Anna Shcherbina PhD

Anna Shcherbina is a Ph.D. student in the Biomedical Informatics program at Stanford University. Anna Shcherbina was jointly advised by Dr. Anshul Kundaje and Dr. Euan Ashley. Anna Shcherbina was interested in developing algorithms that utilize machine learning and data mining approaches to derive medically relevant conclusions from multi-layer omics data. Prior to beginning her graduate studies at Stanford, She was a researcher at MIT Lincoln Laboratory in the Bioengineering Systems and Technologies group. During this time she developed algorithms to characterize microbiome metagenomic datasets and was involved in developing and administering the DTRA Metagenomics Grand Challenge[1]. Anna Shcherbina developed the FASTQSim algorithm[2,3] to simulate complex, platform-independent HTS datasets for use as an algorithm benchmarking tool. Additionally, Anna Shcherbina developed algorithms to detect kinship and deconvolve mixtures in forensic samples by analyzing allele frequencies and inheritance patterns in forensic SNP panels[4,5,6]. During this time, she also developed a strong interest in epigenetics and participated in a time-series CHiP-seq study to characterize different stages of healing from musculoskeletal injury in a mouse model[7].

Anna Shcherbina developing deep-learning algorithms for identifying pathogenic variants in undiagnosed diseases. Anna Shcherbina also mines big data resources such as the UK Biobank and the MyHeart Counts mobile health data for meaningful associations between physical activity and health. Anna Shcherbina's long-term goal is to contribute to precision medicine by integrating physical activity, medical history, and genetic information to build a more complete picture of patient health