Research Focus
Dr. Soleimanpour’s research focuses on the applications of machine learning, mathematics, and signal processing in biomedical research, with a specific emphasize on the role of viral infection in cancer. Her work synthesizes an extensive background in electrical engineering, IoT, and digital signal processing with advanced methods in big data science and quantitative biology. Holding a doctorate in Applied Mathematics and Signal Processing, Dr. Soleimanpour transitioned to biomedical research to address the pressing need for advanced data science and mathematical techniques in cancer studies.
Inside the lab
Viral infections disrupt distinct molecular processes within tumor cells and modify the immune cell composition in the tumor microenvironment. Dr. Soleimanpour’s project aims to uncover these alterations and their impact on outcomes across various tumor types by leveraging advanced big data science and quantitative biology techniques. Her current focus is on investigating how viral infections affect the expression of a family of human editing enzymes known as APOBEC3, which can lead to tumor mutagenesis.
Main Technologies and Methods Used
- Signal processing
- Machine learning
- Deep learning
- Network analysis
- Single-cell data analysis