Find Academic Supervisors

• Perceptual information processing
• Computer Vision
• Artificial Intelligence
• +3 more

Hiroshi Ishikawa is a Professor in the Department of Computer Science and Engineering at Waseda University's Faculty of Science and Engineering. He also serves as a Visiting Professor at the National Institute of Informatics since 2016. Previously, he held positions at Nagoya City University from 2004-2010 as Assistant Professor, Associate Professor, and Professor. His academic journey includes being a JST PRESTO Researcher from 2009-2013 and an Associate Research Scientist at New York University's Courant Institute of Mathematical Sciences from 2000-2001. Ph.D. in Computer Science, New York University (2000) Master of Science, Kyoto University Bachelor's degree in Mathematics, Kyoto University Faculty of Science (1991) Hiroshi Ishikawa's research spans perceptual information processing, computer vision, artificial intelligence, deep learning, and discrete optimization. His work focuses on developing algorithms for image restoration, segmentation, and understanding, with significant contributions to energy minimization techniques in computer vision. He has pioneered approaches in sketch simplification, medical image segmentation, and higher-order graph cuts. His research bridges theoretical advances in mathematical optimization with practical applications in medical imaging, computer graphics, and consumer electronics. Ishikawa's recent publications demonstrate a strong focus on leveraging deep learning for image enhancement and understanding. His work spans super-resolution techniques, colorization methods, human avatar generation, and medical image analysis. A notable trend is the increasing integration of attention mechanisms and generative models to solve complex vision problems, with growing emphasis on real-world applications in medical imaging and computer graphics. His research group consistently produces high-impact work that appears in top-tier computer vision conferences. 75th Annual IEICE Best Paper Award (2019) Innovative Technologies 2016 Special Prize for Culture (Ministry of Economy, Trade and Industry) MIRU Nagao Award (Best Paper Award) (2009) Young Author Award (IEEE Computer Society Japan Chapter, 2006) MIRU2006 Excellent Paper Award (2006) Harold Grad Memorial Prize (Courant Institute of Mathematical Sciences, NYU, 2000) As a professor at Waseda University, Ishikawa has mentored numerous students who have become active researchers in computer vision, including Yuya Masuda, Edgar Simo-Serra, and Satoshi Iizuka. His research has been supported by various grants, including JST PRESTO funding from 2009-2013. He has served on editorial boards for prestigious journals including IEEE Transactions on Pattern Analysis and Machine Intelligence and has held leadership roles in major computer vision conferences such as ICCV, CVPR, and ACCV. Ishikawa leads a vibrant research group at Waseda University focused on computer vision and image processing. His laboratory collaborates extensively with researchers at Nagoya City University, National Institute of Informatics, and international institutions. The group maintains strong connections with industry partners, particularly in medical imaging and consumer electronics sectors, translating theoretical advances into practical applications.

• integrated system design
• quantum computation
• information security
• +4 more

Nozomu Togawa is a Professor at Waseda University's Faculty of Science and Engineering, School of Fundamental Science and Engineering, specializing in Computer Science. He has held this position since 2009 and also serves as Chief Scientific Officer (CSO) of Quanmatic Inc. since 2022. With a PhD in Engineering from Waseda University (1997), his academic journey includes positions at Waseda University and the University of Kitakyushu before his current professorship. His research interests focus on integrated system design , quantum computation , and information security . Togawa has published extensively with over 368 papers and significant citation metrics (Scopus h-index: 23, Google Scholar h-index: 28). His work bridges theoretical quantum computing with practical security applications, particularly in hardware security and IoT systems. Togawa's research demonstrates a clear progression from traditional hardware security toward quantum-inspired computing solutions. His recent publications focus on Ising machines, quantum annealing, and hardware Trojan detection, showing how quantum approaches can solve complex optimization problems in security contexts. He has made significant contributions to applying quantum computing techniques to practical problems like course selection optimization, travel planning, and hardware security verification. Among his notable recognitions are the Minister of Education, Culture, Sports, Science and Technology Award for Science and Technology (2018), SCOPE Results Development Promotion Award (2022), and multiple Best Paper Awards. He serves on important committees including the Ministry of Internal Affairs and Communications Cyber Security Task Force and the Institute of Electronics, Information and Communication Engineers' VLSI Design Technology Research Committee. Togawa actively mentors students who frequently appear as co-authors on his publications. His research group produces high-impact work in quantum computing applications and hardware security, with strong industry connections through his CSO role at Quanmatic Inc. He has received substantial research funding supporting his innovative work at the intersection of quantum computing and security.

