Heather Mattie

655 Huntington Avenue
Building I, 4th floor Room 421A
Boston, MA 02115
Email: hemattie@hsph.harvard.edu
Phone: (617) 432-5308



Dr. Mattie’s research focuses on the intersection between biostatistics, data science, and network science. Specifically, she has used network science and machine learning to study interactions in communities, as well as the development and application of artificial intelligence in healthcare research. Her research has also involved the notion of algorithmic fairness, in terms of an algorithm compounding inequities working against underrepresented or disadvantaged groups in society. Her work has found links between unhealthy weight control behaviors and the use of mobile dating applications, particularly in racial and ethnic minorities. She has developed methods that predict tie strength in a network, which assists in modeling the spread of disease and information. Additionally, her work has examined the potential for artificial intelligence to improve inference from data for care and population health, as well as the challenges related to bias and scalability in such models.

Dr. Mattie is involved in the diversity and inclusion efforts of the Biostatistics department and enjoys mentoring students.


A distributed approach to the regulation of clinical AI by T Panch, E Duralde, H Mattie, G Kotecha, L A Celi, M Wright, F Greaves. PLOS Digital Health1(5) (2022) Journal

Machine Learning for Acute Kidney Injury Prediction in the Intensive Care Unit by E R Gottlieb, M Samuel, J V Bonventre, L A Celi, and H Mattie. Advances in Chronic Kidney Disease 29(5) (2022)

Addressing the elephant in the room of AI clinical decision support through organization-level regulation by J Zhang, H Mattie, H Shuaib, T Hensman, J T Teo, L A Celi. PLOS Digital Health (2022) Journal

An interactive dashboard to track themes, development maturity, and global equity in clinical artificial intelligence research by J Zhang, S Whebell, J Gallifant, S Budhdeo, H Mattie, P Lertvittayakumjorn, M del Pilar Arias Lopez, B J Tiangco, J W Gichoya, H Ashrafian, L A Celi, J T Teo. The Lancet Digital Health 4(4) (2022) Journal

Best practices in the real-world data life cycle by J Zhang, RS McGinnis, J Symons, P Agapow, J Teo, C Paxton, J Abdi, H Mattie, et al. PLOS Digital Health 1:1 (2022) Journal

Performance of intensive care unit severity scoring systems across different ethnicities in the USA: a retrospective observational study by R Sarkar, C Martin, H Mattie, JW Gichoya, D Stone, LA Celi. The Lancet Digital Health 3:4 (2021) Journal

A pragmatic methodology for the evaluation of digital care management in the context of multimorbidity by E Lindemer, M Jouni, N Nikolaev, P Reidy, H Mattie, JK Rogers, LA Giangreco, M Sherman, M Bartels, T Panch. Journal of Medical Economics 24:1 (2021)

Edge Overlap in Weighted and Directed Social Networks by H Mattie and Onnela, JP. Network Science 9:2 (2021) Journal

Unhealthy weight control behaviors among a sample of U.S. journalists by A Tran, M Smallridge, B Cadavos, V Tran and H Mattie. International Journal of Eating Disorders (2020) Journal

“Yes, but will it work for my patients?” Driving clinically relevant research with benchmark datasets by T Panch, T Pollard, H Mattie, E Lindemer, P A Keane and L A Celi. npj Digital Medicine 3:87 (2020) Journal

Artificial intelligence and algorithmic bias: implications for health systems by T Panch, H Mattie, R Atun. J Glob Health. 9(2): 010318 (2019) Journal

A framework for predicting impactability of digital care management using machine learning methods by H Mattie,  P Reidy, P Bachtiger, E Lindemer, N Nikolaev, M Jouni, J Schaefer, M Sherman, T Panch. Population Health Management (2019) Journal

Turning the crank for machine learning: ease, at what expense? by T Pollard, I Chen, J Wiens, S Horng, D Wong, M Ghassemi, H Mattie, E Lindemer, T Panch. The Lancet Digital Health 1:5 (2019) Journal 

The “inconvenient truth” about AI in healthcare by T Panch, H Mattie, L A Celi. npj Digital Medicine 2:77 (2019) Journal

Dating app use and unhealthy weight control behaviors among a sample of U.S. adults: a cross-sectional study by A Tran, C Suharlim, H Mattie, K Davison, M Agenor, S B Austin. Journal of eating disorders 7:1 (2019) Journal

Understanding tie strength in social networks using a local “bow tie” framework by H Mattie, K Engo-Monsen, R Ling, JP Onnela. Scientific Reports 8 (2018) Journal


Current Courses:

BST 219: Core Principles of Data Science, Course website

ID 201: Core Principles of Biostatistics and Epidemiology for Public Health Practice, Textbook, Companion Website

BST 209: Collaborative Data Science in Healthcare

ECPE Innovation with AI in Health Care, Certificate Website

Past Courses:

BST 260: Introduction to Data Science, Course website

BST 261: Data Science II (Deep Learning), Course GitHub repository

BST 270: Reproducible Data Science

HDSC 325: Health Data Science Capstone Course


Harvard Chan News, Artificial intelligence could reshape public health, but obstacles abound (2019)

Managed Healthcare Executive, The Limits of AI in Healthcare (2019)

Unsung Women’s Project, Connecting the Data Dots: Heather Mattie Studies, Teaches and Lives Connection (2019)

MIT Technology Review, How Close Are You Really? (2017)

Social Media



Ph.D. Biostatistics, 2017, Harvard Graduate School of Arts and Sciences
S.M. Biostatistics, 2013, Harvard Graduate School of Arts and Sciences
M.S. Mathematics, 2010, Claremont Graduate University
B.S. Mathematics, 2008, University of La Verne