Shen Lab at Penn

Welcome to the Laboratory of Li Shen at the Perelman School of Medicine at the University of Pennsylvania. Our research interests include machine learning, medical image computing, biomedical and health informatics, trustworthy AI, NLP/LLMs, network science, imaging genomics, multi-omics and systems biology, Alzheimer’s disease, health disparity, and big data science in biomedicine.

The central theme of our lab is focused on developing and applying informatics, computing and data science methods for discovering actionable knowledge from complex biomedical and health data (e.g., genetics, omics, imaging, biomarker, outcome, EHR, health care). The goal is threefold:

  1. advance informatics, computing and data science by producing novel algorithms for analyzing large scale heterogeneous data sets;
  2. provide important new insights into the phenotypic characteristics and genetic and molecular mechanisms of normal and/or disordered biological structures and functions to impact the development of new diagnostic, therapeutic and preventive approaches; and
  3. improve health and health care by contributing to collaborative, multidisciplinary research that influences policy and practice.

Our major focus is to develop and apply advanced artificial intelligence (AI) and machine learning (ML) strategies for analyzing big biobank and health data to advance the study of Alzheimer’s disease (AD) and AD related dementia (ADRD). We are working on the following research topics:

  1. development of novel informatics methods for integrative analysis of imaging, genetics and transcriptomics data to identify brain imaging genetic associations with evidence manifested in the human brain transcriptome;
  2. development of transformative AI approaches for high throughput analysis of next generation sequencing and related AD biomarker and cognitive data in landmark AD biobanks for prediction of disease risk, prognosis and progression, identification of disease subtypes, and better understanding of disease mechanism;
  3. development of innovative translational big data analytic methods to systematically integrate AD biomarker research and systems medicine study, and to identify novel promising targets and drugs for repositioning against AD;
  4. integration of automated machine learning, knowledge database and informatics to advance AD research; and
  5. identifying novel technology for monitoring aging adults and those with AD/ADRD in their home environment and the AI methods and software for analyzing data generated by those technologies.

Recently, we have started to work on additional topics such as Natural Language Processing, Large Language Models and Trustworthy AI. 

We are looking for highly motivated students and scholars with background in biomedical informatics, computer science, statistics, or engineering to join our lab! We are affiliated with the following graduate groups: AMCSBEGCB, GGEB and NGG. Positions for undergraduate and graduate students and PostDocs are available. If you are interested, please email for additional information.