Engineering

This course provides research training for students through the experience of belonging to a specific laboratory at the University of Tokyo. Students carry out an original research project under the guidance of assigned faculty members. Through a full-time commitment, students will be able to improve their research skills by applying the basic principles and knowledge from the literature related to the research questions, and by developing the skills to collect, interpret, and critique data in order to resolve a research question or evaluate a design for a research project. At the conclusion of the program, students submit their final work (paper, presentation, report etc.) as instructed by their lab supervisors

In this course students acquire a broad knowledge base and develop analytical and critical thinking skills. Students actively participate in seminars, read assigned texts and research papers, and analyze research data. Students also discuss results obtained in their own experiments with peers and senior laboratory members.

This course nurtures student startups based on their hands-on user-centered design projects. The course proceeds like a 3.5-month pre-seed accelerator for experiential learning with an academic and theoretical foundation drawn from social-technical system design theories and principles. The practical venture building projects are aided with lectures, sprint workshops, panel discussions and weekly readings. Entrepreneurial students learn underlying factors and forces for decisions in the entrepreneurship process and the principles for designing products, processes and people organizations under extreme uncertainty and resource constraints. The course is supported by a large international network of 150+ entrepreneurs, venture capitalists, design and manufacturing experts around the world.

This course examines the role of data abstraction to solve problems. It discusses data structures, their characteristics, and implementation in object-oriented programming language. Topics include: arrays; recursions; lists; batteries; tails; trees; binary trees; binary search trees; balanced search trees; functions and hash tables; heaps; sorting algorithms; algorithms in graphs. 

This course explores the scientific basis for understanding energy management, both at global and local scales. It discusses the energy resources available worldwide, how we consume energy today, and the pros, cons, and limitations of each form of energy production. This course examines the criteria that should drive energy management and energy transition at a global scale, as well as the current geopolitical and economic scenarios that impact this process.

A research project that assigns students to expert professors in their proposed research topic. The course takes the student's research capabilities to a more professional level. This can be most closely compared to what is called a supervised research project in American universities.

This course offers a study of fluid dynamics with an emphasis on acoustics. Topics include: fundamentals of fluid mechanics; linear acoustic equations; traveling waves; reflection and transmission of plane waves; resonators, cavities, and waveguides; radiation; dispersion and diffraction; absorption and attenuation; measurement of acoustic parameters.

This course is part of the Laurea Magistrale program. The course is intended for advanced level students only. Enrollment is by permission of the instructor. The course introduces solution algorithms for nonlinear optimization problems that are the basis of many machine learning tools which find applications in telecommunications, electronics, automatic control, and decision support systems. The course is divided into two modules. The first module introduces solution algorithms for nonlinear optimization problems. Topics in this section include: nonlinear optimization: introduction to mathematical programming, models, and algorithms; nonlinear models: unconstrained optimization and constrained optimization; relaxations and penalty algorithms; convex optimization: Lagrangian relaxation and barrier algorithm; and applications of convex optimization to support vector machine and deep learning. The second module introduces basic machine learning techniques for classification and learning. Topics in this section include: algorithms for clustering and classification; neural networks; and laboratory activity on applications for machine learning algorithms arising in real applications.

The course introduces engineering students to the concepts and practices of technological entrepreneurial thinking and entrepreneurship. Using lectures, case studies, business plans, and student presentations, the course teaches life skills in entrepreneurial thought and action that students can utilize in starting technology companies or executing R&D projects in large companies. Major course modules include introduction to entrepreneurship, idea generation and feasibility analysis, and business planning and execution.

This course explores research issues in the newly emerging field of mobile computing. Many traditional areas of computer science and computer engineering are impacted by the constraints and demands of mobility. Examples include network protocols, power management, user interfaces, file access, and security.