Alexander Abuabara, '22

I am passionate about using teaching, research and engineering to help folks. I have a bachelor’s degree in Civil Engineering, master’s degree in Production Engineering, and PhD degree in Urban and Regional Science. However, I don’t consider myself an expert in any one particular area — I’m more of a generalist. I love teaching and preparing presentations. I am fortunate to teach Analytical Methods, Planning Methods, and GIS in Urban and Regional Planning. I want to make every semester a great and special one! Every new semester my goal is to introduce fundamental research methods and cultivate analytical thinking and programming skills in my students. I know analytical skills might feel challenging at times, but these are important tools for researchers and planners. Whether it’s analyzing data, modeling scenarios, or creating visualizations, statistics and spatial analysis empower informed and impactful decisions. I encourage students to keep experimenting, exploring, and asking questions. The more they practice, the more confident they become. After all, every expert was once a beginner. Hopefully soon I can start working on a textbook that can cover the topics on causal inference that I try to bring together for my students. Also, I develop research to improve practices on risk assessment related to natural hazards with social impacts. The research areas I focus on are at the nexus of the subfields of urban geography and human ecology, spanning geographic information science to risk analysis. For example, as part of the hurricane evacuation studies, I research hurricane risk on coastal communities, more specifically households. I try to use probabilistic causal analysis through influence diagrams, which includes mapping risk for possible better risk communication and to the development of behavioral surveys. I’m a coding and reproducible research enthusiast, using R and QGIS, who tries to employ and promote the use of open-source data and software.

• Risk analysis through influence diagrams and graphical models (Bayesian networks)
• GIS-based models for risk assessment of social impacts and decision support systems
• Statistical computing (the R project and graphical models in R)
• Community-based participatory research
• Suicide risk assessment and prediction