EDUCATE

This course provides a comprehensive overview of hydrologic processes, including precipitation, infiltration, evapotranspiration, andrunoff generation. The course emphasizes the physical principles governingwater movement through the atmosphere, soil, and surface water systems.Students learn to analyze hydrologic data, apply modeling techniques, andassess the impact of climate variability on water resources. The course isessential for those interested in water resource management, environmental engineering, and hydrologic science.

This course focuses on the principles andapplications of modeling hydrologic processes within a watershed. It introducesstudents to numerical and computational techniques used to simulate water flow,sediment transport, and pollutant dispersion. The course integrates real-worldcase studies to help students develop practical skills in hydrologic modelselection, calibration, and validation. Emphasizing interdisciplinary approaches, itprepares students for careers in environmental consulting, water management,and hydrology research.

This course equips students with essentialskills for analyzing climate data, with applications in hydrology, environmentalengineering, and atmospheric science. The course covers statistical andcomputational techniques for processing large climate datasets, identifyingtrends, and assessing uncertainties in climate projections. Students gainhands-on experience with data visualization, machine learning applications, andclimate model evaluation, enabling them to interpret and apply climate data toreal-world environmental and engineering challenges.

This seminar is a discussion-based seminar designedfor first-year students at UC San Diego’s Eleanor Roosevelt College. The courseexplores global issues from interdisciplinary perspectives, encouraging studentsto think critically about international affairs, cultural exchange, and globalinterconnectedness. Through readings, discussions, and research projects,students engage with contemporary global challenges while developing theiranalytical and communication skills in a small, interactive classroom setting.

This interdisciplinary colloquium explores the complex challenges and opportunities that emerging technologies pose to national and international security. Open to both undergraduate and graduate students, the course delves into how advancements in artificial intelligence, biotechnology, quantum computing, space technologies, and cyber capabilities are reshaping the global security landscape. Via a combination of expert lectures, case studies, and in-depth discussions, students will engage with leading scholars, practitioners, and policymakers from across the university and the broader community including government officials, industry leaders, and the nonprofit sector. The colloquium is also designed to foster interdisciplinary dialogue and collaboration among students and serve as a jumping-off point for research articles, honors theses, and dissertations.

This course examines the public policy challenges posed by the development, deployment, and adoption of artificial intelligence (AI) technologies. As AI reshapes industries, economies, and societies, policymakers face complex issues regarding regulation, ethics, equity, and governance. This class offers an interdisciplinary approach to understanding the policy implications of AI, exploring its impact on areas such as labor markets, privacy, security, healthcare, and democratic governance. Naturally, students will engage in key debates surrounding AI ethics, including algorithmic bias, data privacy, transparency, and accountability. They will explore regulatory frameworks for AI, comparing national and international approaches to governance, with a focus on the tensions between innovation, societal good, and potential risks. Case studies of AI applications in the public and private sectors will illustrate both the promises and pitfalls of AI adoption. The course will also critically assess the role of AI in exacerbating or mitigating inequalities, touching on issues of access, workforce displacement, and global disparities in AI capabilities. Students will be challenged to consider how public policy can balance innovation with public safety, democratic values, and human rights. By the end of the course, students will be equipped to analyze the public policy implications of AI technologies and develop informed, actionable strategies for governing their societal impact.

This course explores the intersection of technology and public policy and how these two fields are inextricably linked. It will examine the impact of technology on society and how public policy can shape the development and use of technology. Delving into the ethical considerations surrounding technology, including privacy, security, and accessibility. By the end of the course, you will have a deeper understanding of how technology and public policy intersect and how you can use this knowledge to make a positive impact in the world working in fields as diverse as climate policy, security policy, and economic policy. As you might expect, technologies discussed during the course of the seminar include artificial intelligence (AI) and machine learning (ML), cybersecurity and privacy, renewable energy and geoengineering, biotechnology and genetic engineering, robotics and automation, virtual/augmented reality and spatial computing, blockchain and distributed ledger technology, 5G networks and wireless communication, and quantum computing.

This course explores the atmospheric boundary layer, the lowest part of the atmosphere that directly interacts with the Earth's surface. The course examines how energy, moisture, and momentum are exchanged between the surface and the atmosphere, influencing local and regional climates. Topics include urban heat islands, micro climates, and the impact of human activities on boundary layer processes. Students gain a foundational understanding of how climate interacts with landscapes and ecosystems.

This course focuses on the climatic systems and processes unique to tropical regions, which play a crucial role in global climate patterns. The course covers major atmospheric phenomena such as monsoons, El Niño-Southern Oscillation (ENSO), and tropical cyclones. Through case studies and data analysis, students explore how tropical climates influence ecosystems, weather variability, and human societies, gaining insight into the broader implications of climate change in these regions.

