis on Bayesian approaches that provide theoretical guarantees for optimal performance. His recent work extends these principles to decision tree models for improved uncertainty quantification and interpretability. Analysis of Nakahara's 15 most recent publications reveals a consistent focus on Bayesian methods for modeling hierarchical structures, particularly tree-based models. His work bridges theoretical information theory with practical machine learning applications, especially in image processing and decision systems. A significant thread throughout his research is the development of computationally efficient algorithms that maintain theoretical optimality. Top Reviewers of NeurIPS 2024 (8.6%, 1,304 of 15,160 reviewers) Dean's Award for Fundamental Science and Engineering, Grand Prize (2014) Dr. Nakahara leads the development of BayesML, an open-source Python library implementing Bayesian machine learning models with a unified API based on decision theory. His teaching portfolio includes numerous data science and statistics courses across Waseda University's Global Education Center, reflecting his commitment to data science education. His research is supported by multiple Waseda University Specific Research Grants focused on lossless image compression through probabilistic modeling, with projects running from 2019 to present.

• Educational Technology
• e-Learning
• Distance Education
• +۵ مورد دیگر

Shoji Nishimura is a Professor at the Faculty of Human Sciences, Waseda University, where he has served since 2006. Prior to his current position, he was an Associate Professor (2004-2006) and Assistant Professor (1997-1999) at the same institution. He holds a Doctor of Philosophy in Human Sciences from Osaka University and a Master of Science from Waseda University. Dr. Nishimura's educational background includes graduate studies in Pure and Applied Physics at Waseda University Graduate School and undergraduate studies in Mathematics at Waseda University Faculty of Science and Engineering. His academic journey reflects a strong foundation in quantitative disciplines that has informed his research in educational technology and data analysis. Dr. Nishimura's primary research interests focus on Educational Technology, Distance Education, and e-Learning . His work spans multiple domains including health informatics, biometrics, and human factors engineering. He has made significant contributions to the development of e-learning systems, particularly for tacit knowledge transfer and advanced skill acquisition. His research increasingly integrates health data analysis, wearable devices, and gait recognition technologies, demonstrating an interdisciplinary approach that bridges educational theory with practical applications in healthcare and safety. His recent publications (2020-2025) reveal a clear trend toward sophisticated health data analytics and biometric applications. The research shows increasing sophistication in causal analysis of health data, development of trustworthy decentralized health data systems, and applications of motion analysis for safety assessment. His work on gait recognition and driver behavior analysis demonstrates practical applications of his theoretical frameworks in real-world safety contexts. The shift from purely educational technology to health-focused applications represents a significant evolution in his research trajectory. Dr. Nishimura is actively involved in several major research projects including the development of e-learning systems for tacit knowledge transfer, research on broadcasting-telecommunications cooperation, and empirical studies on active learning behavior. His work has been supported by prestigious funding sources including Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research. He maintains active membership in numerous professional societies including The Institute of Electronics, Information and Communication Engineers, Information Processing Society of Japan, The Botanical Society of Japan, The Society of Population Ecology, Ecological Society of Japan, Japanese Society for Information and Systems in Education, and Japan Society for Educational Technology, reflecting the interdisciplinary nature of his work.

• Econometrics
• Applied Econometrics
• Regression Discontinuity Design
• +۵ مورد دیگر

