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• Data science
• Machine learning
• Business analytics
• +10 more

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.

• Intellectual Property Law
• Copyright Law
• Information Law
• +3 more

Tatsuhiro Ueno serves as Professor at Waseda University's Faculty of Law and School of Law, specializing in Intellectual Property Law, Copyright Law, and Information Law. He currently holds leadership positions as Director of the Institute of Comparative Law (since 2024) and President of SARLIB (Society for Administration of Remuneration for Public Transmission by Libraries or Similar Facilities). With extensive international experience including visiting scholar positions at Ludwig Maximilian University of Munich and the Max Planck Institute for Intellectual Property and Competition Law, Professor Ueno has established himself as a leading authority in copyright law reform and international intellectual property matters. Professor Ueno earned his Master of Law from Kyoto University following undergraduate and graduate studies there. His academic career includes teaching positions at Rikkyo University College of Law and Politics and Seijo University Faculty of Law before joining Waseda University in 2013, where he now contributes to shaping Japan's legal education landscape. His research focuses on the critical intersection of intellectual property law with emerging technologies, particularly artificial intelligence systems and digital platforms. Professor Ueno has pioneered work on text and data mining exceptions, copyright limitations in digital environments, and creator-oriented copyright frameworks. His scholarship bridges theoretical analysis with practical policy development, influencing both domestic legislation and international copyright discourse. His recent publications demonstrate a concentrated focus on AI and copyright law, with numerous articles examining the legal challenges posed by generative AI systems, text and data mining exceptions, and the evolving landscape of copyright protection in the digital era. Professor Ueno's work consistently addresses the balance between protecting creators' rights and ensuring public access to knowledge. KDDI Foundation Book Publishing Grant (January 2025) for "Creator-Oriented Copyright System" Professor Ueno actively participates in numerous government committees shaping Japan's copyright law, including serving as Chairman of the Law and Computers Society and as a longstanding member of the Copyright Subcommittee of the Council for Cultural Affairs. His research is supported by multiple grants from the Japan Society for the Promotion of Science, including ongoing projects examining human portraits and voices in the AI age (2025-2029) and AI and intellectual property rights systems (2024-2029). He regularly presents at international forums including WIPO Summer Schools, ATRIP conferences, and copyright forums worldwide, establishing his reputation as a key thought leader in global copyright policy development.

• Neural Networks
• Genetic Algorithms
• System Identification and Control
• +7 more

FURUZUKI, Takayuki is a Professor at Waseda University's Faculty of Science and Engineering, Graduate School of Information, Production, and Systems. He holds a PhD from Kyushu Institute of Technology and maintains the NCLab research group (http://nclab.w.waseda.jp/nclab/). His professional memberships include the Institute of Electronics, Information and Communication Engineers, Institute of Electrical Engineers of Japan, Society of Instrument and Control Engineers, and IEEE. His educational background includes a PhD in Information Science from Kyushu Institute of Technology (1994-1997), a Master's degree in Electronics Engineering from Sun Yat-Sen University (1983-1986), and a Bachelor's degree in Electrical & Electronic Systems Engineering from Sun Yat-Sen University (1979-1983). Professor FURUZUKI's research spans neural networks, genetic algorithms, system identification and control, complex systems, bioinformatics, and combinatorial optimization. His work integrates soft computing techniques with medical informatics and control systems engineering, developing innovative approaches for problems ranging from brain tumor segmentation to music information retrieval. His research methodology often combines theoretical foundations with practical applications in healthcare, energy systems, and intelligent transportation. His recent publication record shows a strong focus on sequential recommendation systems, graph neural networks for medical imaging, music information retrieval, and power systems analysis. The consistent thread across his work is the application of advanced machine learning techniques to solve complex, real-world problems across diverse domains, with particular emphasis on handling missing data, improving model efficiency, and enhancing prediction accuracy. His scientific achievements have been recognized with several prestigious awards: ISCIIA2008 Excellent Paper Award (2008) Institute of Electrical Engineers of Japan Excellent Paper Award (2001) Guangdong Province Higher Education Science and Technology Progress Second Prize (1991) Guangdong Province Higher Education Science and Technology Progress Second Prize (1989) Chinese Ministry of Education Scientific and Technological Achievements Second Prize (1986) Professor FURUZUKI has maintained continuous research activity since joining Waseda University in 2003, with significant contributions to multiple research projects spanning neural computation, soft computing, and system engineering. His work demonstrates strong international collaboration, with publications involving researchers from various institutions worldwide. His research output includes over 410 papers with 3,623 citations and an h-index of 23 according to Scopus data. The NCLab research group, which he leads, focuses on neural computation and related areas, providing a platform for interdisciplinary research that bridges theoretical foundations with practical applications in healthcare, energy systems, and intelligent transportation. His laboratory environment fosters innovation in machine learning algorithms and their application to real-world problems, with particular emphasis on developing efficient, robust solutions for complex data analysis tasks.

