This course covers the fundamentals of molecular genetics, including principles of how genes work, how gene expression is regulated, and how signals are passed from cells to cells that are far away. In addition, the class will explore how gene expression and molecular genetics apply to various diseases, such as cancer and muscular dystrophy, and also discuss several case studies, including on the development of COVID vaccines.

Normal functioning and pathophysiology of major organ systems: nervous, respiratory, cardiovascular, renal, digestive, and endocrine. Additional topics include integrative physiology, clinical case studies, and applications in genomics-based personalized medicine.

Energy is the number one contributor to climate change and has significant consequences for our society, political system, economy, and environment. Energy as a fundamental driver of human development and opportunity. Fundamentals of each energy resource -- including significance and potential, drivers and barriers, policy and regulation, and social, economic, and environmental impacts and will be able to put this in the context of the broader energy system. Both depletable and renewable energy resources are covered, including oil, natural gas, coal, nuclear, biomass and biofuel, hydroelectric, wind, solar thermal and photovoltaics (PV), geothermal, and ocean energy, with cross-cutting topics including electricity, storage, climate change and greenhouse gas emissions (GHG), sustainability, green buildings, energy efficiency, transportation, and the developing world.

Application of design thinking to make sustainability compelling, impactful and realizable. Analysis of contextual, functional and human-centered design thinking techniques to promote sustainable design of products and environments by holistically considering space, form, environment, energy, economics, and health. Includes Studio project work in prototyping, modeling, testing, and realizing sustainable design ideas.

A city is a complex system of systems and people. Use of technology including IoT, data, advanced analytics, social media, mobile and cloud computing in Smart Cities to increase urban systems efficiency, improve the public services efficacy, and assure community sustainability. Case studies from around the world to illustrate opportunities in water, energy, transportation, buildings, and social services. The role of the citizen, public policy and public private partnerships. Risks of smart cities. Exploration of smart systems on the Stanford campus, and engagement with public sector and industry leaders.

Random variables, independence, and conditional probability; discrete and continuous distributions, moments, distributions of several random variables. Topics in mathematical statistics: random sampling, point estimation, confidence intervals, hypothesis testing, non-parametric tests, regression and correlation analyses; applications in engineering, industrial manufacturing, medicine, biology, and other fields.

What will the future look like? One idea that is recently gaining attention is the Metaverse, a computer-generated simulation of a world in which people can meet and interact. In this course, students will critically evaluate the current landscape of such conceptualizations of virtual worlds. From the psychological and behavioral mechanisms of how people perceive virtual humans, to the design of virtual spaces and interactions, to the ethical considerations that shape how virtual worlds are regulated, this course will pull from multiple fields to provide a comprehensive understanding of virtual worlds. In the first week, the instructor will distribute Oculus Quest 2 headsets for each student to use and return at the end of the quarter. Using these headsets, we will go on virtual field trips to social worlds. Through in-VR experiences, academic papers and articles, and discussions, students will develop a toolset to learn how to approach designing future virtual worlds.

Introduction to the engineering of computer applications emphasizing modern software engineering principles: program design, decomposition, encapsulation, abstraction, and testing. Emphasis is on good programming style and the built-in facilities of respective languages. Uses the Python programming language. No prior programming experience required.

Abstraction and its relation to programming. Software engineering principles of data abstraction and modularity. Object-oriented programming, fundamental data structures (such as stacks, queues, sets) and data-directed design. Recursion and recursive data structures (linked lists, trees, graphs). Introduction to time and space complexity analysis. Uses the programming language C++ covering its basic facilities.

Topics include: counting and combinatorics, random variables, conditional probability, independence, distributions, expectation, point estimation, and limit theorems. Applications of probability in computer science including machine learning and the use of probability in the analysis of algorithms.