Teresa Maria Creanza graduated in Physics at the University of Bari and obtained a Ph.D. in "Complex Systems for Life Sciences" from the University of Turin. Since 2018 she is a permanent researcher at CNR. Her research interests include the development and application of computational techniques in the biological, biomedical and pharmacological domains. Her research focuses on statistical inference for differential analysis and study of conditional dependencies in high-dimensional data (probabilistic graphical models), development of machine learning and deep learning models. She has been involved in several research projects aimed at the development of computational approaches and software for the analysis and integration of molecular data to understand the molecular drivers of complex diseases such as cancer, Crohn's disease and brain disorders including multiple sclerosis, brain aging and neurodegeneration. In this framework, she implemented machine learning and statistical models to analyze high dimensional expression data generated from high-throuput technologies with the aim to identify new candidate gene markers and biological processes or pathways unveiling molecular mechanims responsible of the pathology. Moreover, she designed and applied new methods for inferring gaussian graphical models by using gene expression data to build regulatory gene networks and study their changes in the pathological state. Recently, her research activity has intensively devoted to the cheminformatics in collaboration with the Institute of Cristallography CNR and the Department of Pharmacy of the University of Bari. In details, she is engaged in the development and the application of computational methods based on machine learning and deep learning for the target prediction, the de-novo design and the repositioning of drugs.

Research interests: Machine Learning, Deep learning, Computational Systems Biology, Bioinformatics, Cheminformatics