Qi Long, Ph.D.

Professor, Department of Biostatistics, Epidemiology and Informatics

Director, Biostatistics Core, Abramson Cancer Center

Dr. Long’s research purposefully includes novel statistical research and impactful biomedical research, each of which reinforces the other. Its thrust is to develop statistical and machine learning, bioinformatics, and data mining methods for advancing precision medicine and population health with keen interests in analysis of big biomedical data including, but not limited to, -omics data, electronic health records (EHRs) data, and mobile health (mHealth) data. Specifically, he has developed methods for the analysis of big biomedical data, predictive modeling, missing data, causal inference, Bayesian methods and clinical trials. He also has made significant contributions to biomedical research areas such as cancer, cardiovascular diseases, diabetes, mental health and stroke.

Dr. Long’s research has been supported by the National Institutes of Health (NIH), the Patient-Centered Outcomes Research Institute (PCORI), the National Science Foundation (NSF), the U.S. Department of Veterans Affairs and the American Heart Association (AHA).

He is an elected fellow of the American Statistical Association and an elected member of the International Statistical Institute.

Read more about Dr. Long's research group, https://www.med.upenn.edu/long-lab/.

We seek highly motivated researchers to join our lab! Positions for PhD student, postdoc and research associate are available.

Publications - 2016

  1. M. Wang and Q. Long, “Addressing issues associated with evaluating prediction models for survival endpoints based on the concordance statistic,” Biometrics, p. in press, 2016.
  2. Y. Deng, C. Chang, M. S. Ido, and Q. Long, “Multiple imputation for general missing data patterns in the presence of high-dimensional data,” Scientific reports, vol. 6, iss. 21689, 2016.
  3. Y. Zhao, M. Chung, B. Johnson, C. Moreno, and Q. Long, “Hierarchical feature selection incorporating known and novel biological information: identifying genomic features related to prostate cancer recurrence,” Journal of the american statistical association, p. in press, 2016.