Assistant Professor, SBME and Dept of Pathology
machine learning; statistical & signal processing algorithms; software infrastructure for combined ‘omics & imaging data sources; cancer treatment stratification
Dr. Bashashati’s research area lies at the interface between computational, engineering and biomedical sciences. He is interested in developing machine learning, statistical and signal processing algorithms and software infrastructure to combine various sources of omics and imaging data with major emphasis on discovering novel complex biological information related to different diseases. His research is specifically focused on ovarian and breast cancers as well as lymphoid malignancies and how these cancers evolve and respond to therapies. He has published extensively in cancer genomics, bioinformatics, computational biology and brain computer interface fields and his papers have appeared in top-tier journals such as Nature and Nature Genetics.