Daniel Zeiberg

PhD Student

  • BSE in Computer Science, University of Michigan

Daniel Zeiberg is a PhD student at Northeastern University’s Khoury College of Computer Sciences, advised by Predrag Radivojac. He received his BSE in Computer Science from the University of Michigan, where he did undergraduate research developing machine learning methods with applications in clinical medicine.

His research focuses on the development of machine learning methods and their applications in bioinformatics. He focuses on learning in the positive-unlabeled classification setting, a semi-supervised classification setting in which one is interested in learning a binary classifier from a small set of labeled positives and a large set of unlabeled data. This problem setting arises in a variety of applications, including bioinformatics. For example, in one of his current projects, predicting the pathogenicity of genetic mutations, datasets often include a large set of known pathogenic mutations but relatively few known benign mutations. Through methods that the field has developed, it is possible to use learn a classifier that can predict whether the unlabeled mutations cause a disease without seeing any labeled benign mutations.

What Daniel finds most interesting about his research is that the applications he works on will one day have a positive impact on patients. Moving forward, he would like to develop tools that are used to improve patient outcomes.