• Biomaterials
• Biomedical Engineering
• Non-clinical testing methodology for Medical Devices
• +3 more

Professor Kiyotaka Iwasaki at Waseda University's Faculty of Science and Engineering is a leading figure in biomedical engineering with a focus on cardiovascular device development , tissue engineering , and regulatory science . His career spans over two decades at Waseda University, including roles as Associate Professor (2006-2014) and positions at Harvard Medical School's Laboratory for Tissue Engineering. Holding a Doctor of Engineering from Waseda, he serves on numerous international regulatory committees and has contributed to ISO/TC194 standards for medical devices. 1993-2002: Waseda University Education in Mechanical Engineering 2001-2004: Research Associate at Waseda University 2004: Research Scientist at Harvard Medical School 2018-Present: Professor at Waseda University His research interests include Non-clinical testing methodologies for medical devices Regulatory science frameworks Tissue engineering for ligament and cardiac applications Cardiovascular biomedical engineering His scientific contributions reveal through Development of decellularized tissue grafts for orthopaedic surgery Innovations in 3D cardiac tissue engineering using fibrin-based cell sheet stacking Pioneering bioresorbable stent technology with magnesium alloys Creation of biomechanical simulators for valvular disease modeling His awards span from the 2021 Japanese Ministerial Science Commendation 2020 JSME Standards Award 2018 ARIA Innovation Award 2001 ASAIO Fellowship While his publications demonstrate expertise in Vascular and cardiac device testing Bioresorbable stent evaluation Machine learning in medical device regulation Decellularized tissue applications

• Information Access
• Information Retrieval
• Natural Language Processing
• +3 more

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

• Marketing
• Consumer Economics
• Business Administration
• +4 more

Professor Tatsuya Kimura serves at Waseda University's Graduate School of Business and Finance, holding a Ph.D. from Waseda University and an MBA from Lancaster University (UK). His academic appointments include Professor at the Faculty of Commerce and Graduate School of Business and Finance, with additional affiliations at the Global Education Center. His research spans Consumer Economics and Society , Marketing , Internal Marketing , and Service Management , with particular focus on Japanese market adaptations of customer satisfaction metrics. Kimura pioneered the Promoter Score Japan (PSJ) as a culturally adapted alternative to Net Promoter Score (NPS), demonstrating through empirical studies that Japanese consumer response patterns differ significantly from Western models. His publication trends reveal evolving specialization from foundational marketing theory (1990s-2000s) toward culturally-specific metric development (2010s-2020s). Recent work analyzes care worker motivation through internal marketing frameworks and examines service industry dynamics through Japanese cultural lenses. His research consistently bridges academic theory with practical business applications across manufacturing and service sectors. Nikkei Advertising Award Category Award Winner (since 1990) Fujisankei Advertising Award (since 1986) Kimura leads multiple Japan Society for the Promotion of Science research projects including Research on the promotion requirements for internal marketing (2018-2023) and Internal Marketing (2011-2013), with findings published in Routledge's Internal Marketing: Another Approach to Marketing for the Growth (2016). His international collaborations include research at Columbia University (2012-2013) on global marketing strategy. He teaches core graduate courses including Marketing & Brand Management , Service Marketing & Management , and Marketing Management , with particular emphasis on case-based learning methodologies for Japanese business professionals.

• Renewable Energy
• Statistical Data Analysis
• Energy Management Systems
• +3 more

