Stipe Kabić

Ena Aničić and Stipe Kabić are machine learning engineers at Atomic Intelligence. They hold bachelor’s degrees in mathematics and masters degrees in mathematical statistics from the University of Zagreb. They work on a variety of projects in AI, some of which are music source separation, document layout analysis and synthetic data generation for medical applications. Their work involves both research and generation of novel ideas and solutions, as well as engineering and efficient implementation of their results. Ena and Stipe are also actively engaged in the AI community, regularly participating in conferences and events to stay at the forefront of trends and advancements in the field.

Enhancing Medical Research through Synthetic Data Generation

Recently, machine learning has been applied to various problems in medicine.There have been many great results, but still a lot of unsolved problems and issues remain. One of the largest roadblocks currently is the lack of large datasets as well as the inability to share existing data due to privacy concerns. For these reasons, synthetic data generation using generative modeling techniques is a very important research area in this field. In this talk, we will give an overview of the different data modalities used in medicine and go over the state of the art generative models for such data, highlighting successful applications on biomedical data. Finally, we will go through a case study of our recent research project, where we succesfully created a synthetic dataset for a dataset containing clinical and peptide data for heart failure patients.