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• Finance
• Financial Engineering
• Corporate Finance
• +3 more

Professor Yasunori Katsurayama at Waseda University's Faculty of Social Sciences is a leading scholar in finance and financial engineering with over 30 years of research experience. His work combines mathematical modeling with empirical analysis to address corporate finance challenges, particularly in Investment decision timing Credit risk valuation Real options frameworks . His research spans 1989-2025 with significant contributions to real options analysis , credit risk modeling , and portfolio optimization . Key trends in his 15 most recent papers show increasing focus on Stochastic investment timing Japanese market reforms Credit rating inconsistencies Technology-driven R&D valuation . As concurrent researcher at Waseda Research Institute for Science and Engineering (2024-2025) and director of the Institute for Motivation Science, he bridges finance with broader data science initiatives. His teaching portfolio includes Corporate Finance , Computational Finance , and Business Management courses at both undergraduate and graduate levels.

• 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.

• Mathematical Physics
• Condensed Matter Physics
• Quantum Zeno Effect
• +4 more

Kazuya Yuasa is a Professor at the Department of Physics, School of Advanced Science and Engineering, Waseda University , where he has worked since 2013, previously holding positions as Associate Professor (2012–2013) and at the Waseda Institute for Advanced Study (2007–2012). His research spans quantum mechanics, mathematical physics, and condensed matter physics , with a focus on quantum coherence, entanglement, and control protocols. Education: Doctor of Science (2001) from Waseda University. His work on quantum Zeno dynamics, Trotter error bounds, and dynamical decoupling has advanced quantum computing and error mitigation. Key collaborations include researchers like Daniel Burgarth and Paolo Facchi, with applications in quantum optics, superconductivity, and atomic physics . The 15 most recent articles highlight his contributions to quantum simulation accuracy , Gaussian metrology , and open quantum systems . Topics range from quantum thermodynamics to entanglement generation , reflecting a blend of foundational and applied quantum research. Scientific Awards: No specific awards are listed in the provided data.

• Computational Biology
• Bioinformatics
• Privacy-Preserving Data Mining
• +5 more

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.

• Probability Theory
• Applied Probability
• Random Matrix Theory
• +3 more

Khanh Duy Trinh is a Professor (non-tenure-track) at Waseda University's Global Center for Science and Engineering, specializing in probability theory and its applications to random matrix theory and stochastic topology. He holds a PhD from Osaka University (2012) and has held academic positions at Tohoku University and Kyushu University. Current affiliation: Waseda University (2025-present) Past roles: Associate Professor at Waseda (2019-2025), Tohoku University, Kyushu University Research areas: Beta ensembles, Random topology, Spectral measures, Stochastic geometry His work demonstrates universal behavior in random matrix models through spectral analysis and topological persistence. Key contributions include central limit theorems for eigenvalue statistics, Poisson approximations in high-temperature regimes, and geometric interpretations of persistence diagrams. His recent papers focus on generalized beta processes and higher-dimensional complex structures. Current projects include: JSPS Grant 2024-2029: Universal approaches in random matrix theory Past JSPS Grant 2019-2023: Multi-aspects of beta ensembles Teaching activities at Waseda include: Introduction to Probability and Statistics Advanced Probability and Statistics Master's Thesis advising in Pure and Applied Mathematics