Yu Fujimoto is a Professor at Waseda University's Advanced Collaborative Research Organization for SmartSociety. Previously, he served as an Associate Professor at Waseda University's Advanced Collaborative Research Organization for Smart Society (2015-2023) and Institute for Nanoscience & Nanotechnology (2012-2015), and as an Assistant Professor at Aoyama Gakuin University's Department of Integrated Information Technology (2009-2012). He holds a Doctorate from Waseda University's Graduate School, Division of Science and Engineering and is a member of IEEE and the Information Processing Society of Japan. His research focuses on statistical science applied to energy systems, with specific interests in renewable energy forecasting, statistical data analysis for energy management systems, and statistical machine learning theory. Fujimoto has published 109 papers with 1,191 citations and maintains an h-index of 16, demonstrating significant impact in his field. His recent publications show a strong trend toward integrating machine learning techniques with power system applications, particularly focusing on renewable energy integration, electric vehicle charging optimization, smart grid technologies, and carbon reduction strategies. His work frequently addresses the challenges of grid stability with high renewable penetration and develops innovative solutions using advanced data analytics. Best Paper Award for Big Earth Data (2025) for wind generation dataset research Best Paper Award in International Conference on Power, Energy and Electrical Engineering (2025) Second Best Paper Award in International Conference on Renewable Energy Research (2024) Top Downloaded Article in IET Smart Cities (2023) Top Downloaded Article in IEEJ Transactions (2022) Fujimoto's research bridges theoretical statistical methods with practical energy system applications, contributing significantly to Japan's efforts in smart grid development and renewable energy integration. His work often involves collaboration with industry partners and government research organizations to address real-world power system challenges.

• Organic Functional Materials
• Materials Informatics
• Structural Organic Chemistry
• +4 more

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.

• Acoustic Signal Processing
• Blind Source Separation
• Digital Signal Processing
• +6 more

Shoji Makino is a Professor at Waseda University's Graduate School of Information, Production and Systems. He has held academic and research positions at institutions such as the University of Tsukuba and NTT Communication Science Laboratories. His work spans acoustic signal processing, blind source separation, and adaptive filtering. Education: Ph.D., Tohoku University (1993.03) Mechanical Engineering, Tohoku University Graduate School of Engineering (1979.04–1981.03) Engineering, Tohoku University Faculty of Engineering (1975.04–1979.03) Research Interests: His research focuses on acoustic signal processing for speech and audio, including blind source separation (BSS) , beamforming , and adaptive filtering . He pioneered methods for solving permutation alignment in frequency-domain BSS and developed geometrically constrained ICA techniques. Scientific Awards: Hoko Award (2018.10, Hattori Hokokai Foundation) Outstanding Contribution Award of the Institute of Electronics, Information, and Communication Engineers (2018.06) IEEE Signal Processing Society Best Paper Award (2014.01) IEEE Fellow (2004.01) IEICE Achievement Award (1997.05) Committee Memberships: He has served as Chair of the IEEE CAS Society's Blind Signal Processing TC, General Chair of IEEE WASPAA2007, and Associate Editor of IEEE Trans. SAP. He is actively involved in EURASIP, APSIPA, and the Acoustical Society of Japan.

Hideyuki SuzukiView profile

Professor

• Nonlinear Dynamics
• Hybrid Systems
• Many-Body Dynamics
• +5 more

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.

• Industrial Dynamics
• Firm Growth and Survival
• Entrepreneurship
• +5 more

Alex Coad is Professor at Waseda University’s Graduate School of Business and Finance, Faculty of Commerce, Tokyo. A 2007 PhD graduate of Université Paris 1 Panthéon-Sorbonne, he specialises in industrial dynamics, entrepreneurship and applied econometrics, with field-leading work on high-growth firms, innovation policy and firm survival. Education PhD student, Université Paris 1 Panthéon-Sorbonne, 2004-2007 Research Interests Coad’s research straddles three tightly linked domains: (i) the life-cycle of firms—how they start, grow, innovate and exit; (ii) the identification and support of high-growth enterprises (gazelles, scale-ups) and their role in job creation and productivity; and (iii) the econometric evaluation of innovation and entrepreneurship policies, including post-crisis resilience after COVID-19. He employs large micro-data panels, quasi-experimental designs and, more recently, machine-learning techniques to uncover robust empirical regularities. Publication Landscape Across 119 Scopus-indexed papers (h-index 44) and 17 000+ Google-Scholar citations (h-index 59), Coad’s recent output reveals three dominant streams: (1) policy-oriented studies on European high-growth firms and scale-ups; (2) COVID-related analyses of firm vulnerability, investment expectations and recovery paths; and (3) methodological contributions linking random-forest prediction to gazelle identification. His work consistently blends rigorous econometrics with actionable policy insight. Honours & Awards 2021 Waseda Research Award Teaching & Doctoral Supervision At Waseda he teaches graduate courses in Firm Growth & Innovation, Econometrics, Time-Series Analysis, Data-Science for Management and Japan Industry Studies, and supervises research seminars for the MSc in Finance. No individual student names are listed in the supplied material. Grants & Teams Coad leads or co-leads large EU-funded micro-data projects on productivity, innovation and COVID resilience, often involving cross-country research consortia; specific grant numbers or budgets are not disclosed in the text.

