جستجوی استاد راهنمای دانشگاهی

• Perceptual information processing
• Computer Vision
• Artificial Intelligence
• +۳ مورد دیگر

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.

• Multimedia Signal Processing
• Computer Networks
• Communication Engineering
• +۳ مورد دیگر

Jiro Katto is a Professor at Waseda University's School of Fundamental Science and Engineering, where he has been conducting research and teaching since 1999. He received his Ph.D. from the University of Tokyo and has established himself as a leading researcher in multimedia signal processing and computer networks. His academic journey includes positions as Associate Professor (1999-2004), Professor (2004-present), and Director at NEDO (2004-2008), along with research experience at NEC C&C Laboratories (1992-1999) and a Visiting Scholar position at Princeton University (1996-1997). Professor Katto's research interests focus on Multimedia Signal Processing and Computer Networks, with particular expertise in video compression, 5G network performance, and learned image compression techniques. His work bridges theoretical advancements with practical implementations, as evidenced by his extensive publications in top-tier conferences and journals. His research group has made significant contributions to point cloud compression, latency compensation in remote systems, and hardware-accelerated video encoding for UHD streaming. His publication record is impressive, with 276 papers cited 3,323 times in Scopus and 6,169 times in Google Scholar, reflecting his substantial impact in the field. His recent work shows a strong trend toward applying deep learning techniques to traditional signal processing problems, particularly in the areas of image and video compression, where his team has developed novel approaches to improve compression efficiency while reducing computational complexity. Electric Telecommunications Promotion Foundation Telecommunications System Technology Award (2023) Takayanagi Kenjiro Foundation Takayanagi Kenjiro Achievement Award (2020) Institute of Image Information and Television Engineers Fellow (2020) Institute of Electronics, Information and Communication Engineers Fellow (2015) IEICE Communications Society Activity Contribution Award (2006) IEICE Academic Encouragement Award (1995) SPIE VCIP 1991, Best Student Paper Award (1991) Professor Katto has served on numerous prestigious committees including IEEE ComSoC Tech News Editorial Board, IEEE Technical Program Committees for major conferences (Globecom, ICC, ICIP), and editorial boards for several academic journals. His leadership in the academic community extends to chairing conferences like IWAIT 2011 and serving as Editor-in-Chief for journals in his field. His research has practical applications in commercial 5G networks, video streaming services, and remote monitoring systems, demonstrating the real-world impact of his work.

Masatoshi Okutomiمشاهده پروفایل

استاد

• Medical Imaging
• Computer Vision
• Deep Learning
• +۵ مورد دیگر

Professor Masatoshi Okutomi is affiliated with the Department of Systems and Control Engineering at the School of Engineering, Institute of Science Tokyo. His research focuses on advanced medical imaging techniques, particularly in endoscopy and 3D reconstruction, leveraging deep learning and neural networks. Key interests include virtual chromoendoscopy for cancer detection, image restoration, and stereo matching under challenging conditions. His work bridges computer vision and healthcare, addressing real-world applications such as MRI reconstruction and foggy stereo matching. Notable contributions include developing lightweight medical segmentation networks for edge devices and advancing neural radiance fields (NeRF) for novel view synthesis. His research spans diverse domains: from improving video quality assessment to enhancing object detection in high-dynamic-range images. Collaborative projects emphasize practical solutions for medical diagnostics and robust image processing in adverse environments. Recent articles highlight advancements in temporally-consistent video restoration, few-shot view synthesis, and degraded image classification using knowledge distillation. These innovations underscore his commitment to pushing boundaries in both theoretical computer vision and applied medical technology.

