جستجوی استاد راهنمای دانشگاهی

Tetsuya Sakai is a Professor at the School of Fundamental Science and Engineering within Waseda University's Faculty of Science and Engineering. His work focuses on information access, retrieval, and natural language processing, with a particular emphasis on evaluation frameworks for search systems. Affiliations: Waseda University (Faculty of Science and Engineering, School of Fundamental Science and Engineering) Academic Rank: Professor Research Interests : Dr. Sakai's research spans four key areas: (1) Information Access —designing systems for direct and immediate information delivery, (2) Search Evaluation —developing metrics like Height-Biased Gain and hierarchical intent-based diversity measures, (3) Fairness in IR —pioneering frameworks for group fairness in conversational search, and (4) Statistical Reform —advocating Bayesian methods and robust experimental design. His work also addresses privacy inconsistencies in mobile apps and cognitive biases in LLMs. Scientific Awards : Notable recognitions include induction into the SIGIR Academy (2023) , ACM Distinguished Member (2018) , ACM Senior Member (2016) , and multiple DEIM/FIT/CSS Best Paper Awards . He has received teaching honors like the Waseda Presidential Teaching Award (2016) and WASEDA e-Teaching Award (2018) . Article Trends : Recent publications highlight: Advancements in LLM-assisted relevance assessments and hallucination diagnostics for tool-augmented models Conversational search fairness through multi-level evaluation frameworks and group diversity metrics Innovations in 3D medical reconstruction from clinical data and multimodal uncertainty modeling Statistical rigor via randomization tests , credible intervals , and topic set design Privacy analysis in mobile app descriptions and cognitive bias studies in search interaction

• Information Access
• Information Retrieval
• Natural Language Processing
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Takuya Taniguchi is an Associate Professor at the Center for Data Science, Waseda University, where he has been employed since 2019, first as an Assistant Professor (2019-2021) before promotion to his current non-tenure track position. His interdisciplinary research bridges organic chemistry, materials science, and data science, with a focus on developing and analyzing functional organic materials, particularly photo-responsive molecular crystals. He maintains active collaborations with research groups including Hideko Koshima and Toru Asahi, and serves on professional committees such as the Manufacturing Industry AI Promotion Association. Education Doctor of Engineering, Waseda University (2019) Graduate studies at Waseda University's Graduate School of Advanced Science and Engineering Undergraduate studies at Waseda University's School of Advanced Science and Engineering, Department of Life Science and Medical Bioscience Research Focus Professor Taniguchi's work centers on structural organic chemistry and physical organic chemistry , with particular emphasis on organic functional materials and the application of materials informatics to crystal engineering. His research investigates how molecular structure influences macroscopic mechanical properties, especially photo-mechanical behaviors in organic crystals. He has pioneered the use of machine learning techniques to predict crystal properties, optimize materials performance, and design novel functional materials with applications in soft robotics and energy conversion. His recent work demonstrates a clear progression from fundamental studies of photo-mechanical crystals toward increasingly sophisticated applications of machine learning in materials science. The 15 most recent publications reveal a strong focus on neural network potentials for crystal property prediction, optimization of photo-actuated materials, and exploration of phase transitions in molecular crystals. This research trajectory shows consistent innovation in combining experimental chemistry with advanced computational methods. Awards and Recognition Inoue Research Encouragement Prize (2019) Ono Azusa Memorial Award from Waseda University (2019) Student Presentation Award at Japan Chemical Society 99th Spring Annual Meeting (2019) Poster Presentation Award at Chirality 2017 Multiple poster and presentation awards at international crystallography conferences Research Leadership Dr. Taniguchi leads multiple research projects funded by JSPS, JST, and industry partners including ENEOS Corporation and Sumitomo Foundation. His current projects include 'Efficient crystal structure prediction for organic porous materials,' 'Machine learning simulation of HOF material stability,' and 'Machine learning application for organic solid-phase transition.' He has successfully secured competitive funding including JST ACT-X and JSPS Early-Career Scientist grants. His work has received significant media attention, with coverage in Nikkan Kogyo Shimbun and Kagaku Kogyo Nippo highlighting breakthroughs in light-driven organic crystals with 3.7x improved output force. Academic Contributions Beyond research, Professor Taniguchi teaches extensively in Waseda's Global Education Center, offering courses in data science, statistical analysis with R, and statistics literacy across multiple academic quarters. He has contributed chapters to technical publications on process informatics, Bayesian optimization, and crystallization process design. His scholarly impact is evidenced by an h-index of 11 (Google Scholar) with 581 total citations, demonstrating significant influence in the emerging field of materials informatics for organic crystals.