This course is a 15-week online course aimed at water professionals and students interested in flood management. The course covers flood science, global flood governance, risk assessment, and strategies for planning, managing, and mitigating flood risks. It includes synchronous online meetings, discussions, group projects, readings, and optional field trips, offering a comprehensive look at international flood-risk management practices.

This course explores the science behind natural hazards such as earthquakes, tsunamis, hurricanes, and volcanic eruptions. The course examines the causes, impacts, and mitigation strategies for these disasters, with a focus on how they affect human societies. It provides students with an understanding of risk assessment and disaster preparedness in the face of natural phenomena.

This course provides students with an overview of leadership theories and their practical applications. Through a combination of readings, lectures, discussions, and group projects,students develop their own leadership styles while exploring historical, philosophical, and theoretical perspectives of leadership. The course also emphasizes key leadership skills, organizational behavior, and the importance of leadership in a global context. It is typically offered in the fall and winter quarters and is co-taught by Danielle Quiñones-Ortega and Joaquin Becerra.

This course is a three-quarter-long program within the Civic Engagement Scholars Program. This course examines the theoretical and historical foundations of civic engagement in American
universities while also incorporating leadership development. Students learn and
practice deliberative discourse skills, allowing them to engage in civil conversations on
controversial topics. Additionally, they design and facilitate workshops on issues relevant
to student organizations and the broader community. Co-taught by Dr. Armistead and Dr.
Viviana Marsano, the course begins in the fall and continues through winter and spring.

This is an advanced course that delves into the principles of seismology, focusing on earthquake processes, wave propagation,and fault mechanics. Students learn about seismic data analysis, earthquake forecasting, and the role of seismology in hazard assessment. The course integrates theoretical concepts with practical applications in studying earthquake behavior and mitigation strategies.

This is a course offered at UC Berkeley that
explores the intersection of health and human rights, focusing on how international
human rights frameworks can address health disparities and ensure access to
healthcare for marginalized populations. The course examines global health issues
through the lens of human rights law, exploring topics such as the right to health, health
equity, access to medicines, and the role of governments and international organizations
in protecting health as a fundamental human right. Students analyze case studies and
legal frameworks, gaining a deeper understanding of how health systems and policies
can uphold or violate human rights. This interdisciplinary course is designed for students
interested in global health policy, law, and advocacy.

This is an online course at UC Berkeley that explores
ethical challenges in global health practice and research. It examines critical issues such
as resource allocation, equity, cultural sensitivity, and the ethical responsibilities of global
health practitioners. Through case studies and discussions, students analyze real-world
dilemmas in humanitarian aid, public health interventions, and medical research in
low-resource settings. The course emphasizes ethical decision-making frameworks and
encourages students to critically evaluate the impact of health policies on vulnerable
populations. Designed for public health professionals and researchers, it provides a
foundation for ethical engagement in global health work.

This is an undergraduate course at UC
Berkeley that provides students with an introduction to the methodologies and practices
used in human rights research. The course covers both qualitative and quantitative
research methods, focusing on how data is collected, analyzed, and applied in human
rights contexts. Students learn to critically assess the ethical considerations involved in
human rights research, including the protection of vulnerable populations and the
challenges of conducting research in conflict zones or under authoritarian regimes.
Through practical exercises and case studies, students gain hands-on experience in
designing research projects and advocating for human rights. The course prepares
students for careers in human rights organizations, advocacy, and policy development.

This is a course at UC Berkeley that focuses on applying epidemiological methods
to address health challenges in low-resource settings. The course emphasizes the
practical aspects of conducting epidemiological research and interventions in developing
countries, where factors such as limited infrastructure, cultural considerations, and
resource constraints impact health outcomes. Students learn to design and implement
studies, analyze data, and develop strategies for disease prevention and health
promotion in these settings. Through case studies and fieldwork, the course provides
students with the skills necessary to tackle global health issues using evidence-based
approaches tailored to the unique challenges of developing countries. The course is
ideal for those pursuing careers in global health, public health policy, and international
development.

This is a course at at UCLA introduces students to numerical computing techniques with
applications in civil and environmental engineering. The course covers fundamental
computational methods, including numerical modeling, data analysis, and algorithm
development, using programming tools such as MATLAB. Students learn to apply these
techniques to solve real-world engineering problems related to structural analysis, fluid
mechanics, and environmental systems. Through a combination of lectures, discussions,
and hands-on exercises, the course emphasizes practical problem-solving skills and
prepares students for advanced computational work in engineering disciplines.

