Dr. Qin develops benchmark data, analytic methods, and computational tools for enabling reproducible statistical translations of cancer genomics data. With colleagues at Memorial Sloan Kettering, she has designed and collected unique benchmark datasets to study the issue of data normalization (i.e. ‘treatment’ for data artifacts due to disparate collection) for microarrays and sequencing. She has developed novel simulation procedures and software packages to show that normalization can lead to seemingly optimistic classifiers, due to its ‘side-effect’ of over-compressed data variability. She is studying the impact of its ‘off-label’ use for an otherwise unapproved inference goal such as tumor subtyping and survival prediction. She has established an improved stochastic distribution for modeling sequencing data and is examining its implication on data normalization. Her research program is supported by a previous NIH R01 grant and an ongoing NIH R21 grant. Dr. Qin collaborates primarily with multidisciplinary investigators (Surgery, Medicine, Radiation Oncology, Pathology, and Radiology) engaged in research on sarcoma, for design and analysis of retrospective and prospective studies. She serves as co-Director of the Biostatistics and Bioinformatics Core on the Soft Tissue Sarcoma SPORE. Over the years she has also worked with investigators studying the molecular basis of other cancer types such as breast, ovarian, and pancreatic cancer. Dr. Qin has recently served as Statistical Reviewer for the Protocol Development Committee of the Department of Medicine, assessing investigator-initiated protocols for statistical rigor. She currently serves as a member of Research Council, which reviews investigator-initiated and industry-sponsored protocols for scientific merit and priority.