Two experiences in one summer: Daniel was part of a research group in Rio de Janeiro (RJ), and taught in public schools in Campo Grande (MS). During his time in Brazil he made contributions to the Brazilian host organizations and was introduced to a different way of living. He comes back to MIT with new skills, and committed to a healthy work-life balance.
“My time in Brazil was a great combination of learning and teaching. I learned about geo-spatial data analysis and some useful R and python packages, and I was also able to share my love for learning with public school students. This experience was only possible with the funding from the MISTI Sun internship, and I am grateful for having been selected as a recipient.”

When Daniel arrived in Rio de Janeiro, he was in awe of the “Cidade Maravilhosa” (Marvelous City). As the days went by, he felt more and more like a local, improving his knowledge of Portuguese, learning his way around Rio, and settling into his home away from home.

Daniel landed in Brazil as a rising sophomore interested in economics and applied mathematics. He was matched through the MIT-Brazil Program with a research internship at the Climate Policy Initiative (CPI), a research institute part of the Pontifical Catholic University (PUC-Rio). CPI’s principal Investigator, Prof Juliano Assunção, together with his collaborator at MIT, Prof Townsend, were recipients of a MISTI MIT-Brazil Seed Fund grant in 2013. CPI’s mission is to help governments, businesses, and financial institutions drive growth while addressing scarce resources and climate risk. The initiative supports key policymakers and decision-makers, such as the Brazilian Ministry of the Environment and the Brazilian National Development Bank. Their researchers/practitioners have deep expertise in policy and finance and work to improve the most important energy and land use policies around the world.

CPI uses geo spatial data analysis to suggest climate policies. All of their code is written in R (programming language) and some of their scripts run slowly. Daniel’s work was to learn about geo spatial data analysis in general, learn what CPI’s code did, and then optimize their code using Python. His main output was a script that handled the intersections between different types of protected areas in Brazil. (Ex: places can be both an indigenous land and a national park). Daniel’s code identified all these intersections and ran in less than one minute, while some similar CPI’s code ran for 20 hours.

After his internship, Daniel and another student went to Campo Grande to implement an MIT Global Teaching Lab with public schools. They taught math and physics alongside with public school teachers and the “Ensinas.” Ensinas are young Brazilians who dedicate two years of their lives after graduation to teaching in public schools as part of the program “Ensina Brasil” (Teach for All in Brazil). Daniel was able to see first-hand the challenges in teaching the high school students and had to come up with creative ways to spark curiosity and attract their attention.

  • Brazil
  • Internship
  • Math