• Robotics
• Artificial Intelligence
• Tactile Sensing
• +۶ مورد دیگر

Satoshi Funabashi is an Assistant Professor in the Department of Intermedia Art and Science at Waseda University's School of Fundamental Science and Engineering, Japan. He is affiliated with the Graduate Program for Embodiment Informatics under Waseda University's Program for Leading Graduate Schools and contributes to multiple graduate schools including the Graduate School of Creative Science and Engineering. Education: Doctor of Engineering (Waseda University, 2017-2021) Research Focus: Robotics, tactile sensing, deep learning, and embodiment informatics Academic Appointments: Assistant Professor (non-tenure-track) His research centers on symbiotic robotics and tactile-driven manipulation, with recent publications exploring graph convolutional networks, vision-touch fusion, and morphology-specific deep learning for robotic hands. He has secured multiple competitive research grants including JSPS KAKENHI and JST ACT-I programs. Scientific Awards: Grant-in-Aid for Scientific Research (B) (KAKENHI), JSPS (2024-2027) Grant-in-Aid for Early-Career Scientists, JSPS (2022-2024) JST ACT-I Research Fellow (2020-2022, 2018-2020) JSPS Research Fellowship DC1 (2017-2020) He collaborates with the Intelligent Dynamics and Representation Lab (Prof. Tetsuya Ogata) and the Intelligent Machine Lab (Prof. Shigeki Sugano) at Waseda University. He has interned at MIT's CSAIL (2018-2019) and conducted research at UC Davis (2015). His work has been cited over 500 times with an h-index of 14 according to Google Scholar.

Kei Hiroseمشاهده پروفایل

استاد

• Sparse Estimation
• Multivariate Analysis
• Statistical Modeling
• +۲ مورد دیگر

Kei Hirose serves as Professor and Division Leader of the Division of Industrial and Mathematical Statistics, with concurrent appointments in the Division of Strategic Liaison and Division of Fujitsu Mathematical Modeling for Decision Making. Division Leader: Industrial and Mathematical Statistics Concurrent Roles: Strategic Liaison, Fujitsu Mathematical Modeling for Decision Making His research pioneers sparse estimation techniques for high-dimensional data analysis, focusing on multivariate methods including factor analysis and Gaussian graphical modeling. He develops computationally efficient algorithms for parameter estimation while investigating theoretical properties of sparse models, with implementations distributed via R packages. Applications prominently include genomic data analysis such as gene expression datasets, bridging statistical theory with practical computational biology challenges. Scientific recognition: No awards or fellowships specified in source material Academic mentorship and funding details were not documented in the provided text. Similarly, laboratory infrastructure, research teams, and future research trajectories remain unspecified in the available information.

• Computer Vision
• 3D Reconstruction
• Perceptual Information Processing
• +۷ مورد دیگر

SATO Jun holds the position of Professor at the Department of Information Engineering (メディア情報分野) within the Faculty of Engineering at Nagoya Institute of Technology. He received his Ph.D. in Information Engineering from the University of Cambridge (1993–1996) and previously served as a Research Assistant at Cambridge (1996–1998). His research focuses on perceptual information processing and intelligent informatics, with specializations in computer vision, 3D reconstruction, and optical engineering applications. He has authored influential books like Computer Vision - Geometry of Vision (1999) and Computer Graphics (2017), and contributed to international publications such as Springer's Computer Vision: A Reference Guide (2020). Key professional roles include serving as President of the IEEE Nagoya Branch since 2023, Associate Editor of the International Journal of Computer Vision (Springer, 2010–present), and committee member for various organizations including Japan's Ministry of Education (2015–present) and the Nagoya City Business Potential Evaluation Committee (2008–2015). He has been recognized with prestigious awards including the BMVC Best Science Paper Prize (1994, 1997) and ITE Niwa-Takayanagi Prize (2015). His research extends to industrial collaborations, evidenced by patents like the "3D Information Presentation Device" (2014–2017) and "Position Detecting Device" (2016–2019). Recent work emphasizes applications in automotive safety, occluded object reconstruction, and novel imaging systems using advanced optical configurations and neural networks.

