Bibhas Chakraborty

Associate Professor


Contact: 66016502

A/Prof Bibhas Chakraborty is a tenured Associate Professor at the Duke-NUS Medical School, with a joint appointment at the Department of Statistics and Data Science, National University of Singapore (NUS), and an adjunct appointment at the Department of Biostatistics and Bioinformatics, Duke University, USA. Previously (2014-18), he served as the Director of the Centre for Quantitative Medicine and the Founding Co-director of the PhD program in Quantitative Biology and Medicine (QBM) at Duke-NUS.

Bibhas completed his bachelor’s and master’s degrees respectively from the University of Calcutta and the Indian Statistical Institute in Kolkata, India. Subsequently he completed a PhD in Statistics from the University of Michigan, Ann Arbor, USA, under the supervision of Prof Susan Murphy in 2009. He then worked as an Assistant Professor of Biostatistics at the Mailman School of Public Health, Columbia University, USA (2009-13), prior to his move to Singapore.

He has been the Principal Investigator (PI) of several grants funded by Singapore-based organizations, e.g., the Ministry of Education (MOE), the Institute of Mathematical Sciences at NUS, the Duke-NUS Medical School, and the A*Star. In addition, he has been the PI of grants from the National Institutes of Health (NIH) and the Patient-Centered Outcomes Research Institute (PCORI) from the USA.

He has served as a scientific reviewer for many prominent funding agencies across the world, including the NIH and the PCORI in the United States, the Medical Research Council (MRC) in the United Kingdom, the Netherlands Organization for Scientific Research, the French National Alliance for Life and Health Sciences, the Health Research Council of New Zealand, and the National Medical Research Council (NMRC) of Singapore. In addition, he has been an Expert Statistical Resource to the Institutional Review Board of the Singapore Health Services (SingHealth).

He is the recipient of several awards and recognitions, including the Calderone Research Prize for Junior Faculty from Columbia University’s Mailman School of Public Health in 2011, the Young Statistical Scientist Award from the International Indian Statistical Association (IISA) in 2017, and an Elected Membership of the International Statistical Institute (ISI) in 2022.


Research Interest
A/Prof Chakraborty’s primary research interest lies in developing novel statistical methods and associated study designs to facilitate data-driven precision health in a time-varying setting, often known as dynamic treatment regimens (DTRs) or adaptive interventions. Once developed, these treatment regimens can serve as data-driven decision support systems for healthcare providers. He has authored the first textbook on this cutting-edge topic that assimilated concepts from reinforcement learning, causal inference and precision medicine.

He is an expert in modern clinical and behavioural intervention trial designs, including the sequential multiple-assignment randomized trial (SMART) design for DTRs, the micro-randomized trials (MRTs) for developing just-in-time adaptive interventions (JITAIs) in mobile/digital health, full and fractional factorial designs in the context of multi-phase optimization strategy (MOST) for developing multi-component interventions, as well as various adaptive designs. In 2019, he organized a mobile health workshop funded and hosted by the Institute of Mathematical Sciences, National University of Singapore.

His other area of interest is the analysis of big electronic health records data using interpretable machine learning and other tools from artificial intelligence (AI).

His research has been supported by the Ministry of Education (MOE), the Duke-NUS Medical School, the NUS Institute of Mathematical Sciences and the A*Star from Singapore, as well as the NIH and the PCORI from the USA.

Here is his Google Scholar citation page.

Research Team

Kenny Jing Xu
Asst Professor

Yan Xiaoxi
Research Fellow

Yeung Kar Fu
Senior Research Assistant

Leung Utek
Research Assistant

Xueqing Liu
PhD student

Xinru Wang
PhD student


[A “*” denotes a student / mentee at the time of the work.]

1. *Ghosh P, *Yan X, and Chakraborty B (2023). A novel approach to assess dynamic treatment regimes embedded in a SMART with an ordinal outcome. Statistics in Medicine, in press

2. *Liu X, Deliu N, and Chakraborty B (2023). Micro-randomized trials: developing just-in-time adaptive interventions for better public health. American Journal of Public Health, 113(1): 60-69. (with commentary)

3. *Wang X and Chakraborty B (2023). The sequential multiple assignment randomized trial for controlling infectious diseases: A review of recent developments. American Journal of Public Health, 113(1): 49-59. (with commentary 1 and commentary 2)

[Selected as a Continual Medical Education (CME) offering of the American Journal of Public Health.]