This is a course at UCLA is an advanced course
focusing on the theoretical and computational aspects of finite element methods for
structural analysis. It covers direct energy formulations for deformable systems,
numerical solution methods for linear equations, and the analysis of structural systems
using one-dimensional elements. The course also introduces variational calculus and
discrete element methods for membranes, plates, and shell structures, incorporating
instability effects. Through lectures and discussions, students develop the skills needed
to model and analyze complex structural systems using finite element techniques,
preparing them for research or professional practice in structural engineering.

This is a course at UC Davis focuses on the
fundamental principles of designing structural components using metallic materials,
primarily steel and aluminum. The course covers topics such as material behavior, load
analysis, and the design of tension members, compression members, beams, and
connections. Students learn to apply engineering principles and design codes to create
safe and efficient structural systems. Through lectures, problem-solving exercises, and
design projects, the course prepares students for professional practice in structural
engineering, emphasizing real-world applications and industry standards.

This is a course at UC Davis explores the
assessment and modeling of various loads acting on structures, including dead loads,
live loads, wind loads, seismic loads, and other environmental forces. The course covers
the fundamental principles of load estimation, load path analysis, and the application of
building codes and standards in structural design. Students learn analytical and
computational techniques to model structural loads accurately and evaluate their impact
on different types of structures. Through case studies and practical applications, the
course prepares students to design resilient and efficient structures capable of
withstanding real-world loading conditions.

This is a course at UC Davis is a graduate-level course
that examines probabilistic methods for assessing the reliability and safety of structural
systems. The course covers fundamental concepts in probability theory, uncertainty
quantification, and risk assessment as they apply to structural engineering. Students
learn to model random variables, perform reliability-based design, and use statistical
techniques to evaluate structural performance under uncertain conditions. The course
emphasizes practical applications in civil engineering, including load and resistance
factor design (LRFD) and reliability-based optimization. Through theoretical analysis and
computational modeling, students develop the skills to assess and improve the safety
and resilience of engineering structures.

This is a course at UCSF is a graduate-level course that equips students
with the knowledge and skills to conduct systematic reviews, a cornerstone of
evidence-based medicine and public health. The course covers the principles and
methodologies of systematic reviews, including formulating research questions,
developing search strategies, assessing study quality, synthesizing evidence, and
interpreting findings. Students learn to critically evaluate existing literature and apply
best practices in data collection and meta-analysis. By the end of the course, participants
gain hands-on experience in conducting systematic reviews, enabling them to contribute
to high-quality research and informed decision-making in healthcare and epidemiology.

This is a course at UCSF provides students with a
foundational understanding of epidemiological principles and methods. The course
covers key topics such as study design, measures of disease occurrence, association and
causation, bias and confounding, and data interpretation in public health research.
Students learn how to critically evaluate epidemiological studies and apply
epidemiological concepts to real-world public health challenges. Through lectures, case
studies, and hands-on data analysis, the course equips students with essential skills for
conducting research and making evidence-based decisions in global health contexts.

This is a course at UC Berkeley is a
graduate-level course that delves into the principles and practices of river and stream
restoration. The curriculum emphasizes understanding the foundational goals and
assumptions underlying restoration projects, integrating scientific knowledge into
planning and design processes. Students engage in reviewing restoration plans, evaluating
completed projects, and participating in independent research projects. The course format
includes lectures by the instructor and guest speakers, student presentations, and field
trips, providing a comprehensive learning experience.

This is a course at UC San Diego is an advanced course designed
to teach students the essentials of Python programming for data science applications. The
course covers fundamental Python concepts, such as data types, control flow, and
functions, and extends into more complex topics like data manipulation, visualization,
and working with data structures. Students learn how to use popular Python libraries like
Pandas, NumPy, and Matplotlib to analyze and visualize large datasets. Emphasis is
placed on practical, hands-on exercises that prepare students to solve real-world data
science problems. By the end of the course, students will have developed a strong
foundation in Python programming and be equipped to apply their skills in data-driven
fields.

This is a course at UC San Diego focuses on the
principles and techniques used in spatial data analysis. The course covers methods for
handling, analyzing, and interpreting spatial data, which are essential for understanding
patterns and relationships in geographic and environmental datasets. Students learn how
to apply these methods to real-world problems in fields such as urban planning,
environmental science, and transportation. Topics include spatial statistics, geospatial
data visualization, and the use of geographic information systems (GIS) tools. The course
provides practical applications and hands-on experience in spatial data analysis,preparing students for careers that require expertise in managing and interpreting spatial
information.

This is a course at UC San Diego which provides graduate students with the
opportunity to engage in independent research under the supervision of a faculty advisor.
The course allows students to explore a research topic in depth, typically within the field
of data science and its applications, with the goal of developing original contributions to
the field. Students are expected to conduct literature reviews, formulate research
questions, design experiments or models, and analyze results. Regular meetings with
faculty advisors guide the research process. This course is tailored to students pursuing
advanced study and prepares them for thesis projects or further academic and
professional research.