• Organic Functional Materials
• Materials Informatics
• Structural Organic Chemistry
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Hideyuki Suzuki is a Professor at the Department of Information and Physical Sciences , Graduate School of Information Science and Technology , Osaka University , where he has been employed since April 2016. His research spans nonlinear dynamics , hybrid systems , and many-body dynamics , with applications to power systems , brain modeling , and epidemic networks . 2001 : Ph.D. in Mathematical Engineering and Information Physics, University of Tokyo 1998 : M.Eng. in Mathematical Engineering and Information Physics, University of Tokyo 1996 : B.Sc. in Mathematics, University of Tokyo His research interests focus on nonlinear dynamical systems , particularly those with discontinuities or large-scale interactions , such as chaotic billiards , hybrid systems , and spatio-temporal chaos . He explores computational applications in machine learning (e.g., chaotic Boltzmann machines ), epidemiology (e.g., vaccine allocation models ), and power grid stability . The 15 most recent articles highlight his work in photonic computing , nonlinear sampling algorithms , and chaotic dynamics in engineering and biology . Key trends include interdisciplinary applications of nonlinear mathematics to renewable energy , neuroscience , and epidemic spread . Scientific awards include: 2022 Osaka University Prize 2018 JSIAM Best Paper Award for Hamiltonian Monte Carlo (2017) He leads the Nonlinear Mathematics Course laboratory, which accepts graduate and undergraduate students. His team investigates hybrid dynamical systems , chaotic computation , and real-world modeling in fields like traffic dynamics and epidemic networks . Research is supported by grants from JST CREST and ALCA-Next programs.

• Nonlinear Dynamics
• Hybrid Systems
• Many-Body Dynamics
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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.

• Medical Informatics
• Statistical Science
• Bioinformatics
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Hajime Katayama is a Professor at Waseda University's School of Commerce since 2017, where he teaches advanced econometric methods and applied microeconomic analysis. He previously held academic positions at Waseda University (2010-2017) and the University of Sydney (2003-2010). Education : Ph.D. in Economics from Pennsylvania State University , MA & BA in Sociology from Rikkyo University Research Focus : Environmental economics, applied econometrics, household energy behavior, and corporate environmental policies His research bridges empirical methods with policy implications, particularly in environmental economics . Key contributions include analyzing cookstove choices in India , spillover effects of corporate scandals , and energy-saving behavioral patterns . He has received multiple JSPS research grants for projects on market dynamics , energy behavior , and corporate governance . Recent collaborative work examines social norms and pro-environmental practices across households and organizations. Teaching Activities include graduate-level courses on applied microeconometrics and discrete variable analysis . He also teaches undergraduate courses in intermediate regression analysis and econometrics at Waseda University's School of Commerce.

• Environmental Economics
• Energy Economics
• Applied Econometrics
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Manabu Kobayashi is a Professor at the Center for Data Science of Waseda University, with previous faculty roles at Shonan Institute of Technology (1998-2014). His research spans data mining, machine learning, and information theory, with a focus on applications in document classification, ECOC frameworks, and latent structural models. Current Affiliation: Waseda University (2018-present) Previous: Shonan Institute of Technology (2002-2014) Research Interests: Specializes in distance metric learning, latent class modeling, and fault diagnosis systems using probabilistic frameworks. Key areas include: ECOC (Error-Correcting Output Codes) optimization Collaborative filtering for recommendation systems Reconfigurable logic circuits using DG-CNTFETs Privacy-preserving distributed linear regression Scientific Awards: PC Conference Best Paper Award (2025) World CIST'18 Best Paper Award IEICE Achievement Award (2017) 74th Information Processing Society of Japan Conference Excellence Award (2012) Article Trends: Recent work focuses on latent structure analysis for relational data (2025), ECOC performance with noisy classifiers (2023), and flipped classroom effectiveness through log-based grouping (2022-2025). Earlier contributions include reconfigurable logic circuits (2013-2014) and probabilistic fault diagnosis (2011-2012).