4. *Maiti R, Li J, Das P, *Liu X, Feng L, Hausenloy D, and Chakraborty B (2022). A distribution-free smoothed combination method to improve discrimination accuracy in multi-category classification. Statistical Methods in Medical Research, DOI: 10.1177/09622802221137742.

5. Ning Y, Ong MEH, Chakraborty B, Goldstein BA, Ting DSW, Vaughan RD, and Liu N (2022). Shapley variable importance cloud for interpretable machine learning. Patterns, 3(4): 100452.

6. *Xie F, Ong MEH, Ning Y, Goldstein BA, Liu N, and Chakraborty B (2022). AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data. Journal of Biomedical Informatics, 125:103959.

7. Qian M, Chakraborty B, *Maiti R, and Cheung YK (2021). A sequential significance test for treatment by covariate interactions. Statistica Sinica, 31(3): 1353 – 1374.

8. Figueroa C, Aguilera A, Chakraborty B, Modiri A, Aggarwal J, *Deliu N, Sarkar U, Williams JJ, and Lyles CR  (2021). Adaptive learning algorithms to optimize mobile applications for behavioral health: guidelines for design decisions. Journal of the American Medical Informatics Association, 28(6): 1225-1234.

9. Ghosh P, Ghosh R, and Chakraborty B (2020). COVID-19 in India: State-wise analysis and prediction. JMIR Public Health and Surveillance, 6(3): e20341.

[This paper received extensive media coverage. The Government of India acknowledged this work on their social media pages.]

10. *Ghosh P, Nahum-Shani I, Spring B, and Chakraborty B (2020). Non-inferiority and equivalence tests in a sequential multiple-assignment randomized trial (SMART). Psychological Methods, 25(2): 185-205.

11. *Xu J, Bandyopadhyay D, *Mirzaei S, Michalowicz B, and Chakraborty B (2020). SMARTp: A SMART design for non-surgical treatments of chronic periodontitis with spatially-referenced and non-randomly missing skewed outcomes. Biometrical Journal, 62(2): 282-310.

[Included in the NIH/NIDCR Director’s Report to the US National Advisory Dental and Craniofacial Research Councilin Jan 2020 as a high-impact publication in Clinical Research.]

12. Simoneau G, Moodie EEM, Platt RW, and Chakraborty B (2018). Non-regular inference for dynamic weighted ordinary least squares: understanding the impact of solid food intake in infancy on childhood weight. Biostatistics, 19(2): 233-246.

13. Chakraborty B, *Ghosh P, Moodie EEM, and Rush AJ (2016). Estimating optimal shared-parameter dynamic regimens with application to a multistage depression clinical trial. Biometrics, 72(3): 865 - 876

14. Cheung YK, Chakraborty B, and Davidson K (2015). Sequential multiple assignment randomized trial (SMART) with adaptive randomization for quality improvement in depression treatment program. Biometrics, 71: 450 – 459.

15. Chakraborty B and Murphy SA (2014). Dynamic treatment regimes. Annual Review of Statistics and Its Application, 1: 447 – 464.

 [This article is among the most highly cited articles published in this journal.]

16. Chakraborty B and Moodie EEM (2013). Statistical Methods for Dynamic Treatment Regimes: Reinforcement Learning, Causal Inference, and Personalized Medicine. Springer, New York. ISBN: 978-1-4614-7427-2.

17. Chakraborty B, Laber EB, and Zhao YQ (2013). Inference for optimal dynamic treatment regimes using an adaptive m-out-of-n bootstrap scheme. Biometrics, 69(3): 714 - 723.

18. Moodie EEM, Chakraborty B, and Kramer M (2012). Q-learning for estimating optimal dynamic treatment rules from observational data.  Canadian Journal of Statistics, 40(4): 629 – 645.

19. Chakraborty B, Murphy S, and Strecher V (2010). Inference for non-regular parameters in optimal dynamic treatment regimes. Statistical Methods in Medical Research, 19(3): 317 – 343.

20. Chakraborty B, Collins L, Strecher V, and Murphy S (2009). Developing multicomponent interventions using fractional factorial designs. Statistics in Medicine, 28(21): 2687 – 2708.