Below is a sampling of new courses offered by Sustainability Institute affiliated faculty members.
If you are a Sustainability Institute affiliated faculty member and have a new sustainability or resilience course you would like to submit for this listing, send the details to sustainability@osu.edu. Include the course name, number, a brief description, days/times and location of the class, and your name, title and contact information. Please note this listing is intended for new courses.
How to become a Sustainability Institute affiliated faculty member
Spring 2024 Term
Ecohydrology in a Changing Climate, EARTHSC 5656 Credit Hours: 3; T/Th 2:20 – 3:40 pm, Derby Hall 048
This course is about physical and ecological processes of plant-water interactions in natural ecosystems. We will cover the principles of water transport in the soil-plant system, plant drought response, and feedbacks of plants to the climate through changing energy-water-carbon cycles. Students will analyze ecohydrological datasets from ground and remote sensing measurements and apply physically based models to analyze plant-mediated land surface processes in the context of climate change.
For questions and more information, please contact the course instructor Yanlan Liu.
Spring 2023 Term
Wicked Science, ANTHROP 5505 Credit Hours: 3; T/Th 2:20 – 3:40 pm, Scott Lab E103
Humanity faces a host of wicked problems– from global pandemics, climate change, systemic racism to growing economic inequality. Since such complex and dynamic problems are plagued by disagreement among stakeholders over their nature and cause, they are notoriously difficult to solve. To tackle such thorny problems, a new kind of science is needed.
In this course, we advocate for what we call "wicked science" –– a model of scientific training that (1) cultivates disciplinary expertise and transdisciplinary skills; (2) encourages purpose-driven commitment to problems that defy easy resolution; and (3) requires researchers and other collaborators to work across methodological and epistemological differences. In the course, we will delve into foundational texts on wicked problems, dissect key case studies, practice transdisciplinary skills, and develop on our own creative inquiries into pressing wicked problems of today. For questions and more information, please contact the course instructor Nicholas Kawa.
Quantitative Microbial Risk Analysis Modeling, PUBHEHS 7375 Credit Hours: 3; W/F 9:35 – 10:55 am, Room 47 Derby Hall
Risk is a collaborative science that addresses critical questions on how we engineer and safely interact with a world filled with chemical and microbial hazards.
You’ll learn a stochastic modeling method that can be as simple or as challenging as you want to make it. This course is specifically designed to use your data, or challenge chosen for your Masters’ or dissertation research. Many students develop a methods chapter, or build a model needed for their research for the project whilst learning the method. If you don’t have data yet or not planning on collecting it, we can use secondary data from my lab or from the open literature.
The methods you’ll learn are the same as those used to inform COVID-19 reopening of OSU campus, Ohio Correction facilities, and numerous schools, other large buildings, and hospitals in the USA and UK. Also, the same methods have been used to develop sustainability and resiliency assessments and models for drinking and wastewater treatment for both chemical and microbial hazards. For more information contact the instructor Mark H. Weir.
Public Health Data Analytics II, PUBHLTH 7015 Credit Hours: 3; T/TH 11:10 am – 12:30 pm; 300A Pomerene Hall
A new course in the College of Public Health offers graduate students from all backgrounds a unique opportunity to learn about novel concepts, the latest methods, and impactful next-gen tools in public health. This course explores the intersection of public health, computational science, epidemiology, translational science, simulation modeling, and community engagement. A combination of lectures, problem-based learning approaches, live demos, and group work will help public health data analytics come to life in this course. Students will be exposed to foundational concepts in data analytics that are relevant to the design; to analysis and interpretation of data-driven decision-making in public health; and the translation of data analytics tools and computational models to public health practice and healthcare policy. For more information contact the instructors Ayaz Hyder or Greg Rempala.