• Homomorphic Encryption
• Big Data Analysis
• Database Systems
• +6 more

Hayato Yamana serves as Professor at Waseda University's Faculty of Science and Engineering and concurrently holds the position of Vice President for IT Promotion and Chief Information Officer since October 2020. His academic journey began with a Dr. Eng. degree from Waseda University in 1993, followed by positions at the Electrotechnical Laboratory of MITI, before joining Waseda University as Associate Professor in 2000 and becoming full Professor in 2005. He has held significant leadership roles including Director of the Database Society of Japan and the Information Processing Society of Japan, as well as Vice Chair of IEICE's Information and Communication Society. Waseda University, Faculty of Science and Engineering (2005-Present) National Institute of Informatics, Visiting Professor (2005-Present) Waseda University, Vice President for IT Promotion (2020-Present) Deputy Chief Information Officer (2015-2020) His research spans homomorphic encryption, big data analysis, and computer architecture, with notable contributions in privacy-preserving computation, recommender systems, and secure data processing. His work bridges theoretical cryptography with practical applications in smart cities, healthcare, and e-commerce security. Recent publications demonstrate strong focus on accelerating homomorphic encryption operations, improving recommendation system diversity, and developing novel authentication mechanisms. Analysis of his 15 most recent publications reveals consistent emphasis on privacy-preserving technologies (particularly homomorphic encryption applications), innovative recommender system architectures, and biometric security solutions. His research group produces highly cited work at the intersection of cryptography, machine learning, and systems security, with practical implementations in real-world scenarios including smart grids, e-commerce, and healthcare. Fellow, Information Processing Society of Japan (IPSJ), 2020 Golden Core Award, IEEE Computer Society, 2018 Fellow, Institute of Electronics, Information and Communication Engineers (IEICE), 2018 IBM Faculty Award, 2009 Multiple Best Paper Awards from IEICE, IPSJ, and ITE Yamana has secured substantial research funding for projects in homomorphic encryption, smart city infrastructure, and privacy-preserving systems. His leadership extends to advising numerous doctoral students and directing major research initiatives including the Smart Systems and Services Innovative Professional Education Program. He maintains active collaborations with industry partners through projects involving secure computation and data analytics. His research group operates at the forefront of secure computing, with specialized laboratories focused on homomorphic encryption acceleration, privacy-preserving machine learning, and secure mobile authentication. The team actively develops practical implementations of cryptographic protocols for real-world applications in healthcare, finance, and smart city infrastructure, bridging theoretical cryptography with deployable security solutions.

• 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.

• Human–robot interaction
• Spoken language processing
• Image and audio processing
• +3 more

Tetsunori Kobayashi is a Professor in the School of Fundamental Science and Engineering at Waseda University, Japan, where he has served since 1997. He is renowned for pioneering research in human–robot interaction, spoken language processing, and multimodal conversational systems, leading to over 230 refereed papers and an h-index of 35 (Google Scholar). Education: 1980 B.Eng. in Electrical Engineering, Waseda University 1982 M.Eng. and 1985 Dr.Eng. from Graduate School of Science and Engineering, Waseda University Research Interests: His work spans intelligent robotics , perceptual information processing , pattern recognition , image and audio processing , and conversational AI . He develops algorithms for real-time dialogue systems, multi-party conversation facilitation robots, and non-autoregressive speech recognition leveraging CTC and pre-trained language models. Recent Publication Trends: Since 2020 his group has advanced non-autoregressive end-to-end ASR (Mask-CTC, Intermpl, BECTRA), noise-robust attention , multi-look-ahead conversational ASR , and neural speaker diarization . They integrate BERT-style pre-training with CTC losses to accelerate inference while maintaining accuracy. Parallel work explores vision-and-language topics such as scene-graph generation, video semantic indexing, and personalized summarization for spoken news delivery. Scientific Awards: IEICE Fellow 2023 – for multi-modal multi-party conversation research IPSJ Fellow 2016 – for pioneering robot conversation studies JST Award for Academic Start-ups 2024 Best Paper Awards from IEICE, IEEE BTAS, ACM SIGGRAPH VRCAI, and several IPSJ workshop prizes Advising & Grants: He has mentored dozens of PhD and Master’s students who now lead in academia and industry. Major funded projects include JST CREST on conversational robotics, NEDO and JST-support for AI-based speech interfaces, and industry collaborations with NHK, OKI, and NEC. Labs & Teams: Kobayashi heads the Perceptual Computing Laboratory at Waseda, conducting interdisciplinary research with domestic and international partners such as MIT, ATR, and NHK Science & Technology Labs.

