Addressing the human respiratory health following exposure to airborne biological pollutants motivates my research. I have followed this line of research by merging human-based immunological approaches with aerobiology, molecular and computational biology. Following extensive postdoctoral training at the Bloomberg School of Public Health of the Johns Hopkins University, I linked epidemiology into my research to address non-experimental variables that may influence human respiratory health. This integrated approach has led to biochemically depicting previously uncharacterized potent airborne allergens endemic in the atmosphere of the Caribbean basin and highly pro-inflammatory microbial compounds of non-bacterial origins. My line of research has also found that these compounds pose a high respiratory burden among susceptible individuals, such as those with asthma, respiratory allergies, and other chronic respiratory diseases.
I am also a strong supporter of reproducibility and mentoring opportunities in science. Not only the experimental aspect but also the data analysis must be reproducible. For this reason, I employ code-based computational approaches in different computer languages (R, Python, Matlab) to extract the scientific story the data tries to communicate and thus make it available for others to replicate the data analysis pipeline. The data-driven mindset, combined with the experimental approaches, permits mentoring of students in the different epicycles of scientific research.