Brief about myself
Welcome to my webpage! I am an Assistant Professor in the Department of Statistics at the University of California, Riverside. Prior to joining UCR, I was a Postdoctoral Associate in the Department of Biostatistics and Bioinformatics at Duke University, working with Sheng Luo.
I received Ph.D. in Statistics from North Carolina State University in 2021. My dissertation work primarily focused on developing inference techniques for functional data. I completed Masters of Statistics from Indian Statistical Institute, Kolkata in 2015. The entire credit of sparking my interest in the subject of statistics goes to Ramakrishna Mission Residential College, Narendrapur, Kolkata, where I received the Bachelors of Science degree in 2013.
My research lies at the interface of statistical methodology and biomedical applications. On the methodological side, I develop principled statistical and machine learning methods for complex, structured data, with a primary focus on functional data analysis. I work on data observed as curves, trajectories, images, or signals, including multivariate, longitudinal, and other second-generation functional data. My work emphasizes valid inference and uncertainty quantification in these settings, including hypothesis testing, conformal prediction, and high-dimensional variable selection. It increasingly integrates modern AI and machine learning tools with the theory of functional data.
Apart from functional data techniques, I am also interested to work on topics related to foundations to statistics such as Generalized fiducial inference (GFI), conformal predictions and apply them a toolbox to statistical learning. As a part of my dissertation work, I have developed an Epsilon-admissible subset (EAS) framework for variable selection in multivariate linear regression setting using a GFI approach. See the recently published article here.
On the application side, my research is motivated by questions in biomedicine and public health, particularly aging-related neurodegenerative disorders such as Alzheimer’s and Parkinson’s disease. I work with longitudinal clinical outcomes, neuroimaging, clinical trials, wearable device data, electronic health records, and genomic data. I am always happy to collaborate with scientists and clinicians who have data that fit these descriptions.
Current research interests
Statistics on second-generation functional/shape-object data : Develop projection-based tests for complex hierarchically structured functional or share object data, such as longitudinal functional data, multivariate functional data, and with an extended focus on inference from observational studies.
Machine learning on intensively collected digital bio-signatures data: Distribution-free inference methods for electronic health records and wearable device data for Alzheimer’s and Parkinson’s disease.
Analysis of longitudinally recorded clinical and brain imaging data: Statistical methods to integrate longitudinal brain imaging data with clinical data for early detection of Alzheimer’s disease.
- High-dimensional statistical inference: Research on Epsilon-admissible set methodology for variable selection in high-dimensional data.
Application areas
- Alzheimer’s and Parkinson’s disease’
- Inference from Multi-arm Clinical trials
- Neuroimaging and longitudinal clinical data
- Wearable devices and digital health
- Electronic health records and genomics
Prospective Students
I am looking for motivated Ph.D. students interested in functional data analysis, statistical machine learning, and biomedical applications. Please feel free to reach out by email.