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The central theme in Dr. Mukherjee's research program has been to develop novel inferential methods for observational data, using Bayesian, frequentist and hybrid methods. Dr. Mukherjee’s interest over the years has shifted to integration of diverse data sources and scalable biobank data analysis. This includes integration of genetic, environmental and phenomic data. She is interested in principled analysis of electronic health records and other administrative healthcare data sources that were not designed for population-based research. One of her research themes is understanding selection bias across these diverse, heterogeneous data sources. Prior to joining Yale in August of 2024 as an inaugural senior associate Dean of public health data science and data equity and Ana MR Lauder Professor of Biostatistics, Dr. Mukherjee had a long and distinguished career at the University of Michigan where she was appointed asJohn D. Kalbfleisch Distinguished University Professor of Biostatistics (2023-2024), Siobán D. Harlow Collegiate Professor of Public Health (2023-2024), John D. Kalbfleisch Collegiate Professor of Biostatistics (2015-2023) and Chair of the Department of Biostatistics (2018-2024). She had several other significant leadership appointments at Michigan including an institutional appointment as the inaugural Assistant Vice President for Research for Research Data Services Strategy (2023-2024); Associate Director for Quantitative Data Sciences(2019-2024), Associate Director for Cancer Control and population Sciences (2015-2018) at the University of Michigan Rogel Cancer Center. She also held professorial appointments in Epidemiology and Global Public Health. Professor Mukherjee was actively engaged with the U-M Precision Health initiative, as well as the Michigan Institute of Data Science (MIDAS). Dr. Mukherjje served as the founding director of a flagship undergraduate summer program in big data from 2015-2024. She has supervised twenty doctoral students and three post-doctoral fellows. Dr. Mukherjee is a fellow of the American Statistical Association and the American Association for the Advancement of Science. Dr. Mukherjee has received numerous awards for her outstanding scholarship, service, and teaching at the University of Michigan and beyond. These include the Gertrude Cox award, the Adrienne Cupples Award, the Janet Norwood award, the Sarah Goddard Power award, the Karl E Peace Award, the Jerome Sacks Award, and the Marvin Zelen Statistical Leadership Award. In 2022 she was elected to the US National Academy of Medicine.

What do you do with Data Science?

Dr. Mukherjees’s research interests span statistics and data science methods for analyzing electronic health records, gene-environment interaction studies, data integration, data equity, shrinkage estimation, and the analysis of environmental mixtures. Collaboratively, she contributes to areas such as cancer, cardiovascular diseases, reproductive health, exposure science, and environmental epidemiology. With over 390 publications in statistics, biostatistics, medicine, and public health, Professor Mukherjee is globally recognized for her research contributions in integrating genetic, environmental and health outcome data. She is has served as the Principal Investigator on methodology grants funded by the National Science Foundation (NSF) and the National Institutes of Health (NIH). Dr. Mukherjee is passionate about diversifying the data science workforce and has served as the founding director of an undergraduate summer program on big data from 2015, training 357 undergraduate students, with 55% women and gender minorities, and 18% of students coming from historically excluded groups in STEM fields. She has supervised twenty two PhD students and three post-doctoral fellows as a major professor. Training the next generation of data scientists and early career researchers have been a lifelong commitment for Dr. Mukherjee. Following are five recent publications (CV contains complete list): 1. Yang Y, Dempsey W, Han P, Deshmukh Y, Richardson S, Tom B, Mukherjee B. Exploring the Big Data Paradox for various estimands using vaccination data from the global COVID-19 Trends and Impact Survey (CTIS). Sci Adv. 2024 May 31;10(22):eadj0266. doi: 10.1126/sciadv.adj0266. Epub 2024 May 31. PMID: 38820165. 2. Salvatore M, Kundu R, Shi X, Friese CR, Lee S, Fritsche LG, Mondul AM, Hanauer D, Pearce CL, Mukherjee B. To weight or not to weight? The effect of selection bias in 3 large electronic health record-linked biobanks and recommendations for practice. J Am Med Inform Assoc. 2024 May 14:ocae098. doi: 10.1093/jamia/ocae098. Epub ahead of print. PMID: 38742457. 3.Kundu R, Shi X, Morrison J, Barrett J, Mukherjee B. A framework for understanding selection bias in real-world healthcare data. Journal of the Royal Stat Society Series A. 2024 May 02. https://doi.org/10.1093/jrsssa/qnae039 4. Zhang G, Beesley L, Mukherjee B, Shi X. Patient recruitment using electronic health records under selection bias: a two-phase sampling framework. Annals of Applied Statistics. Epub ahead of print. https://imstat.org/wp-content/uploads/2024/02/AOAS2206-042R2A0-1.pdf 5. Gu T, Taylor JMG, Mukherjee B. A synthetic data integration framework to leverage external summary-level information from heterogeneous populations. Biometrics. 2023 Dec;79(4):3831-3845. doi: 10.1111/biom.13852. Epub 2023 Apr 4. PMID: 36876883; PMCID: PMC10480346

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