Computer Science

The increasing reliance upon technological innovation, from pervasive digital computing in everyday smart phones and smart infrastructures to new forms of intelligent materials and pharmaceutical augmentation, is changing the nature of human-technology relationships. This course introduces the relationships among human-technology interface (HTI), human-machine interface (HMI), and human-computer interface (HCI), which have been rapidly developed during the past decade. The course teaches current design examples and theories of HTI as well as how they reflect the future of HTI.

This course provides individual research training for students in the Junior Year Engineering Program through the experience of belonging to a specific laboratory at Tohoku University. Students are assigned to a laboratory with the consent of the faculty member in charge. They participate in various group activities, including seminars, for the purposes 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 the program.

This course discusses the basic concepts and methods of information retrieval including capturing, representing, storing, organizing, and retrieving unstructured or loosely structured information. 

The principles covered include caching in order to overcome latency; pipelining to increase processor utilization; Multi-Threading and Multi-Core principles, along with potential structures, and challenges such as memory coherency and consistency.

This course addresses a number of topics in computer and network security. Topics include memory errors, Web, network, countermeasures, and pointers to research papers. The course prepares students to identify software vulnerabilities, shows how to address these, and introduces how vulnerabilities are exploited through malware.

This special lab course nurtures international students' creative competency by offering them opportunities for learning in communities of research practice. The student's supervisor arranges the research topic. Students give three oral presentations during the study period. In the presentations, students integrate ideas and analyses on laboratory results into creative and academically coherent work. FrontierLab program coordinators and supervisors attend and evaluate the final oral presentation.

Data science and machine learning are exciting new areas that combine scientific inquiry, statistical knowledge, substantive expertise, and computer programming. One of the main challenges for businesses and policy makers when using big data is to find people with the appropriate skills. Good data science requires experts that combine substantive knowledge with data analytical skills, which makes it a prime area for social scientists with an interest in quantitative methods. This course extends the foundation of probability and statistics with an introduction to the most important concepts in applied machine learning, with social science examples. It covers the main analytical methods from this field with hands-on applications using example datasets, so that students gain experience with and confidence in using the methods covered. 

This course provides students with practical knowledge and understanding of basic issues and techniques in data management, with sufficient theory to understand the reasons for these techniques. Topics include conceptual (entity relationship model) and logical design (relational model) of database models, relational database management (data definition, data manipulation, SQL, visual interactive query interfaces), and their use in application development (in particular, data extraction from DBMS to spreadsheets application and data extraction to Web applications). Projects in developing a database within an application form an essential component of this course.

This course explores the fascinating new field of digital mental health, which combines theories and methodologies from psychology and human-computer interaction science. The course starts with a general introduction to how technology has and can be used in the prevention, treatment, and support of common mental health problems (e.g., depression and anxiety). The comprehensive problem-based learning approach allows students to develop a basic understanding of the causes and symptoms of common mental health problems, and explore the active ingredients of traditional (e.g., cognitive behavior therapy) and digital interventions.  Different methods used across the two disciplines to design, develop, and evaluate digital mental health interventions and programs are examined. There are many opportunities to practice and get hands-on experience in applying cross-disciplinary methods. Students take part in co-design workshops, introductory sessions on systematic literature reviews, and develop and pitch their own (non-digital) prototypes and intervention ideas. Towards the end of the course, students are encouraged to think critically about current challenges in the field, including the ethical and moral implications of digital applications, and the future of digital mental health.

This course is about the security of networks of computers and their communications. It describes network fundamentals, and the security of Internet protocols, including wired, wireless, and mobile communication networks. It also describes network attacks and countermeasures, particularly focusing on intrusion detection and prevention systems.