Pelagie Elimbi Moudio

Pelagie Elimbi Moudio is a Ph.D. student in Industrial Engineering and Operations Research and a D-Lab Data Science Fellow. She uses data science in her research on forest management and fire prevention.

Question: How did you first get interested in data science?

Answer: During my undergrad, I was trained as a mechanical engineer and I was mainly interested in design and manufacturing. Then in graduate school, I switched into an operations research program and started working on natural resource management problems. I needed to learn a number of data science skills to be able to proceed with my research so I became interested in the area.

Q: How are you using data science in your work? 

A: Currently, I am using data science in my research with fire simulation and forest management. I am working on optimal strategies for forest management under wildfire uncertainty. As part of my research, I use spatial and temporal data to model fire spread and growth. 

Q: What has surprised you about what you’ve learned in your work with data science?

A: I have been surprised by the spatial analysis dimension that has become increasingly popular in data science. Time series has a long history in data science. However, in recent years, combining time and space has become essential for the study of many fields such as, in my case, fire modeling.

Q: Are you planning on using data science in the future?

A: I work in the space of decision-making models, and in the era of big data, data-driven decision models have become ubiquitous. I will certainly be using data science to extract, process, and analyze data for various decision models throughout my career.

Q: Do you have any advice for students curious about data science?

"I view the skills in data science as an adaptable toolkit that can enhance one's career and research. It is never too early or too late to get into data science. In addition, the greatest benefit is obtained when data science is intersected with domain-specific knowledge. Do not be afraid to take data science skills into other fields of interest."