• Data Mining
• Machine Learning
• Information Theory
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Yu Hayakawa is a Professor at Waseda University since 2007, affiliated with the School of International Liberal Studies and the Faculty of International Research and Education. He holds a PhD in Operations Research from the University of California at Berkeley, an MSc in Mathematics from Illinois State University, and a BA in Elementary School Education from Hiroshima University. His career includes roles at Victoria University of Wellington (1992-2004) and Waseda University. Doctor of Philosophy (1986-1992), University of California at Berkeley, College of Engineering, Operations Research Master of Science (1983-1986), Illinois State University, Department of Mathematics Bachelor of Education (1978-1983), Hiroshima University, Faculty of School Education Hayakawa’s research centers on Operations Research and Reliability Engineering , with a focus on Bayesian statistical methods , stochastic processes , and warranty cost analysis . His work includes modeling alternating geometric processes for warranty costs, nonparametric Bayesian hazard rate estimation , and dependent failure analysis in multicomponent systems. Key contributions involve delayed reporting of warranty faults , non-zero repair time modeling , and stochastic epidemic models for fault detection. His recent projects include a non-parametric Bayesian approach to hazard function modeling (2018-2020) and generalized alternating renewal processes for warranty cost analysis. He collaborates extensively with international researchers like Richard Arnold, Stefanka Chukova, and Sarah Marshall, with publications spanning journals such as Reliability Engineering & System Safety and IEEE Transactions on Reliability . He has also explored finite mixture models for failure time and severity analysis, presenting at conferences like the Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM). Hayakawa’s scientific contributions include developing models for imperfect warranty repairs, damage accumulation in multicomponent systems, and Bayesian methods for capture-recapture estimation. He has served as President (2023-2025) and Vice-President (2020-2023) of the Reliability Engineering Association of Japan and holds memberships in IEEE and the Operations Research Society of Japan. His overseas activities include research at Victoria University of Wellington (2011-2012, 2016) and collaborations on system reliability and stochastic process modeling . Advising and Grants: Hayakawa has mentored students like Yan Liu (PhD student) and Yimo Xu (undergraduate). His research is supported by grants from the Japan Society for the Promotion of Science, including New models for failures in multi-component systems (2013-2014) and Non-parametric Bayesian modeling of system reliability (2018-2020). He also worked on robustness measures for telecommunications networks (1993-1994) in New Zealand.

• Operations Research
• Reliability Engineering
• Bayesian Statistics
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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.

• Bayesian statistics
• Statistical learning theory
• Business statistics
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Hiroki Iwamoto is an Assistant Professor at Waseda University's School of Creative Science and Engineering, Department of Industrial & Management Systems Engineering since 2023. Previously, he held research and teaching positions at Keio University and Tokyo Keizai University. His academic work bridges Business Administration, Social Systems Engineering, and Statistical Science with applications in Accounting. Education: Ph.D. in Engineering (2021, Keio University) Research Interests: Management Strategy Human Resource Management Creating Shared Value (CSV) Statistical Quality Control Business Analytics Recent Research Trends: Focus on advanced statistical methods like Mahalanobis-Taguchi Systems, DEA-R models, and SMOTE for small-sample anomaly detection. His work connects quality management with corporate social responsibility and human capital analytics. Scientific Awards 22th Asian Network for Quality Best Paper Award (2024) 21th Asian Network for Quality Best Paper Award (2023) 20th Asian Network for Quality Best Paper Award (2022) 2015 Japan Academy of Management Information Student Award Teaching: Delivers courses on Business Data Analysis, Multivariate Analysis, and Science/Engineering Literacy. Professional memberships span Japanese Society of Applied Statistics, Japan Industrial Management Association, and Bayesian SEM research communities.

• Business Administration
• Social Systems Engineering
• Statistical Science
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Naoki Awaya is an Assistant Professor at the School of Political Science and Economics , Waseda University. His research focuses on economic statistics, computational statistics, and financial econometrics with applications in machine learning and Bayesian inference. Education : Not explicitly stated Current Projects : Developing Bayesian estimation methods for nonstationary economic time series and structural changes His work includes tree-based models for probability distributions and advanced MCMC sampling techniques. He teaches graduate-level econometrics courses (Econometrics I/II) using R programming, emphasizing methodological rigor and theoretical foundations. Research interests span high-dimensional data analysis, density ratio estimation, and financial market applications through multivariate Hawkes processes. Recent publications highlight innovations in: Unsupervised tree boosting Hidden Markov Pólya trees Particle rolling MCMC algorithms Nonstationary errors-in-variables models

• Economic Statistics
• Machine Learning
• Bayesian Statistics
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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.

• Information Theory
• Coding Theory
• Statistical Learning Theory
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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.

