Computer Science

In this course, students study time and space complexity classes; identify the complexity classes associated with computational problems; prove that problems are complete for particular complexity classes; develop the ability to fit a particular problem into a class of related problems, and so to appreciate the efficiency attainable by algorithms to solve the particular problem; study circuit complexity and the class NC of parallelizable problems; study randomized computation and the associated complexity classes; and explore how the P=NP problem is related to cryptography.  

This course aims to develop flexible and logical problem-solving skills, understanding of main bioinformatics problems, and appreciation of main techniques and approaches to bioinformatics. Through case studies and hands-on exercises, students (i) master the basic tools and approaches for analysis of DNA sequences, protein sequences, gene expression profiles, etc. (ii) understand important problems and applications of computational biology, including identifying functional features in DNA and protein sequences, predicting protein function, and deriving diagnostic models from gene expression profiles, (iii) be confident to propose new solutions to both existing and emerging problems in computational biology. This course requires students to take prerequisites.

This course provides an introduction to systems with multiple agents/units/robots that mutually depend on each other’s behaviors in order to evaluate their own or collective system performance. The course covers theory for strategic interaction between self-interested agents as well as more altruistic agents working explicitly together in complex distributed environments. Game theory and swarm intelligence are central parts of the course curriculum.

This course provides research training for exchange students. Students work on a research project under the guidance of assigned faculty members. Through a full-time commitment, students improve their research skills by participating in the different phases of research, including development of research plans, proposals, data analysis, and presentation of research results. A pass/no pass grade is assigned based a progress report, self-evaluation, midterm report, presentation, and final report.

In this course and through the DAMS Lab group (FG Big Data Engineering), students learn how to conduct research in areas of data engineering, data management, and machine learning systems. Students review scientific literature in these areas as well as how to design, implement, and evaluate prototypes. The lab group offers this project on large-scale data engineering. The course includes tasks in a wide range of components of data management and machine learning systems. Students will have the opportunity to make meaningful contributions to free open-source projects.

In this course, students demonstrate independence and originality in order to plan and organize a large project over an extended period, and to put into practice prior engineering knowledge, skills, and research methods that they have learned throughout the course. Students demonstrate their ability to apply previously taught knowledge and skills to a substantial problem in computing; conduct an independent investigation and apply cutting-edge research, methods, and thinking appropriate to the problem; present complex technical material orally to a mixed audience; and exercise scientific writing skills by way of a substantial written report, summarizing their findings.

In this course, students advance their knowledge of data-structures and algorithms to data-processing algorithms and  applications. They acquire theoretical and practical knowledge of data processing systems design and implementation for correct results and (close-to) optimal performance. Students learn how Database Management Systems (DBMSs) optimize query performance, and understand Data Processing System tuning. Finally, students explore challenges and opportunities of cloud-native Data Processing Systems, as well as the research directions such as Big Data or data management on modern hardware.

This course empowers undergraduate students in the College of Natural Sciences with essential knowledge in programming and artificial intelligence. Regardless of their specific majors, students gain foundational insights into computer science, computational science, statistics, and deep neural networks. This course equips students with practical skills that can be directly applied to scientific challenges. Through a combination of theory and practical exercises, this course offers students the opportunity to tackle real-world problems and work with data using artificial intelligence techniques. Students who possess basic computing and programming skills gain an understanding of how artificial intelligence and programming are applied in various subfields of natural sciences, fostering their ability to utilize these skills in future research endeavors. 

This six-week summer course provides individual research training through the experience of belonging to a specific laboratory at Tohoku University. Students are assigned to a laboratory research group with Japanese and international students under the supervision of Tohoku University faculty. They participate in various group activities, including seminars, for the purpose of training in research methods and developing teamwork skills. The specific topic studied depends on the instructor in charge of the laboratory to which each student is assigned. The methods of assessment vary with the student's project and laboratory instructor. Students submit an abstract concerning the results of their individual research each semester and present the results near the end of this program.

This course examines network and web security broadly from the network to the application layer. The emphasis of the course is on the underlying principles and techniques, with examples of how they are applied in practice. Students study the themes and challenges of network and web security, and the current state of the art. They develop a critical approach to the analysis of network security and web application security, and learn to bring this approach to bear on future decisions regarding security.