Computer Science

Main topics include the design of parallel computers and parallel programming models; shared memory architecture; message passing and distributed memory architecture; parallel programming of computer clusters using MPI and multicore programming using OpenMP; parallel algorithms for sorting, searching, linear algebra, and various graph problems.

This course introduces students to the fundamental theory and practice of the social, political, legal, and ethical implications of computer technologies in Japan and abroad. Through in-class activities, group assignments, and reflection work, students will gain a basic understanding of essential concepts, modern and historical cases, and guidelines for best practice. Key concepts include AI bias; privacy in the social media era; personal data and digital behavior tracking; vectors of misinformation; stereotypes in design, digital inclusion, and more. The main objective is to inform and encourage critical thinking in students who will be playing key roles in deciding, creating, marketing, governing, and disseminating computer technologies in Japan.

Typically, the first class each week will introduce a new topic, with interactive activities (e.g., hands-on demos, brainstorming, quick activities), individual reflection, and group discussion. Students will be given a homework assignment to be completed before the second class that week. That second class will start with a discussion of the homework and introduce the next topic for that week. Students will be expected to complete readings from the text and/or other sources before the next week of classes. Attendance is taken randomly in every class.

This course is to systematically introduce the knowledge related to the ethics and governance of artificial intelligence technology, improve students' moral sensitivity to the ethical issues of artificial intelligence, and jointly explore the possible strategies of AI governance.

The course will first systematically introduce the technical principles and development history of artificial intelligence, discuss the technical nature of artificial intelligence from a philosophical perspective, and analyze the safety and ethical issues of artificial intelligence. On this basis, it will further explain the path and strategy of artificial intelligence safety and ethical governance from three aspects: technical norms, ethical guidance and institutional regulations.

This course includes knowledge of common methods in asymmetric encryption, as well as possible attacks in faulty implementations of these methods: RSA, El-Gamal, Diffie-Hellman-Key-Exchange, elliptic curves, and selected methods of Post-Quantum-Cryptography. Students who completed this course possess profound knowledge of cryptographic methods. They are able to correctly and securely use cryptographic protocols. They are proficient in verifying the security of One-Way-Functions and (Pseudo-)Random-Number-Generators. Furthermore, they are able to recognize and avoid typical mistakes in asymmetric encryption.

This course provides an introduction to the key concepts, issues, and methods in human-computer interaction and interaction design. Through a combination of lectures and exercises, it covers usability, designing user-friendly systems, and evaluating user interfaces. The course discusses theories of human-computer interaction, the special challenges associated with the design of user-friendly interactive systems, advantages and disadvantages of different forms of interaction, building user interfaces and prototypes of user interfaces, and how to examine the usability of IT systems in a rigorous way.

This course covers various aspects of multi-core programming. Topics include programming models for multi-threading (Pthread), GPUs (CUDA), and the theoretical backgrounds behind them. Students also implement and optimize various emerging applications such as matrix multiplication, reduction, and deep learning kernels.

Through this course, students will learn basic optimization knowledge, be able to formulate a practical problem into a solvable optimization model, solve the problem using existing commercial or open source software or design their own algorithms to solve the problem.

This course mainly includes the introduction of convex analysis basics, linear and linear cone programming, optimal condition Lagrange duality, some basic optimization algorithms and their applications in finance, signal processing, machine learning and statistics.

This course instructs on python programming from a digital humanities perspective. It begins with the basics using interactive notebooks that require no installation. First, the course covers the basics of programming such as data types, loops, and variables. Later it explores and solves language-based and digital humanities problems using new programming skills and natural language processing (NLP) tools. Python 3 will be used in this course.

This course introduces the issues of digital ethics and AI faced by individuals and organizations. It covers the ethical principles governing the behaviors and beliefs about how we use technology, and how we collect and process personal information in a manner that aligns with individual and organizational expectations for security and confidentiality. It addresses challenges in balancing technological desirability with social desirability while developing digital products and services. Key topics include Professional Ethics, Artificial Intelligence Ethics and Governance, Automation and Autonomous Systems, Digital Ethics by Design, Data Protection in ICT, Human Machine Interaction, Computing for Social Good, Digital Intellectual Property Rights, and Digital Divide, Equity, Accessibility, and Inclusion.

This course introduces the mathematical formalism of quantum information theory. Topics include a review of probability theory and classical information theory (random variables, Shannon entropy, coding); formalism of quantum information theory (quantum states, density matrices, quantum channels, measurement); quantum versus classical correlations (entanglement, Bell inequalities, Tsirelson's bound); basic tools (distance measures, fidelity, quantum entropy); basic results (quantum teleportation, quantum error correction, Schumacher data compression); and quantum resource theory (quantum coding theory, entanglement theory, application: quantum cryptography).