Yoichi ARAI is an Associate Professor at the School of Social Sciences, Waseda University, specializing in econometrics and applied economic analysis. He holds a Ph.D. in Economics from the University of California, San Diego, and has established himself as a leading researcher in regression discontinuity designs, cointegration analysis, and program evaluation methodologies. Dr. ARAI's educational background includes a doctoral degree from UC San Diego, one of the world's leading institutions for econometric theory. His academic journey has positioned him at the intersection of theoretical econometrics and practical policy evaluation. Dr. ARAI's research focuses on econometrics and applied econometrics , with particular expertise in regression discontinuity designs, bandwidth selection methods, cointegration testing, and program evaluation. His work bridges theoretical developments with practical applications in labor economics, education economics, and monetary policy. His research has significantly advanced methodological approaches for causal inference in non-experimental settings, particularly through innovations in regression discontinuity frameworks and treatment effect estimation. His contributions have been instrumental in developing more robust statistical tests for identifying assumptions in fuzzy regression discontinuity designs and optimizing bandwidth selection procedures for both sharp and fuzzy regression discontinuity estimators. Analysis of Dr. ARAI's publication record reveals a strong trajectory of methodological innovation in econometrics, particularly in regression discontinuity designs and cointegration analysis. His recent work (2020-2025) focuses on high-dimensional extensions of regression discontinuity methods, optimal bandwidth selection, and testing identifying assumptions. Earlier work (2000-2015) established foundational contributions to cointegration testing with structural breaks and time series analysis. His research demonstrates consistent contributions to both theoretical econometrics and applied policy evaluation, with particular relevance to Japanese economic issues including educational upgrading of youth and labor market dynamics. Dr. ARAI has received recognition through substantial research funding, including multiple Grants-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (JSPS). His projects include "Development and Application of Policy Evaluation Methods Based on Dynamic Structural Models" (2024-2029), "Econometrics for Policy Evaluation and its Application" (2015-2019), and "Econometric Analysis of Program Evaluation and Its Application to Unemployment Insurance Policy" (2011-2015). As an educator, Dr. ARAI teaches advanced econometrics courses at both graduate and undergraduate levels, including "Research on Econometrics for Policy Evaluation," "Basic Data Analysis (Regression Analysis)," and "Applied Econometrics." His teaching emphasizes the practical application of econometric methods to real-world policy questions. He has supervised numerous research projects and contributes significantly to the academic community through committee memberships with the Japan Statistical Society, Japanese Economic Association, American Economic Association, and Econometric Society.

• Environmental economics
• Real Estate Economics
• Urban Economics
• +۲ مورد دیگر

Sho Kuroda is an Assistant Professor at Waseda University 's Faculty of Commerce (non-tenure-track) since October 2023, concurrently serving as a researcher at the Research Institute of Business Administration (RIBA). He earned his Ph.D. in Policy and Planning Sciences from University of Tsukuba in September 2023. His research focuses on applied econometrics , environmental economics , real estate , urban economics , and regional sciences . His work includes discrete-continuous choice models for carbon policy analysis, spatial econometric studies on automobile demand, and computational methods for energy-resilient communities. Recent publications (2021-2025) examine carbon dividend frameworks, transportation emission reductions, and hedonic models for urban view valuation. Articles demonstrate expertise in policy simulation , spatial modeling , and renewable energy integration . He teaches Introductory Econometrics , Basic Statistics , and Data Analysis Using Statistical Packages at Waseda University's Graduate Schools of Commerce and Accountancy. Concurrent roles include developer of educational materials for R/Stata-based statistical analysis and causal inference.

• Second Language Writing
• Language Assessment
• Study Abroad Effects
• +۳ مورد دیگر

