Stefan Стефан Анђелковић Andjelkovic

WORK HISTORY

EDUCATION

SKILLS

ABOUT STEFAN СТЕФАН АНЂЕЛКОВИЋ ANDJELKOVIC

An avid problem solver and a firm believer in synergistic co-evolution of human and machine intelligence.Since early childhood, I was drawn to mathematics as a magic portal you can use to bring your problems to another, safer realm (of the blank paper), disarm them there (with a pen), and come back to reality with solutions. Growing up, this notion helped me build confidence in life through learning, and the more I learned, the more fascinated with the world we live in I became. I pursued studies in physics to explore the fabrics of the universe, meanwhile, sharing my passion with younger enthusiasts by teaching physics and astronomy. After graduation, I felt that regardless of the satisfaction of my intellectual curiosity, I felt unhappy with the limited impact my work had on society, and I wanted to find a line of work where I could put my skills to better use. That\'s how I discovered bioinformatics.Working on cancer genomics for 1.5 years, and understanding molecular foundations of how life emerges from the non-living structure of the universe, I fell in love with computational biology. Wanting to learn more, I joined the Joint CMU-Pitt Computational Biology PhD program where I am currently a 4th-year student, specializing in cell and systems modeling. I joined MeLoDy lab, which is modeling cell signaling pathways, with a particular interest in the automation of model building - we are looking for patterns in how experts build network models and trying to emulate expert behavior. We are processing information from structured (databases) and unstructured (texts) data sources to help decision-makers bring informed decisions rapidly. Our work is applied beyond the scope of cell modeling, to large-scale systems, such as finding optimal interventions in pandemics.To automate most of the previously considered intellectual tasks, I am leveraging my broad background and not only coding skills (mostly in Python and bash scripting), but also statistics, graphical models, machine learning, deep learning, and NLP, while for biological models further supporting these with my training in systems biology, genomics, and structural biology. Lately, my research expanded to neuroscience as well. Ultimately, I believe that in the process of building these AI expert models we will learn more about ourselves and how we can improve our thinking to solve modern challenges.