Dr. Mei-Ching Chen has worked, over 20+ years in the area of data science, in academia, commercial organizations as well as support to various federal agencies, as a software developer, computer scientist, data engineer/analyst/scientist, and machine learning algorithm expert. She had also worked as a project and program manager at DSFederal, Inc., leading teams in support of multiple federal agencies to improve both organizational and project processes with better management and deliverables. Her recent work and research focuses on creating and developing practical solutions for all aspects of data characterization: from requirements, architecture design to analytical product release; from algorithm design and development, analysis/analytics strategies, process improvement and automation to program evaluation. Her current position is with the National Institutes of Health (NIH) Center for Scientific Review (CSR) as a Data Scientist. Furthermore, she continues and devotes herself to educating STEM students at various educational levels. She has been teaching courses at Johns Hopkins University Whiting School of Engineering since 2010.

Her goal in STEM education is to influence students to further their education in Science and Engineering, leaving a well-trained and educated next generation Scientists and Engineers. In her teaching, she focuses on hands-on experimental laboratories, the practical applications of the subject matter, and theoretical concepts in computer science, electrical engineering, and mathematical disciplines. She has taught both graduate and undergraduate courses in topics include but are not limited to algorithms, computer programming, data science, digital signal processing, engineering analysis, neural networks, as well as signals and systems. Dr. Chen has published numerous technical journal articles and conference proceedings.

Education History

  • M.S. Electrical Engineering, University of Texas at San Antonio
  • M.S. Computer Science and Engineering, Tatung University
  • Ph.D. Electrical Engineering, University of Texas at San Antonio

Work Experience

Instructor, JHU Whiting School of Engineering, Engineering for Professionals


• Richard K. Nakamura, Lee S. Mann, Mark D. Lindner, Jeremy Braithwaite, Mei-Ching Chen, Adrian Vancea, Noni Byrnes, Valerie Durrant, Bruce Reed, “An experimental test of the effects of redacting grant applicant identifiers on peer review outcomes,” eLife 10:e71368, Oct. 2021, https://doi.org/10.7554/eLife.71368.
• Elena A. Erosheva, Sheridan Grant, Mei-Ching Chen, Mark D. Lindner, Richard K. Nakamura, and Carole J. Lee, “NIH Peer Review: Criterion scores completely account for racial disparities in overall impact scores,” Science Advances, vol. 6, no. 23, Jun. 2020.
• Mark D. Lindner, Adrian Vancea, Mei-Ching Chen, and George Chacko, “NIH Peer Review: Scored review criteria and overall impact,” American Journal of Evaluation, Apr. 2015.
• Kevin W. Boyack, Mei-Ching Chen, and George Chacko, “Characterization of the peer review network at the Center for Scientific Review, National Institutes of Health,” PLOS ONE, Aug. 2014.