• Applied Probability
• Operations Research
• Computational Biology
• +4 more

Hiroshi Toyoizumi is a Professor at Waseda University's Faculty of Commerce and Graduate School of Accountancy, with a Ph.D. in Mathematical Physics from Waseda University. His work bridges Applied Probability , Operations Research , and Computational Biology , focusing on stochastic modeling across biological and network systems. Research Interests Stochastic Models in Biology : Analyzing social queues in hover wasps and meiotic DSB regulation using quasi-birth-death processes and survival analysis. Network Security : Modeling computer virus spread on scale-free networks and proposing active defense strategies. Queueing Theory : Developing analytical frameworks for on-demand streaming, quantum merging, and blockchain-based systems. Article Trends Recent work (2020-2024) explores nonlocal diffusion , quantum queueing , and swarm behavior in financial markets . Mid-2010s studies focus on cooperative breeding and DNA recombination dynamics . Early papers (pre-2010) examine computer virus ecology , secure group communication , and deterministic queue analysis . Scientific Awards Biotechno 2013 Best Paper Award IEICE Switching Workshop SSE Research Award (1999) IEICE Young Research Engineer Award (1997) Teaching Activities Graduate-level courses in Operations Research and Financial Engineering . Undergraduate and graduate seminars in Applied Mathematics and Probabilistic Models .

Katsuro InoueView profile

Professor

• Software Engineering
• Program Analysis
• Code Clone Detection
• +5 more

Katsuro Inoue is a Professor at Ritsumeikan University within the College of Information Science and Engineering. He holds a Dr. degree of Engineering from Osaka University (1984) and specializes in Software Engineering with a focus on Program Analysis , Code Clone Detection , Software Ecosystem Analysis , and Process Modeling . His work includes developing systems like CCFinder for code clone analysis and SPARS for software component significance ranking. Research Interests Program Analysis: Code Clone Analysis, Origin and Evolution Analysis, Software Ecosystems, Bug Detection, Analysis Tools and Algorithms Program Reuse: Code Search Engines, Program Archives, Searching Mechanisms Software Process: Process Modeling, Process Centered Environments, Process-Based Metrics, Quality Standards Key Publications Code Clone Analysis (2021), Library Dependency Updates (2018), Code Clone Tracking in Open Source (2012), Distributed Code Clone Detection (2007), Software Component Significance Ranking (2005), Compiler Garbage Collection (1991), Code History Tracking (2012) Professional Activities Program Committee Member in over 20 conferences (ICSE, IWPSE, PROFES) Editorial Board Member for IEEE Transactions on Software Engineering Co-Chair of multiple workshops (IWSC, IWPSE) Reviewer for SEC Journal Teaching Course: System Architect Course Developed course materials for Compiler Design and Language Processing Created exam problems and optimization examples for language processing courses

• 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.

Naoki YamamotoView profile

Assistant Professor

• Graph Pattern Matching
• Static Type Checking
• Graph Rewriting Languages
• +4 more

Naoki Yamamoto is an Assistant Professor (non-tenure lecturer) at Waseda University's School of Fundamental Science and Engineering, Department of Computer Science and Engineering since April 2025. He also holds a part-time lecturer position at the University of Tokyo's College of Arts and Sciences (Junior Division) since October 2025. Previously, he served as a Research Associate at Waseda University from April 2022 to March 2025. He received his Doctor of Engineering from Waseda University in March 2025, following his Master of Engineering (2021) and Bachelor of Engineering (2019) from the same institution. Dr. Yamamoto's educational background is impressive, having earned his Bachelor's, Master's, and Doctoral degrees consecutively from Waseda University's School of Fundamental Science and Engineering. During his doctoral studies under Professor Kazunori Ueda, he focused on advanced topics in programming languages and graph theory. He was awarded the Outstanding Student Award (Dean's Award) from Waseda University in March 2019. His research interests center around graph-based programming language theory, with particular expertise in Graph Pattern Matching , Static Type Checking for complex data structures , and Graph Rewriting Languages . He has developed innovative approaches to handling graph structures that go beyond traditional algebraic data types like lists and trees. His work bridges theoretical computer science with practical implementation, focusing on how to safely manipulate complex graph structures in programming languages. His publication record demonstrates consistent advancement in graph-based type systems. Starting with foundational work on static type checking for graph operations (2019), he progressed to handling numeric constraints (2020), extending type expression power (2022), and developing grammar-based approaches for difference data structures (2024). His research shows a clear trajectory from theoretical foundations to practical applications, with increasing sophistication in handling complex graph structures. His work on LMNtal ShapeType represents a significant contribution to the field of graph rewriting languages. Outstanding Student Award (Dean's Award), Waseda University (2019) Dr. Yamamoto has been actively involved in teaching and research supervision, having served as a Teaching Assistant for 14 courses between 2018-2021. He currently teaches Introduction to C Programming and C Application Development at Waseda University, and Introduction to Algorithms at the University of Tokyo. He has received research funding through the Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research, collaborating with Professor Kazunori Ueda on projects related to high-level languages with powerful data structures and concurrency. As a member of Ueda Laboratory at Waseda University, Dr. Yamamoto has contributed to the development of tools like Lambda Friends, a web-based interpreter for lambda calculus written in TypeScript. His research group focuses on advancing programming language theory, particularly in the areas of graph rewriting and type systems, with applications in both programming languages and modeling languages. His work demonstrates how graph rewriting languages can unify program execution and model checking functionalities while guaranteeing well-formedness of graph structures.

