Lect. PhD ASLI BOYRAZ

After completing my Bachelor's degree in Mathematics, I desired to pursue applied science,

leading me to pursue a Master's degree in Informatics in the US at University of Pittsburgh. During

my studies, I focused on unstructured text data analysis for my thesis, where I explored various

data mining techniques. Upon returning home, I began working as an Instructor in the Computer

Technologies Department. Currently, I teach Object-Oriented Programming (Java), Database

Systems (SQL), and Web Technologies (HTML, CSS, JS). 

Merely teaching didn't fulfill my aspirations; I yearned to delve deeper into learning and research.

Recognizing the potential of my mathematical and programming background to contribute

meaningfully to humanity, I determined that the medical field was the ideal avenue. Thus, I

embarked on a PhD journey in Medical Informatics at Middle East Technical University, which has

the highest ranking in Turkey. Throughout my doctoral studies, my focus gravitated towards

becoming a data scientist. To equip myself with the requisite skills, I undertook courses in data

mining and machine learning offered by the computer engineering department. 

For my PhD project, I delved into microbiome bioinformatics. To refine my research focus, I secured

a government scholarship to visit the Science for Life Laboratory at Karolinska Institute in

Stockholm, Sweden, where I spent six months. During this time, I gained hands-on experience with

R. At the onset of my PhD, I contributed

to a forensic research endeavor aimed at addressing hospital infection issues. My role involved

analyzing a contagion dataset collected from an intensive care unit to uncover transmission

pathways among objects, leveraging microbiome profiles. While grappling with the contagion dataset, I realized the

need to treat microbiome data as "compositional data". In response, I pursued a course on

Compositional Data Analysis (CODA) offered by the Compositional Data Research Group in Spain.

Subsequently, we devised a novel procedure to analyze microbiome data employing

compositional data techniques grounded in rigorous mathematical principles. 

My future plan involves leveraging compositional data techniques for analyzing microbiome and medical datasets.