جست‌وجوی استاد و پژوهشگر

Tomohiro Iمشاهده پروفایل

دانشیار

• Algorithms on Strings
• String Pattern Matching
• Indexing
• +۹ مورد دیگر

Tomohiro I is an Associate Professor in the Department of Artificial Intelligence at Kyushu Institute of Technology, Japan. He has been in this position since January 2019, following a research associate role at the same institution from 2015 to 2018. Prior to that, he held postdoctoral positions at Kyushu University and TU Dortmund, Germany. His academic foundation includes a Ph.D. in Science from Kyushu University, awarded in 2012. His research primarily centers on string algorithms , with a strong emphasis on compressed data structures , pattern matching , indexing , and algorithmic efficiency . Key interests include Lyndon factorization, Lempel-Ziv compression, palindrome matching, and reverse engineering of string data structures. He frequently collaborates with prominent researchers like Hideo Bannai and Shunsuke Inenaga, producing high-impact work in theoretical computer science. His recent publications demonstrate a consistent focus on improving algorithms for string processing in compressed formats. Work on Re-Pair , RLBWT , and SLP encoding highlights his expertise in space-efficient computation. The 2022 Best Paper Award at IWOCA for work on Lyndon subsequences underscores the quality and recognition of his contributions. His research bridges theoretical analysis with practical algorithm design. Best Paper Award, International Workshop on Combinatorial Algorithms (IWOCA) 2022 Tomohiro I advises graduate students in his laboratory, although he currently notes that the lab is not accepting new research students. His work involves significant algorithmic research, often supported by theoretical grants or institutional funding, leading to numerous publications in peer-reviewed journals and conferences. He has also presented his work in invited talks, such as at CompressedAI2025 and WCTA 2024, indicating active engagement with the research community. He leads a research laboratory at Kyushu Institute of Technology, focused on advanced string processing and compressed data structures. His team collaborates extensively on algorithm design and analysis, contributing to the broader field of combinatorial pattern matching.

• Robotics
• Mechanical Engineering
• Fiber Optics
• +۳ مورد دیگر

Shinya Sadachika is a researcher affiliated with Tokyo Institute of Science , focusing on robotics, mechanical engineering, and materials science. His work centers on synthetic fiber rope mechanics, motion control systems, and sensing technologies. Research Highlights: Twisted string actuators, non-circular pulley fixation, Brillouin optical reflectometry, and semi-autonomous mobile systems Key Collaborations: Gen Endo, Hiroyuki Nabae, and other robotics experts His research combines material durability testing with advanced optical sensing and mechanical design innovations. Patents highlight practical applications in industrial systems. Scientific Awards: Not explicitly mentioned Grants: Not explicitly mentioned Lab affiliations and student mentorship details remain unspecified, though he contributes to international conferences and collaborative projects.

• machine translation
• analogy
• multilingual alignment
• +۷ مورد دیگر

Yves Lepage is a Professor at Waseda University's Faculty of Science and Engineering, specifically within the Graduate School of Information, Production, and Systems. He maintains an active research laboratory (lepage-lab.ips.waseda.ac.jp) and teaches courses including Example-based machine translation/NLP, Natural language processing, and Master's/Doctoral thesis supervision for the 2025 academic year. His research focuses on the application of analogical reasoning to natural language processing problems, particularly machine translation. Lepage's work spans formal analogy between strings, sentence-level analogies, morphological analysis, and multilingual systems. His research interests include machine translation, analogy, multilingual alignment, multilingual large language models, and foreign language aids. He has made significant contributions to understanding analogical density in corpora and developing methods to leverage analogies for translation, especially in low-resource scenarios. His publication record shows consistent output through 2024, with research evolving from foundational work on proportional analogy to sophisticated applications with neural networks. Recent work explores masked prompt learning for analogies, fuzzy analogies for translation, and organizing lexica into analogical grids for morphological generation across languages. Waseda University Teaching Award (Spring semester 2016) Lepage has successfully led multiple research projects funded by the Japan Society for the Promotion of Science, including "Theoretically founded algorithms for the automatic production of analogy tests in NLP" (2021-2024) and "Self-explainable and fast-to-train example-based machine translation using neural networks" (2018-2021). His work has involved international collaboration, including a 2023-2024 research period at the University of Montreal. He serves as Concurrent Researcher at the Waseda Research Institute for Science and Engineering (2024-2026) and has been active in professional organizations including the Information Processing Society of Japan and the Japanese Natural Language Processing Association.

• Computational Biology
• Bioinformatics
• Privacy-Preserving Data Mining
• +۵ مورد دیگر

Kana Shimizu is a Professor at Waseda University’s Faculty of Science and Engineering, School of Fundamental Science and Engineering, and a leading authority on privacy-preserving algorithms for genome and biomedical data. Since obtaining her Dr.Eng. from Waseda, she has held positions at AIST and a visiting investigator role at Memorial Sloan-Kettering Cancer Center, advancing to full Professor at Waseda in 2018. Education: Dr.Eng., Waseda University M.Eng., Waseda University (Dept. of Information Science) B.Eng., Waseda University (School of Science and Engineering) Research Interests: Prof. Shimizu integrates cryptography with life-science data analytics, developing homomorphic encryption and secret-sharing protocols that enable secure genome search, private chemical database queries, and privacy-preserving machine learning. She also contributes to computational biology through algorithms for next-generation sequencing, structural bioinformatics, and prediction of intrinsically disordered proteins. Publication Trends: Her recent works (2022-2024) emphasize practical privacy technologies—secure range queries, function secret sharing, and gradient-clipping for synthetic genomes—while earlier high-impact papers established fast similarity search (SlideSort), binding-site databases (PoSSuM), and encrypted genomic search using the Burrows-Wheeler transform, collectively bridging algorithmic innovation and real-world biomedical privacy needs. Scientific Awards: KDDI Foundation Achievement Award 2022 MEXT Commendation for Science and Technology (Research) 2018 Multiple best-paper/demo awards at CSS, IIBMP, and domestic bioinformatics meetings Grants & Advising: She currently leads JSPS KAKENHI (S) on “Compressed Secure Computation for Large-Scale Data” and an AMED project on precise cancer-genome graphs. She serves as advisor/evaluator for JST PRESTO, AMED, NBDC, and the Tokyo High Court, and has supervised numerous bachelor theses at Waseda. Labs & Teams: Prof. Shimizu heads a laboratory within the School of Fundamental Science and Engineering at Waseda, collaborating with national centers (AIST, RIKEN) and international partners (MSKCC, Finland Tekes/AF), focusing on encrypted bioinformatics platforms and high-performance genomic algorithms.