The development and diffusion of science innovative technologies is indispensable for modern society. However, despite its benefits, the development of science and technology is not without various risks and social problems. So far as we are going to make societal decisions for the use of science and technologies with diverse social implications that encompass both risks and benefits, sometimes involving values implications, there is a need for mechanisms of decision making and management of the development and utilization of science and technology. Decisions can be different depending on environmental, institutional and cultural conditions. In addition, innovative policy instruments/ mechanisms to deal with rapidly changing science and technology, including regulatory measures, are required for implementing decisions. This course will deal with wide range of issues from local to global levels faced at the interface areas between science, technology and public policy from comparative perspective of Japan, the US and Europe. It offers key theoretical issues surrounding Science and Technology and provides students with the tools and frameworks, such as risk assessment/ management and transition management, to analyze them. This course invites students from both natural science backgrounds (i.e. the graduate school of engineering, new frontier science and so on) and social science backgrounds (graduate school of public policy, law and politics, and economics and public policy). We expect students to acquire interdisciplinary perspective in addition to their primary major, which is one of the critical skill in analyzing complex social technical issues posed by science and technology.

This course will deal with wide range of issues from local to global levels faced at the interface areas between science, technology and public policy from comparative perspective of Japan, the US and Europe. It offers key theoretical issues surrounding Science and Technology and provides students with the tools and frameworks, such as risk assessment/ management and transition management, to analyze them. This course invites students from both natural science backgrounds (i.e. the graduate school of engineering, new frontier science and so on) and social science backgrounds (graduate school of public policy, law and politics, and economics and public policy). We expect students to acquire interdisciplinary perspective in addition to their primary major, which is one of the critical skill in analyzing complex social technical issues posed by science and technology.

Important note: Prior coding or statistical modeling experience not required Learn basic visualilzation and statistical modeling to cutting-edge techniques like LLMs (ChatGPT). This course provides rigorous training to create reproducible research in economics and public policy. Open to all skill levels. - Use Python to collect, clean, and analyze policy-relevant data. - Design and implement reproducible research workflows to effectively manage and utilize public data. - Apply statistical and machine learning methods to analyze policy problems - Process and analyze text data using traditional NLP and modern LLMs (ChatGPT) to extract meaningful insights. - Develop visualization to communicate research findings effectively to both technical and non-technical audiences. - Collaborate effectively using professional data science tools like GitHub, Overleaf, and Google Colab.

広く産学官にわたってグローバルに活躍するために必要な「俯瞰力」を養成することを目指す。物質科学の各分野について最先端の知識を修得し、自分の専門分野と周辺分野がどのように関連するか、あるいはし得るか、について深く考察するために、第一線で活躍する講師の方々にその分野の最前線を概観していただく。さらに、それらの講義を通して異分野間のコミュニケーションを円滑に進めるための具体的方法論を学ぶ。 This survey course is designed to enable students to develop the broad perspective that is required of global leaders working in and across industry, academia, and government. Students will gain knowledge and insight on advancements in each field of materials science research, given by leading researchers working on the frontline in those fields. This will allow students to consider how peripheral fields are related to their own area of expertise, and to consider the potential for forging bridges between related fields in the future. In addition, students will learn specific methodologies designed to facilitate smooth communication among different disciplines.

The course will take an intersectional approach to why we commonly find an underrepresentation of women in the Science, Technology, Engineering and Mathematics (STEM) fields. The course will begin by addressing our own unconscious biases and stereotypes, and question the research behind male and female differences. We will cover the brief history of the underrepresentation of minorities and explore research articles on why and how women and other minorities have been excluded from STEM fields. Students will be expected to read scientific articles, discuss them, and apply those concepts to examine their own social environments.

The course will take an intersectional approach to why we commonly find an underrepresentation of women in the Science, Technology, Engineering and Mathematics (STEM) fields. The course will begin by addressing our own unconscious biases and stereotypes, and question the research behind male and female differences. We will cover the brief history of the underrepresentation of minorities and explore research articles on why and how women and other minorities have been excluded from STEM fields. Students will be expected to read scientific articles, discuss them, and apply those concepts to examine their own social environments.

This course uses a genre theory approach to examine how popular writing about science and technology reflects and shapes social structures. Students will investigate how science and technology are defined, how they differ from other kinds of knowledge-making, and what roles they play in society. The readings, writing assignments, and discussions will help students to analyze how texts implicitly and explicitly address these questions, thus developing their ability to assess and communicate scientific and technological concepts for a broad audience. Students taking this course should anticipate completing weekly reading and writing tasks outside of class in preparation for the in-class lecture and discussion. Goal 4: Quality education Goal 9: Industry, innovation and infrastructure Goal 16: Peace, justice, and strong institutions Goal 17: Partnerships for the goals (other goals depending on class readings)

This course uses a genre theory approach to examine how popular writing about science and technology reflects and shapes social structures. Students will investigate how science and technology are defined, how they differ from other kinds of knowledge-making, and what roles they play in society. The readings, writing assignments, and discussions will help students to analyze how texts implicitly and explicitly address these questions, thus developing their ability to assess and communicate scientific and technological concepts for a broad audience. Students taking this course should anticipate completing weekly reading and writing tasks outside of class in preparation for the in-class lecture and discussion. Goal 4: Quality education Goal 9: Industry, innovation and infrastructure Goal 16: Peace, justice, and strong institutions Goal 17: Partnerships for the goals (other goals depending on class readings)

This course aims to cultivate internationally competitive young researchers equipped with literacy and competency to become future leaders in industry and academia. The course deals with multidisciplinary application skills and the in-depth research in specialized fields so that students accomplish the ability to work in a broader spectrum and apply one’s skills to a multidisciplinary setting. The topics of the course include medical and biomedical robotics, medical high-tech industries, disease prevention, health care system, science technology and industrial policy, energy technology, and health security and community resilience.

Important note: Prior coding or statistical modeling experience not required Learn basic visualilzation and statistical modeling to cutting-edge techniques like LLMs (ChatGPT). This course provides rigorous training to create reproducible research in economics and public policy. Open to all skill levels. - Use Python to collect, clean, and analyze policy-relevant data. - Design and implement reproducible research workflows to effectively manage and utilize public data. - Apply statistical and machine learning methods to analyze policy problems - Process and analyze text data using traditional NLP and modern LLMs (ChatGPT) to extract meaningful insights. - Develop visualization to communicate research findings effectively to both technical and non-technical audiences. - Collaborate effectively using professional data science tools like GitHub, Overleaf, and Google Colab.