• Hyperbolic Geometry
• Coxeter Groups
• Teichmüller Theory
• +2 more

Yohei Komori is a Professor at the Department of Mathematics, Faculty of Education and Integrated Arts and Sciences, Waseda University. His research spans hyperbolic geometry, Coxeter groups, Teichmüller theory, and low-dimensional topology. He has made significant contributions to understanding growth rates of hyperbolic Coxeter groups, pleating coordinates in Teichmüller space, and structures of quasi-Fuchsian groups. Education: PhD in Mathematics (Waseda University). Research: Focus on hyperbolic Coxeter groups' growth rates (proved they are Perron/2-Salem numbers), Teichmüller space compactifications, and degenerate families of Riemann surfaces. Publications: 12 papers on Scopus, including works in Proceedings of the Japan Academy and Conformal Geometry and Dynamics . Teaching: Courses in geometry, analysis, and seminars for graduate and undergraduate students. Presentations: Invited talks at international conferences like the Oberseminar Geometrie (Fribourg) and Mini-Workshop on Reflection Groups (Hausdorff Institute).

• Quantum Computing
• Quantum Information Theory
• Quantum Algorithms
• +6 more

Seiichiro Tani is a Professor at Waseda University's Faculty of Education and Integrated Arts and Sciences since April 2024. He previously held prominent roles at NTT Communication Science Laboratories (2003-2024), including Leader of the Computing Theory Research Group and Project Manager of the Research Center for Theoretical Quantum Information. He has also served as a Visiting Professor at Tokyo Institute of Technology (2022-2024) and a researcher at JST's ERATO projects (2004-2009). His academic journey includes a Ph.D. in Computer Science from the University of Tokyo (2006), preceded by M.E. and B.E. degrees from the University of Tokyo and Kyoto University, respectively.

• Computer Systems
• Soft Computing
• Combinatorial Optimization
• +5 more

Kazushi Kawamura is an Assistant Professor at the Tokyo Institute of Technology 's AI Computing Research Unit , with prior roles at Waseda University and the Institute of Science Tokyo. His academic journey began at Waseda University, where he earned a Dr. Eng in 2016. 2025.04 - Now: Assistant Professor, School of Fundamental Science and Engineering, Tokyo Tech 2024.10 - 2025.03: Assistant Professor, School of Fundamental Science and Engineering, Waseda 2020.04 - 2025.03: Specially Appointed Assistant Professor, Institute of Integrated Research, Institute of Science Tokyo His research spans combinatorial optimization , annealing processors , Ising machines , and FPGA-based computing systems . He has contributed extensively to LSI design methodology and high-level synthesis . Key publication trends include neural network compression , parallel annealing algorithms , edge AI inference , and sparse matrix operations on FPGAs. His work often integrates theoretical optimization with practical hardware implementations . 2023 CS Achievement Award 2016 ISOCC Best Paper Multiple Algorithm Design Contest Prizes (2014-2019) IPSJ SLDM Outstanding Student Awards Kawamura teaches courses like Physical Electronics Laboratory and Machine Learning at Waseda, with recent projects such as Amorphica (metamorphic annealer) and Pianissimo (sub-mW DNN accelerator). He serves on committees for IEEE , IPSJ , and IEICE , focusing on system design and circuit optimization.