• Artificial Intelligence
• Intelligence Informatics
• Internet of Things
• +۴ مورد دیگر

Xin Qi is an Assistant Professor (non-tenure-track) at the School of International Liberal Studies, Faculty of International Research and Education, Waseda University. He holds a Doctor of Engineering degree from Waseda University (2019) and previously studied at Hangzhou Dianzi University (2009-2013). His educational background includes: Doctor of Engineering, Waseda University (2014.09-2019.09) Bachelor's degree, Hangzhou Dianzi University (2009-2013) Dr. Qi's research focuses on the intersection of artificial intelligence and network technologies. His primary research areas include Intelligent Informatics, with specific interests in Artificial Intelligence applications for security systems, IoT infrastructure, and Information-Centric Networking. His work bridges theoretical computer science with practical applications in public safety, disaster management, and secure communications. His research demonstrates strong interdisciplinary connections between computer science, electrical engineering, and practical security applications. Analysis of Dr. Qi's publication record (44 papers with 557 Scopus citations and h-index of 12, or 757 Google Scholar citations with h-index of 15) reveals a strong focus on practical applications of AI and blockchain technologies. His most recent work (2023-2024) centers on latency compensation in remote monitoring systems, GNSS spoofing detection using machine learning, and AI-based security screening systems. Earlier work (2021-2022) shows significant contributions to ledger-based point transfer systems for LPWAN networks and image processing techniques for secure data transmission. His research demonstrates a consistent trajectory toward solving real-world problems in security, networking, and IoT applications. Dr. Qi is an active member of professional organizations including the Institute of Electronics, Information and Communication Engineers and IEEE. His research is supported by multiple projects including "Low latency interactive zero latency video/Somatic integrated network" (2022-2024), "Dynamic Secure Network for Traffic and Logistics" (2021-2023), and "Lightweight distributed ledger technology authentication system for LPWA" (2020-2022). These projects reflect his focus on practical applications of his research in real-world scenarios, particularly in disaster management and secure communications infrastructure. He has received consistent research funding that supports both theoretical development and practical implementation of his work. Dr. Qi also contributes to educational initiatives at Waseda University, teaching courses including Data Structure and Algorithms, Human-Technology Interface, and Computerized Society, where he bridges theoretical computer science with practical applications for undergraduate students.

Akihiko Toriiمشاهده پروفایل

استادیار

• Computer Vision
• 3D Reconstruction
• Place Recognition
• +۶ مورد دیگر

Akihiko Torii is an Assistant Professor (Research Associate) at the Okutomi & Tanaka Lab in Tokyo Institute of Technology. He holds a PhD from Chiba University and completed a postdoctoral fellowship at the Center for Machine Perception (CMP) at Czech Technical University in Prague. His research focuses on computer vision, with expertise in 3D reconstruction, place recognition, localization, and structure from motion. He has contributed to influential datasets like the 24/7 Tokyo, Pittsburgh (Pitts250k), SF Revisited, and InLoc datasets, advancing visual localization and feature matching techniques. **Education and Background:** PhD in Computer Science, Chiba University Postdoctoral Researcher, Czech Technical University (2014–2018) **Research Interests:** His work spans Computer Vision , including 3D Reconstruction , Place Recognition , Feature Matching , and Multi-View Stereo . He emphasizes practical applications such as indoor/outdoor localization under changing conditions. **Awards and Recognition:** 2017: CVPR Outstanding Reviewers Award 2016: IEEE Japan Chapter Young Author Award 2015: Tokyo Tech Young Investigator Award Multiple Best Paper Awards (2013–2014) **Key Projects:** Developed algorithms like Multi-View Inverse Rendering and NetVLAD , and contributed to benchmarking frameworks for visual localization accuracy. His work bridges theoretical advancements and real-world applications in robotics and autonomous systems.

