Our research group focuses on developing computational approaches to analyze high-throughput drug and genetic screens, along with multi-omics datasets, to study the fundamental regulatory mechanisms underlying cancer cell response to treatments. Working closely with experimental and clinical groups, we develop integrative approaches to address one of the most pressing challenges in cancer: drug resistance. We believe that the next generation of machine learning models in biology will be driven by high-throughput screens coupled with rich endpoint assays such as single-cell omics and imaging. Ultimately, these models will accurately predict cellular phenotypes across different contexts and reveal fundamental molecular mechanisms that can be exploited to tackle human disease. Another strong focus of the group is to develop the next generation of multidisciplinary engineers who can bridge the fields of computer science, cell biology, and biomedicine. Keywords: Cancer Systems Biology, Bioinformatics, and Machine Learning.