Lu Tang

Lu Tang is an Associate Professor and Vice Chair for Education in the Department of Biostatistics and Health Data Science at the University of Pittsburgh School of Public Health. Her research bridges biostatistics and machine learning to advance health data science through integrative analysis of distributed and high-dimensional datasets, with significant applications in precision medicine, opioid use disorder, sepsis treatment, and public health policy.

Dr. Tang's educational journey includes foundational training at Sun Yat-sen University followed by advanced degrees in quantitative fields:

• PhD in Biostatistics, University of Michigan (2018)
• MS in Statistics, University of Virginia (2013)
• BA in Mathematics, University of Virginia (2012)

Her research program develops statistical methodologies for data integration, causal inference, and decision-making in healthcare settings. She pioneers techniques for federated learning, robust individualized treatment rules, and high-dimensional data analysis, directly addressing challenges in health disparities, electronic health records, and clinical trial heterogeneity. Her work consistently translates methodological innovations into practical tools for biomedical researchers.

Analysis of Dr. Tang's publication trajectory reveals a strategic evolution toward solving real-world healthcare data challenges through distributed learning frameworks. Her recent work emphasizes robust decision rules for heterogeneous populations (2022-2025), transfer learning for federated systems (2024), and methodological innovations for longitudinal and high-dimensional data (2019-2023), demonstrating consistent contributions across biostatistics, machine learning, and public health domains.

Dr. Tang has been recognized with:

• IMS New Researchers Travel Award (2023)

She actively mentors 6 current graduate students across PhD and MS programs while supervising 8 alumni now in industry and academia. Her research program is sustained by major funding including NIH R21DA055672 (PI, 2023-2025) on federated Medicaid data analysis, NSF DMS 2310217 (PI, 2023-2026) for longitudinal missing data methods, and multiple NIH R01 grants as Co-I addressing opioid treatment equity and precision medicine in sepsis.

Dr. Tang leads the development of critical open-source software including RISE (Python) for robust individualized decisions, ifedtree (R) for tree-based federated learning, and metafuse (R/CRAN) for data integration, establishing infrastructure widely adopted in health data science research communities.