• Network Analysis
• Complex Systems
• Social Systems Engineering
• +۵ مورد دیگر

Tengfei SHAO is an Assistant Professor (non-tenure-track) at Waseda University's Global Education Center and Researcher at the Institute of Data Science. His academic journey includes a Doctorate in Engineering from Waseda University's School of Creative Science and Engineering, completed in March 2025. 2022.04 - 2025.03: Doctorate at Waseda University, School of Creative Science and Engineering, Management Systems Program 2020.04 - 2022.03: Master's studies at Waseda University Dr. SHAO's research centers on network science applications across diverse domains. His primary interests include Complex Systems, Network Analysis, and Social Systems Engineering, with particular focus on second-hand luxury market dynamics, AI ethics education, and sentiment analysis. His work frequently employs network motifs as analytical tools to uncover hidden patterns in consumer behavior, market trends, and social interactions. He has developed innovative approaches to analyzing e-commerce versus brick-and-mortar retail environments, particularly in the luxury goods sector. His publication record reveals a consistent trajectory of interdisciplinary research spanning computer science, economics, and social sciences. His most recent work integrates multimodal learning with sentiment analysis, medical image segmentation techniques, and advanced network analysis methodologies. The trend shows increasing sophistication in applying network science to real-world business problems, particularly in the luxury market sector. Association for Computing Machinery (ACM) member since 2024 Institute of Electrical and Electronics Engineers (IEEE) member since 2024 計測自動制御学会 (SICE) member since 2022, serving on Social Systems Committee since 2024 人工知能学会 (JSAI) member since 2022 電子情報通信学会 (IEICE) member since 2022 情報処理学会 (IPSJ) member since 2020 At Waseda University's Global Education Center, Dr. SHAO teaches multiple programming courses including Basic AI Programming, Introduction to Python Programming, Database Management, and Fundamentals of Information Science across different quarters. His teaching portfolio demonstrates expertise in foundational computer science education for international students. His research methodology consistently applies network science principles to diverse domains, creating bridges between technical analysis and practical business applications, particularly in understanding consumer behavior in digital marketplaces.

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

Shunsuke Horii is an Associate Professor at the Center for Data Science, Waseda University. His research spans information theory, coding theory, statistical learning theory, and data science applications. He actively collaborates with industry through initiatives like the Waseda Data Science Consortium. Education: Ph.D. in Science and Engineering from Waseda University (2009), Master's from Waseda University Graduate School of Science and Engineering (2004). Research Focus: Addresses causal effect estimation in data science using Bayesian decision theory, sparse modeling, and optimization techniques like ADMM and variational inference. Develops efficient algorithms for multiuser communication, matrix completion, and privacy-preserving distributed computing. Teaching: Instructs courses on statistics literacy, data science, and programming with Python/R across multiple academic quarters. Grants: Leads projects funded by Japan Society for the Promotion of Science, including causal inference frameworks, product recommendation systems, and business analytics. Publications: 21 papers with 61 Scopus citations, focusing on LP decoding, Bayesian hierarchical models, and statistical causal analysis.

• Stochastic Processes
• High-Dimensional Statistics
• Financial Econometrics
• +۳ مورد دیگر

Yuta Koike is an Associate Professor at the Graduate School of Mathematical Sciences, University of Tokyo . His research focuses on statistical inference for stochastic processes , particularly in high-frequency financial data and high-dimensional statistics . He has contributed to covariance estimation under non-synchronous observations, microstructure noise, and jumps, and recently explores lead-lag relationships between stochastic processes. Research Interests : Stochastic processes, high-dimensional statistics, financial econometrics, high-frequency data, probability theory. Awards : The 32nd JSS Ogawa Award The 1st ISI Tokyo Memorial Award Editorial Roles : Associate Editor for Asia-Pacific Financial Markets (2019–present), Bernoulli (2025–present), and Japanese Journal of Statistics and Data Science (2023–present). Teaching : Courses in statistical analysis, econometrics, and probability theory at the University of Tokyo, Seijo University, and Tokyo Metropolitan University. His publications span journals like Annals of Statistics , Stochastic Processes and their Applications , and Journal of Theoretical Probability . He actively presents at international conferences, including the Joint Statistical Meetings and SPA Conference .