• Statistical Science
• Earthquake Prediction
• Actuarial Science
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Goki Yasuda is an Associate Professor at the Center for Data Science, Waseda University. His research spans statistical science, intelligent informatics, and machine learning, with specialized interests in label noise robustness, semi-supervised learning, and asymptotic analysis of algorithms. He teaches extensively in data science education, including courses like 'Statistics Literacy', 'Data Analysis with Python', and 'Modeling for Data Science' through Waseda's Global Education Center. His research explores foundational machine learning challenges, including: Theoretical analysis of classification under label noise conditions Performance limits of semi-supervised learning with unlabeled data Bayesian inference for misspecified probabilistic models Algorithm optimization for real-world data imperfections Publications demonstrate a strong emphasis on theoretical machine learning (75% of recent work) with secondary focus on applied signal processing for video coding (25% of earlier publications). Research consistently employs statistical methods to address data quality issues in learning systems. He leads research projects including: 'Developing machine learning algorithms for low-quality data' (2022) 'Research on machine learning from data of various quality' (2019) 'Performance evaluation of prediction in half-ranked learning' (2018)

• Machine Learning
• Statistical Learning Theory
• Statistical Science
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Kenichiro Tamaki is an Associate Professor at the School of Political Science and Economics, Waseda University. He holds appointments in both the Faculty of Political Science and Economics and the Graduate School of Economics, with additional sub-affiliation in the Faculty of Social Sciences. His research focuses on statistical methods applied to financial time series analysis. 2008.04 - Present: Associate Professor, Faculty of Political Science and Economics, Waseda University 2005.04 - 2008.03: Research Associate, Faculty of Science and Engineering, Waseda University Dr. Tamaki earned his Doctor of Science from Waseda University. His research interests center on Statistical Finance and Time Series Analysis, with particular emphasis on developing asymptotic theory for financial applications. His work bridges theoretical statistics with practical financial engineering problems, focusing on non-Gaussian processes, locally stationary time series, and higher-order asymptotic methods. His publication record shows consistent research focused on applying advanced statistical techniques to financial problems, particularly in option pricing, bond valuation, and time series modeling with non-Gaussian innovations. His work demonstrates progression from foundational time series asymptotics to more applied financial engineering problems while maintaining rigorous statistical methodology. Dr. Tamaki is an active member of several professional organizations: Mathematical Society of Japan Japanese Association of Financial Econometrics and Engineering Japan Statistical Society International Society for Mathematical Sciences He has led research projects including 'Generalized empirical likelihood for time series' (2013-2017) and 'Bayes approach to time series models' (2007-2009), with his research resulting in 11 publications with 28 citations and an h-index of 4 (Scopus data as of September 2025). Dr. Tamaki teaches graduate courses including Statistical Finance and Research Guidance on Statistical Finance, as well as undergraduate courses in mathematics for economics and advanced econometrics.

• Statistical Finance
• Time Series Analysis
• Mathematical Statistics
• +۱ مورد دیگر

Synge Todo is a Professor in the Department of Physics, Graduate School of Science at the University of Tokyo , with joint appointments at the Mathematics and Informatics Center , Institute for Solid State Physics , Institute for Physics of Intelligence , Quantum Software Project , and Next-Generation AI Research Center . Born in 1968, he earned his B.Sc. and Ph.D. from the University of Tokyo and subsequently held post-doctoral positions at ETH Zürich. Education Ph.D. (Science), University of Tokyo, 1996 B.Sc. (Physics), University of Tokyo, 1991 Research Interests Todo’s research integrates quantum many-body physics , computational physics , and quantum computing . He develops advanced Monte-Carlo algorithms , tensor-network techniques , and renormalization group methods to study strongly correlated electron systems , lattice QCD , quantum phase transitions , and machine-learning applications in physics . His recent work explores fault-tolerant quantum computing architectures , non-variational quantum ground-state preparation , and universal scaling laws in deep neural networks . Scientific Awards Prizes for Science and Technology, The Commendation for Science and Technology by MEXT Japan (April 2019) Grants & Collaborations He currently leads several JSPS KAKENHI projects, including “ Quantum-circuit design for computational materials science ” (2023-26) and “ Enhancement of detailed-balance-violating MCMC methods ” (2020-24). He also co-leads interdisciplinary teams focusing on data assimilation for materials discovery and scalable high-performance computing . Laboratories & Software Todo heads research activities in the HΦ quantum lattice model solver and the MateriApps portal, providing open-source tools for large-scale simulations in condensed-matter and materials science.

• Quantum Many-Body Physics
• Computational Physics
• Quantum Computing
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