Miyuki Sasaki is a Professor at Nagoya City University's Faculty of Education and Integrated Arts and Sciences, School of Education, specializing in second language writing, language assessment, and the effects of study abroad experiences. With a PhD in Applied Linguistics from UCLA and extensive research experience, she has made significant contributions to understanding L2 writing development and assessment. Educational Background: PhD in Applied Linguistics, University of California, Los Angeles MA in English Education, Hiroshima University BA in English Education, Hiroshima University Additional studies at Georgetown University in Teaching English as a Second Language Professor Sasaki's research focuses on the longitudinal development of second-language writing ability, the effects of raters' values and belief systems on composition evaluations, and sociocultural factors affecting L2 writers' motivation. Her work takes an ecological-historical approach to understanding multilingual writing development, examining how learners' experiences across different languages and contexts interact to shape their writing abilities. Recent research has increasingly incorporated technology, exploring AI tools like ChatGPT in writing assessment and the impact of machine translation on writing instruction. Her publication trends show a consistent focus on longitudinal studies of writing development, with increasing attention to technology-mediated language learning and assessment. The research demonstrates a progression from examining individual writing processes to investigating broader sociocultural and technological influences on L2 writing. Recent work has particularly emphasized the ecological context of writing development and the intersection of writing with other language skills. Scientific Awards: Special Grant for the Promotion of Research (2019) Nagoya City University Highest Ranked Paper Award (2018) Abe Fellowship for international research (2016) UCLA Nida Award for best qualifying paper (1990) Rotary International Foundation scholarship (1984) Ministry of Education scholarship for University of Michigan (1980) Professor Sasaki has served on numerous editorial boards including the Journal of Second Language Writing, Language Testing, and TESOL Quarterly. She has coordinated AAAL conference strands and served on TOEFL and TOEIC technical panels. Her research projects have been consistently funded by Japanese academic institutions, with current work examining third-language study abroad effects and multilingual writing development. She teaches courses in second language writing, applied linguistics, and English communication, mentoring the next generation of language education researchers.

• Microeconomics
• Econometrics
• Business Administration
• +۳ مورد دیگر

Daisuke Miyakawa serves as a Professor at the School of Commerce, Hitotsubashi University. His academic roles encompass teaching graduate and undergraduate courses in statistical causal inference, industry dynamics, and applied microeconometrics using big data. His research focuses on managerial succession , resource allocation efficiency , and firm growth patterns . Key investigations analyze causal effects of leadership transitions on corporate performance through large-scale Japanese firm panel data, revealing distinct growth trajectories after CEO changes—particularly noting optimal transitions between same-age cohort leaders versus disruptive outcomes in extreme age-gap successions. Miyakawa actively contributes to the Research Institute of Business Administration as a Concurrent Researcher. His current work involves calibrating occupational choice models with search frictions to quantify macroeconomic impacts of policy interventions in managerial succession.

Ryo Kobayashiمشاهده پروفایل

دانشیار

• Computational Materials Science
• Molecular Dynamics Simulation
• Solid State Ionics
• +۴ مورد دیگر

Ryo Kobayashi serves as Associate Professor in the Department of Applied Physics within the Graduate School of Engineering at Nagoya Institute of Technology. His research focuses on computational materials science with emphasis on molecular dynamics simulations for solid-state ionics and battery materials development. His work bridges fundamental atomic-scale phenomena with practical energy storage applications. Doctor of Science from Chiba University (2001) Assistant Professor at Nagoya Institute of Technology (2006-2023) Associate Professor at Nagoya Institute of Technology (2023-present) Kobayashi's research centers on computational materials design, particularly using molecular dynamics to investigate ion transport mechanisms in solid electrolytes, ferroelectric materials, and cementitious systems. His work develops high-throughput methods for force field parameterization and examines nanoscale phenomena in battery materials. Recent studies analyze grain boundary effects in ionic conductors, polarization switching in ferroelectrics, and dehydration processes in construction materials. His publication record shows strong focus on solid-state ionics and computational materials science, with significant contributions to understanding Li/Na-ion transport mechanisms in next-generation battery materials. Recent work combines machine learning potentials with traditional molecular dynamics to accelerate materials discovery. Award for Encouragement of Research in Materials Science (2011) Kobayashi leads multiple competitive research grants including MEXT Innovative Areas projects on local ionic conductivity analysis in solid electrolytes and reaction force field development for electrode interfaces. His teaching includes Solid State Physics II, Quantum Mechanics Seminar, and Applied Physics Experiments. He actively contributes to industry through workshops on materials informatics for corporate researchers and educational outreach to high school students on nanoscale computer simulations. His laboratory maintains active collaborations with national research institutions and develops computational tools like the fitpot program for force field optimization and pmd for molecular dynamics simulations.