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

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

• Human-Computer Interaction
• Virtual Reality
• Haptics
• +۳ مورد دیگر

Haruo Igarashi serves as Assistant Professor at Waseda University's School of Advanced Science and Engineering since April 2025, following a Research Assistant position at the same institution (2022-2025). His academic foundation spans mathematics and physics across Japan's top universities. His educational pathway includes: Doctorate in Physics and Applied Physics (2022-2025), Waseda University Graduate School of Advanced Science and Engineering Master's in Pure and Applied Physics (2020-2022), Waseda University Graduate School of Advanced Science and Engineering Bachelor's in Mathematics (2016-2020), University of Tokyo Faculty of Science Dr. Igarashi pioneers human-computer interaction systems that translate abstract high-dimensional concepts into tangible experiences. His research integrates virtual reality with advanced haptics—particularly shape-memory alloy tactile displays and thermal feedback mechanisms—to enable intuitive exploration of 4D+ spaces. Key innovations include decomposing 4D force vectors for 3D haptic presentation and developing VR systems for scientific data visualization like XAFS analysis. His publication trajectory (2021-2025) reveals progressive refinement from foundational 4D interaction frameworks to applied medical/industrial implementations. Early work established core algorithms for tactile 4D navigation, while recent publications demonstrate practical applications in pain management and materials science, maintaining consistent focus on user-centered interface design. Recognition includes: Best Presentation Award from Information Processing Society of Japan (2021) for tactile 4D visualization research He leads the internal research project 'High-dimensional Space Interaction Using Force Feedback' (2023), which developed novel algorithms for projecting 4D force vectors into dual 3D haptic streams. His primary collaboration with Hideyuki Sawada spans 7 publications, creating an integrated pipeline from hardware (tactile sensors) to theoretical frameworks (4D spatial reasoning).

• High Performance Computing
• Entertainment and Game Informatics
• Human Interface and Interaction
• +۳ مورد دیگر

Dr. Tsukasa Fukusato is an Assistant Professor at the School of Fundamental Science and Engineering , Waseda University since April 2023. His work spans Human Interface and Interaction , Entertainment Informatics , and High-Performance Computing , with a focus on interactive systems for digital art and animation. Current Research: Developing interactive tools for 2D/3D animation, workflow systems in bioinformatics, and AI-assisted artistic creation Technical Expertise: Latent diffusion models, motion segmentation, sketch-based interfaces, garment transfer, and aerodynamic design His recent publications (2022-2024) demonstrate advancements in: Animation and motion design (15+ papers) Workflow execution services AI-guided facial synthesis Interactive garment/textile modeling Biomechanical simulation systems Visual language interfaces Scientific Awards: 2024 Best Poster Award (NICOGRAPH) 2023 Honorable Mention (Computational Visual Media) Multiple Best Paper/Poster awards (2014-2023) 2014 Nishida Award from the Institute of Image Information and Television Engineers of Japan Teaching Activities (2025): Lecturer for Computer Graphics, Media Science, and Project-Based Learning Supervising graduation theses and advanced digital media projects

Yonghwan Kimمشاهده پروفایل

دانشیار

• Distributed Algorithms
• Optimization
• Autonomous Mobile Robot Systems
• +۴ مورد دیگر

Yonghwan Kim serves as an Associate Professor in the Department of Computer Science within the Graduate School of Engineering at Nagoya Institute of Technology. His academic foundation includes a Doctorate (2015) and Master's (2011) in Information Science from Osaka University, where he completed both graduate programs. Dr. Kim's research spans distributed algorithms with emphasis on autonomous mobile robot systems, fault tolerance, and self-stabilization. His work addresses critical challenges in multi-robot coordination under asynchronous scheduling, defected view models, and dynamic network topologies. Key research areas include: Distributed algorithm design for mobile entities Self-stabilizing graph construction techniques Optimization of robot gathering and dispersion Fault-tolerant systems in constrained environments His publication record reveals strong focus on theoretical foundations with practical applications, particularly in robotics and network systems. Recent work (2022-2024) demonstrates increasing complexity in handling defected views, multi-color luminous robots, and dynamic grid environments. His research shows consistent progression from foundational graph algorithms toward sophisticated mobile entity coordination systems. Professional recognition includes the Engineer of Information Processing qualification and multiple principal investigator roles for competitive research grants. His committee service spans prestigious conferences including SSS, DISC, and CANDAR where he served on program committees from 2018-2024. Dr. Kim actively mentors researchers, evidenced by his role as corresponding author for junior colleagues' publications. His grant portfolio demonstrates leadership in projects like 'Distributed Graph Algorithms for Unpredictable Dynamic Environments' (2020-2024) with ¥16,120,000 funding, and internal university grants focused on low-functionality mobile terminals and network optimization.