Climate Change Impact on Networked Socio-Technological Systems: Learning and Control
| Interdisciplinary Areas: | Data and Engineering Applications, Autonomous and Connected Systems, Smart City, Infrastructure, Transportation, Power, Energy, and the Environment, Security and Privacy, Human-Machine/Computer Interaction, Human Factors, Human-Centered Design |
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Project Description
As the world shifts to being more interconnected, the effects of climate change will be compounded by the inequities in resources, education, and healthcare. For example, as EVs become ubiquitous in combating climate change, equitable access to EV charging will impact social mobility and access to resources such as education and healthcare in socio-economically vulnerable populations. Another example is the disproportionate social and geographical impacts of extreme weather events that are expected to become more frequent due to climate change. In fact, such extreme events may further compound barriers to accessing the aforementioned resources and services. In order to combat these challenges, new tools and approaches that take an integrated view of these multifaceted issues are essential. This project will focus on developing models, metrics, and learning-based algorithms that leverage cross-domain data from climate models, infrastructure network physics, and social dynamics to mitigate the impacts of climate change on infrastructure access, equity, and operation
Start Date
Spring 2024
Postdoc Qualifications
Required qualifications:
- Programming experience (Python, Matlab, etc.)
- Research experience and publication record in at least one of the relevant domains (AI/ML, controls, climate science, optimization, infrastructure, health equity, etc.)
Desired qualifications:
- Data processing and visualization
- Machine learning algorithm design and/or implementation
- Mathematical modeling and analysis
- Network modeling
- Climate/equity models and impacts
- Large-scale optimization
Under-represented and women candidates are highly encouraged to apply.
Co-Advisors
Sivaranjani Seetharaman (she/her/hers), sseetha@purdue.edu, School of Industrial Engineering, https://sivaranjanis.com/
Philip E. Paré (he/him/his), philpare@purdue.edu, Elmore Family School of Electrical and Computer Engineering, https://sites.google.com/view/philpare
Short Bibliography
Panteli, Mathaios, and Pierluigi Mancarella. "Influence of extreme weather and climate change on the resilience of power systems: Impacts and possible mitigation strategies." Electric Power Systems Research 127 (2015): 259-270.
P. E. Paré, C. L. Beck, and T. Başar, "Modeling, Estimation, and Analysis of Epidemics over Networks: An Overview," Annual Reviews in Control: Special Issue on Systems & Control Research Efforts Against COVID-19 and Future Pandemics, 2020.
Rolnick, David, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross et al. "Tackling climate change with machine learning." ACM Computing Surveys (CSUR) 55, no. 2 (2022): 1-96.
Ruan, Guangchun, Dongqi Wu, Xiangtian Zheng, Haiwang Zhong, Chongqing Kang, Munther A. Dahleh, S. Sivaranjani, and Le Xie. "A cross-domain approach to analyzing the short-run impact of COVID-19 on the US electricity sector." Joule 4, no. 11 (2020): 2322-2337.
Zheng, Xiangtian, Nan Xu, Loc Trinh, Dongqi Wu, Tong Huang, S. Sivaranjani, Yan Liu, and Le Xie. "A multi-scale time-series dataset with benchmark for machine learning in decarbonized energy grids." Scientific Data 9, no. 1 (2022): 359.