• Software Engineering
• Model Checking
• Formal Verification
• +4 more

Professor Tomoji Kishi is a distinguished faculty member at Waseda University's School of Creative Science and Engineering, where he has been serving since 2009. Previously, he held academic positions at Japan Advanced Institute of Science and Technology (2003-2009) following a 21-year career at NEC Corporation (1982-2003). He earned his Ph.D. in Information Science from Japan Advanced Institute of Science and Technology in 2002, building upon his earlier engineering graduate studies at Kyoto University. Professor Kishi's research focuses on software engineering, particularly in software product line development, model checking, formal verification, and aspect-oriented modeling. His work bridges theoretical formal methods with practical applications in embedded systems, automotive software, and IoT technologies. He has made significant contributions to scalability challenges in model checking for configurable systems and has pioneered approaches to variability management and approximate modeling techniques. His publication record demonstrates remarkable consistency and evolution, with 42 papers and 153 citations according to Scopus data (h-index: 7), spanning from foundational work in software architecture in the 1990s to cutting-edge research on AI-enhanced verification methods in 2025. His recent work shows increasing application of machine learning techniques to traditional formal methods problems, particularly in the context of highly configurable systems and IoT applications. ITS Standardization Activity Merit Prize (2022) from Society of Automotive Engineers of Japan IPSJ/ITSCJ Standardization Contribution Award (2017) IPSJ/ITSCJ Project Editor Award (2016 and 2013) Information Processing Society of Japan Society Activity Contribution Award (2010) IPA/SEC Journal Best Paper Award (2007) Information Processing Society of Japan Yamashita Memorial Research Award (1998) Professor Kishi has led multiple JSPS-funded research projects, including recent work on 'variability management methods prioritizing usability through variability mining' (2020-2023) and 'utility-first modeling method' (2017-2020). His industry collaborations, particularly with automotive systems developers, demonstrate the practical impact of his research. He maintains active membership in major professional societies including IEEE Computer Society, ACM, and the Information Processing Society of Japan.

• Medical Assistive Technology
• Robotics and Intelligent Systems
• Design Engineering
• +3 more

Eiichiro Tanaka is a Professor at Waseda University's Faculty of Science and Engineering , specializing in Medical Assistive Technology , Robotics , and Design Engineering . His research focuses on developing wearable robotic systems for gait training, neuro-rehabilitation, and elderly mobility assistance. Key Research Areas : 1. Human-Robot Interaction for emotion-adaptive assistive devices. 2. Biomechanical Modeling of lower-limb assistance. 3. Ontological Frameworks for academic emotion analysis. 4. Non-Powered Exosuits for muscle fatigue reduction. His work integrates deep neural networks and physiological signal processing to create emotion-aware walking aids. Recent studies (2022-2020) demonstrate 24% fatigue delay and 16% walking distance improvement using RE-Gait® devices. Earlier projects include guide-dog robots and self-contained gear diagnostics . All publications employ 3D motion analysis , Wearable Sensors , and torque control algorithms .

• Medical Informatics
• Statistical Science
• Bioinformatics
• +5 more

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.

• Information Security
• Network Security
• Peer-to-Peer Systems
• +3 more

Masato Uchida is a Professor at the School of Fundamental Science and Engineering, Waseda University, with a focus on information security and network systems. His career spans roles at Chiba Institute of Technology and Kyushu Institute of Technology, and he holds degrees in Engineering from Hokkaido University. Education: Master of Engineering (Hokkaido University, 2001), Doctor of Philosophy (Hokkaido University, 2005) His research interests include network security, peer-to-peer systems, machine learning for cybersecurity, and traffic analysis. His work addresses anomaly detection, malware classification, and resource allocation fairness in networks. Recent publications analyze adversarial machine learning, cloud service abuse, and domain parking security. Uchida's articles demonstrate expertise in cybersecurity, network modeling, and data science. He has received multiple awards for research excellence, including IEEE Outstanding Paper Awards and IEICE Communications Society honors. His leadership in committees like the Ministry of Internal Affairs and Communications' technical task forces highlights his policy influence. Scientific Awards: Outstanding Paper Award, IEEE International Conference on Teaching, Assessment, and Learning for Engineering (2020) MWS Outstanding Paper Award, Computer Security Symposium (CSS 2020) IEICE Activity Contribution Award (2016) Best Poster Award, SCIS & ISIS (2014) IEICE Network Systems Research Award (2012) He advises students in cybersecurity and collaborates on projects involving traffic load balancing, Android malware analysis, and cloud security. His professional memberships include IEEE, ACM, and the Information Processing Society of Japan.