Dr. Daniel Bojar is a tenure-track assistant professor at the Wallenberg Centre for Molecular and Translational Medicine & the Department of Chemistry and Molecular Biology at the University of Gothenburg in Sweden, focusing on machine learning and data science in the field of glycobiology. He obtained his PhD in mammalian synthetic biology at ETH Zurich and continued his postdoctoral training in computational biology at MIT & Harvard University. His group develops and applies methods to discover sequence-to-function associations and biological roles of glycans via a plethora of approaches. Daniel was awarded a Branco Weiss Fellowship - Society in Science, as well as a Foresight Fellowship, and was recognized as a "Rising Star" by the journal Advanced Science. He was also featured on the 2022 Forbes 30 Under 30 Europe list for work in Science & Healthcare.

Lecture: Glycans are ubiquitous, contributing to an array of biological functions and playing a crucial role in various physiological and pathological processes. However, their intrinsic complexity, coupled with data scarcity, has often stymied comprehensive analysis and understanding. Recent developments in AI and data science have ushered in a new age of data-driven research in glycobiology that will transform research on complex carbohydrates. In this lecture, I will discuss our latest advances in using machine learning and bioinformatics to automate as well as enhance glycan data analysis, from processing over prediction to understanding. By developing dedicated neural networks for analyzing glycans, my group has advanced diverse aspects from lectin-glycan binding prediction to approaching single-cell glycomics. This enables us to use AI and data science to both ask and answer questions about glycan properties and functions, revealing new biological insights into human physiology and disease.