Research Interests

My primary research focus is on Computational Statistics, particularly the modeling and analysis of tensor-variate data to tackle complex, high-dimensional problems such as functional MRI. I also work on spatial data modeling, creating efficient techniques to address spatially correlated data with the help of computational methods such as matrix-free methods.

My current work at Washington University in St. Louis focuses on using high-frequency naturalistic driving data as a passive digital biomarker to detect preclinical Alzheimer's disease in older adults, involving processing and modeling large volumes of telematics data collected over multiple years.

Beyond these areas, I also explore the integrating machine learning and deep learning to enhance the analysis of large datasets, and interested in text mining to extract patterns from unstructured data and apply these insights to real-world problems, including environmental science and collaboration with applications in different fields.

Awards and Honors

• 2026: First Prize, Datathon, I2DB Scientific Symposium 2026, Institute for Informatics, Data Science & Biostatistics, Washington University in St. Louis, jointly with Sayan Das, Ayoushman Bhattacharya, and Subhrajyoty Roy.
• 2025: Student Paper Award (2nd) from the American Statistical Association (ASA) Sections on Medical Devices and Diagnostics.
• 2023: Research Excellence Award, Graduate College, Iowa State University.
• 2023: Student Paper Award from the American Statistical Association (ASA) Sections on Statistical Computing and Graphics.
• 2023: Best Narrative (Statistics), International Cherry Blossom Prediction Competition, George Mason University, jointly with Aniruddha Pathak, Kunal Das.
• 2022: Vince Sposito Computing Excellence Award Department of Statistics, Iowa State University.
• 2022: Outstanding Student Presentation Award, Honorable Mention, 21st Conference on Artificial Intelligence for Environmental Science, American Meteorological Society Annual Meeting.
• 2013-2018: INSPIRE Scholarship, funded by the Department of Science & Technology, Govt. of India.

Guest Lectures

• Guest Lecturer: Multivariate Statistical Methods (STAT 5010), 2025S. Two lectures on statistical analysis of tensor-variate data. GitHub Link

Teaching Assistantship

• Grader: Introduction to Statistical Computing (STAT 579), 2022F.
• Lab TA: Principles of Statistics (STAT 101), 2018F, 2019S.
• Lab TA: Introduction to Business Statistics II (STAT 326), 2019S.
• Lab TA & Grader: Statistical Methods for Research Workers (STAT 587), 2018F.

Workshops

• 2023: Conducted "A Python workshop to bridge the gap between statistics and practical machine learning," with the help of Aniruddha Pathak, Gautham Venkatasubramanian, Benjamin Jacobs, and Federico Veneri Guarch. GitHub Link
• 2019: Conducted workshop on "Text analysis," jointly with Fan Dai, under the supervision of Prof. Somak Dutta and Prof. Ranjan Maitra. GitHub Link

Mentoring

• Mentoring Srika Raja on her project involving Partial PCA with Missing Data, focusing on guiding methodology, algorithmic implementation, and interpretation of results.

Below is my CV:

Publications

• Babulal, G. M., Blake, M., Chen, C., Zhu, Y., Pal, S., Brown, D. C. (2025). Operationalizing passive sensors into scalable, reproducible, neurobehavioral digital markers for Alzheimer's disease: Lessons learned over 10 years, Digital Health, 11: 20552076251404502. DOI
• Tirone, E., Pal, S., Gallus, W. A., Dutta, S., Maitra, R., Newman, J. L., Weber, E., and Israel Jirak, I., (2024). A Machine Learning Approach to Improve the Usability of Severe Thunderstorm Wind Reports, Bulletin of the American Meteorological Society, 105(3): E623-E638. DOI
• Pal, S., Dutta, S., Maitra, R. (2023). Fast matrix-free methods for model-based personalized synthetic MR imaging, Journal of Computational and Graphical Statistics, 33(3): 1109-1117. DOI
• Pal, S., Dutta, S., Maitra, R. (2023). Personalized Synthetic MR Imaging with Deep Learning Enhancements, Magnetic Resonance in Medicine, 89(4): 1634-1643. DOI
• Ray, S., Pal, S., Kar, S., Basu, A. (2022). Characterizing the Functional Density Power Divergence Class, IEEE Transactions on Information Theory, 69(2): 1141-1146. DOI

Preprints

• Ray, S., Pal, S., Kar, S., Basu, A. (2022). Characterizing Logarithmic Bregman Functions. (arxiv preprint)

Work in Progress

• Pal, S., Maitra R., ToTTR: Tensor-on-Tensor Time Series Regression for Integrated One-step fMRI analysis.
• Pal, S., Dutta S., Matrix Free computations for Spatial Functional Data.
• Pal, S., Dutta S., Matrix Free Analysis of Multivariate Spatial Data.

Software

• ToTR: an R package for Tensor-on-Tensor Regression with Kronecker Separable Covariance. GitHub Link, Contributing author
• symr: a C++, R, and Python package for model-based Synthetic Magnetic Resonance Imaging Program. GitHub Link, creator, maintainer, and contributing author
• DeepSynMRI: a Python package for Synthetic Magnetic Resonance Imaging Program using Deep Learning Enhancements. GitHub Link, creator, maintainer, and contributing author

Extra Curricular Activities

Beyond my academic pursuits, I have a deep passion for photography, painting, and creative arts. I find that engaging in these activities allows me to explore the world in a different light, enhancing my creativity! All images in this website are by Subrata Pal, licensed under CC BY-NC-SA.