Akira SuzukiView profile

Professor

• Combinatorial Reconfiguration
• Computational Complexity
• Graph Algorithms
• +2 more

Akira Suzuki is a Professor at the Center for Data-driven Science and Artificial Intelligence of Tohoku University, Japan. He earned his PhD in Information Sciences from Tohoku University in 2013, advised by Xiao Zhou. His career includes roles as Assistant Professor (2013-2019), Associate Professor (2019-2025), and a visiting student at the University of Waterloo (2013). His research spans combinatorial reconfiguration , computational complexity , graph algorithms , and threshold circuits . Education Bachelor of Engineering (2010), Tohoku University Master of Information Sciences (2011), Tohoku University PhD in Information Sciences (2013), Tohoku University Research interests focus on dynamic algorithmic transitions in combinatorial structures, including reconfiguration frameworks for graph problems, spanning trees, and vertex covers. His work often intersects with parameterized complexity and threshold circuit design . Recent publications emphasize spanning tree optimization , token swapping , and power distribution network reconfiguration . His 15 most recent articles address problems in combinatorial reconfiguration (spanning trees, vertex covers, graph colorings), computational complexity of puzzles, and applications to electrical distribution systems. Keywords include Graph Theory , NP-completeness , and Threshold Circuits . Scientific Awards Tohoku University ECEI Outstanding Performance Award (2012) IEICE Academic Encouragement Award (2013) Inoue Research Award for Young Scientists (2015) Best Paper Award at WALCOM 2025 (2025) Presidential Prize for Educational Excellence, Tohoku University (2024) He contributes to algorithmic education and has collaborated internationally, including with researchers at the University of Waterloo. His work appears in journals like Theoretical Computer Science and Algorithmica , with a focus on bridging theoretical computer science and applied systems.

• Software
• Intelligent informatics
• Control and system engineering
• +4 more

Professor Shigeru Fujimura is affiliated with Waseda University as a faculty member of the Faculty of Science and Engineering , specifically in the Graduate School of Information, Production, and Systems . With a PhD in Engineering from Waseda University, his research focuses on intelligent informatics and system engineering , particularly in scheduling, optimization, and human-robot collaboration. His research spans multiple domains including: Deep Reinforcement Learning for combinatorial optimization Energy-efficient manufacturing systems Multi-agent collaboration frameworks Augmented Reality interfaces IoT business modeling Recent publications demonstrate expertise in graph neural networks , particle swarm optimization , and generative adversarial networks applied to industrial problems. He has received multiple awards including the Invention Encouragement Prize (2003) and IEEJ Paper Presentation Award (1994) .

• Image Processing
• Pattern Recognition
• Multi-media
• +3 more

Seiichiro Kamata is a Professor at Waseda University's Faculty of Science and Engineering, specifically within the Graduate School of Information, Production, and Systems. He holds a Doctor of Engineering degree from Kyushu Institute of Technology and maintains an active research laboratory focused on advanced image processing techniques. His professional affiliations include the Institute of Electrical and Electronics Engineers, Institute of Image Information and Television Engineers, Information Processing Society in Japan, and Institute of Electronics, Information and Communication Engineers. Professor Kamata's research spans image processing, pattern recognition, multi-media systems, and signal processing with particular emphasis on deep learning applications. His work bridges theoretical advancements with practical implementations across medical imaging, hyperspectral analysis, and real-time computer vision systems. Recent publications demonstrate his leadership in developing novel neural network architectures for medical diagnostics, video enhancement, and document analysis. His publication record shows consistent high-impact contributions, particularly in medical image analysis (including Alzheimer's disease classification, ADHD diagnosis, and skin lesion detection) and computer vision techniques (including real-time semantic segmentation, visual tracking, and hyperspectral image classification). The research demonstrates a clear trajectory from foundational image processing algorithms toward sophisticated deep learning frameworks. Best Paper Award at ICIPRoB2020 (2020) - Constant-Time Gaussian Filtering Best Paper Award at ICSIPA2019 (2019) - Edge-guided Hierarchically Nested Network Best Paper Award at ICIEV2018 (2018) - Deep Neural Networks with Mixture of Experts Best Paper Award at Image Media Processing Symposium (2017) Best Paper Award at ICARCV2010 (2010) - Fast Polar Harmonic Transforms Professor Kamata's research group maintains a strong publication record with 266 papers and 1,622 citations (h-index: 21) as of September 2025. His work demonstrates consistent innovation in both theoretical foundations of image processing and practical applications across medical diagnostics, robotics, and cultural heritage preservation. The laboratory continues to produce cutting-edge research with recent focus on transformer architectures, multi-modal fusion techniques, and medical AI applications.