About

My name is Giuliano, and I am a Brazilian Bioinformatician who loves stats and stories. I graduated as a Pharmacist from the Universidade Federal do Rio Grande do Sul (UFRGS) in 2019. I spent most of my undergrad studying Immunology & Microbiology. I lived in the USA for about a year as part of the Science without Borders program, sponsored by the Brazilian Government. During the summer of 2015, I had the opportunity to work as a research assistant in the Rohwer Lab at San Diego State University. In 2018, one year after starting exploring the field of bioinformatics, I was accepted as an intern at Neoprospecta Microbiome Technologies. I was focused on microbiome data analysis applied to tracking down microorganisms in food industries, hospital environments, and the random sort of possible scenarios.

After graduation, I became “formally” a Bioinformatician, thanks to the great working environment at “Neo” and the mentoring from many great people. Beyond data analysis, we were responsible for creating, maintaining, and operating analytical pipelines and web applications, mostly based on python and Django.

Around the middle of 2019, I became a Bioinformatician at BiomeHub, Neoprospecta’s spin-off focused on health care. My research would then be focused on developing statistical models to estimate bacterial colony-forming units from NGS data. As you might know, NGS is thought to yield relative information only. It would be nice, however, to be able to tell the difference between two samples with 50% of Klebsiella pneumoniae, if one has $10^2$ CFU while the other one has, say, $10^5$ CFU. This was our most recent research task (besides all COVID-19 stuff, of course). Our results are published here, in case you are interested.

Currently, I am a master’s student in bioinformatics at the University of British Columbia and a research assistant in Dr. Keegan Korthauer’s lab at the BC Children’s Hospital Research Institute.

This is just to summarize my short academic life so far. Currently, As a researcher, I am particularly interested in answering the following question: how can we navigate through the omics world and deliver reliable and deployable clinical prediction models? Hopefully, using cool statistical modeling and rigorous epidemiological designs, we can ultimately aid diagnosing, prognosing, and treating life-threatening diseases.

I consider myself a Bioinformatician, as you may have noticed, but I never left my Pharmacist background behind. As I move forward, my ultimate goal is to better integrate statistical modeling with health care & caring. This means not only complex statistics, data, and coding, but even more complex aspects of the Health Sciences. At the end of the day, decision making is to be made at the clinic, and that is the very place where I dream my science will make an impact one day.