Teaching at University of Wisconsin-Madison
AI for Sustainability Science
ENVIR ST 403, 3 credits: Spring 2026
People and the planet are changing at unprecedented speeds- from transgressing planetary boundaries, to increased extreme weather events, to the continued loss of ecosystems and biodiversity, and ever high levels of ocean heat and atmospheric warming. The time to make change and secure a sustainable future for all, is now. Technology is moving just as fast- AI, sensors, and compute power is fundamentally changing society and economies- with accompanying profound ethical and even existential challenges. This course will examine the role of AI in advancing sustainability science, as well as the ethical concerns the methods and energy required to enable it poses for people and the planet. We will begin with foundations of machine learning for those with no prior experience, including classical machine learning (e.g. random forest), classical deep learning such as neural networks, convolutional neural networks (CNN) and long short-term memory networks (LSTMs), and newer AI approaches such as transformers, foundation models, and LLMs (Large Language models). We will review applications of these models in 7 domains of sustainability science, including global risks, earth systems, water management, ocean challenges, biodiversity, urbanization, and collective decision making. The course will include several light programming exercises and labs or homework assignments in Python, but do not require prior programming experience. Homework labs may draw on common dataset types in sustainability science including satellite imagery, geospatial data, temporal climate data, photography, and large bodies of text such as news media and sustainability reports. Tutorials will be provided to those without prior Python experience to ensure it is accessible. Students will be supported to work on a final project alone or in groups or pairs, to propose an application of AI in a sustainability domain or problem of their choice. Ambitious students or groups with programming experience or interest will be supported to pilot an AI model or application with instructor and TA support. Students will leave the course understanding the main modalities of AI used in modern sustainability sciences, and how to review or assess the validity or usefulness of an AI application in the sustainability sciences to solve real problems for people and our planet. Syllabus
Tackling Sustainability Conflicts
ENVIR ST 900 SEM 004, 3 credits: Fall 2025
What are the consequences of humans adapting to the environment? How do vulnerability and resilience shape who bears the burden or reaps the benefits of environmental change? This seminar will give students foundational concepts and frameworks in human-environment and sustainability science, including vulnerability, adaptation, resilience/robustness, transformation, politics/power/access, institutional analysis, coupled human natural systems (CHANS)/socio-ecological technical systems (SETS), transdisciplinarity, co-designing sustainable futures, decolonization, and land system science. In the 2nd half of the course, we will use these frameworks to debate contemporary issues (based on class interests!), such as geoengineering, "half-earth"/ nature positive development, Artificial Intelligence (AI)/ Big Data for development, climate finance, insurance, and justice, limits to adaptation/managed retreat, and outcomes of the Feb. 2022 working group 2 IPCC report on Impacts, Adaptation, and Vulnerability. The final project will apply course concepts/ frameworks to investigate a mechanism or feedback loop in the human-environment system of your choice. Syllabus
Past Teaching at University of Arizona
Water, Environment, and Society
GEOG 304: Spring 2023, Fall 2024
Water and society shape one another in a process of constant flux. The ways we value and manage water have implications for the environment, for society, for land, and for the flow of the resource itself. In this course, we focus on justice as a central theme- what is the current unequal distribution of water supply, access, quality, flood water, and the impact of climate change- and what might a more equitable water society look like? We assess the social processes of water management that transform water into an ecological resource, a hazard, a mechanism of settler colonialism to seize land, a commercial product, or a vector for pollution. Along the way, we will learn how inequitable access to quality water and inequitable exposure to water burdens are produced by human management. We draw on history, geography, legal scholarship, economics, hydrology, climatology, ecology, social movement activism, and other social sciences to illuminate key issues surrounding water. Some of the key questions that will guide our inquiry in this course are: Who controls water, why, and how? Who are the winners and losers of water management decisions? How is a changing climate influencing who has too much water, too little water, or land loss from sea level rise? How do approaches to mitigate or adapt to climate change interact with human water systems- and who gets left out? How might we make more just and equitable decisions about how to live with (and without) water? We will use the Colorado River, Mexico City, and the United States as our main sites of focus- but will draw on other examples in the course. Syllabus
Introduction to Remote Sensing
GEOG 330: Fall 2022
Syllabus Introduction to Remote Sensing introduces students to the modern field of remote sensing with an emphasis on how to use satellite imagery to monitor environmental change, such as urban growth, flood events, agricultural production and deforestation. This course covers a broad range of remote sensing topics including theory, data acquisition, analysis and applications. We will use both public and commercial satellite data- and both optical and radar data too. The multidisciplinary nature of remote sensing has important and far-reaching applications that span the environmental and social sciences. Thus, all of the principles, techniques, and applications of remote sensing cannot be covered in depth during one semester. But this course will provide an overview of remote sensing principles/applications and give students the background and training needed to understand and utilize satellite image data and be prepared for more advanced work in image analysis. Note that we will be using the Google Earth Engine platform for all image analysis and computer programming (coding!) in JavaScript for every lab. This course is cross-listed as GEOG 330, ENVS 330, GIST 330, GEOS 330, GEN 330, and WSM 330. Upon completion of the course, students who excel are able to:
Seminar: Foundations and Contemporary Debates in Human Environment Science
GEOG 696I: Spring 2022
What are the consequences of humans adapting to the environment? How do vulnerability and resilience shape who bears the burden or reaps the benefits of environmental change? This seminar will give students foundational concepts and frameworks in human-environment and sustainability science, including vulnerability, adaptation, resilience/robustness, transformation, politics/power/access, institutional analysis, coupled human natural systems (CHANS)/socio-ecological technical systems (SETS), and land system science. In the 2nd half of the course, we will use these frameworks to debate contemporary issues (based on class interests!), such as geoengineering, "half-earth"/ nature positive development, Artificial Intelligence (AI)/ Big Data for development, climate finance and justice, limits to adaptation/managed retreat, and outcomes of the Feb. 2022 working group 2 IPCC report on Impacts, Adaptation, and Vulnerability. The final project will apply course concepts/ frameworks to investigate a mechanism or feedback loop in the human-environment system of your choice. Syllabus