Alokesh Manna

News

Matthew Goldstein Foundation Fellowship, Summer 2025, UCONN
2025 Symposium on Data Science & Statistics – Student & Early Career Scholarship, American Statistical Association (ASA)

Hi, I am Alokesh Manna, a current 4th-year PhD Candidate in the Department of Statistics, at the University of Connecticut. My advisors are Dr. Dipak Dey, Dr. Neil Spencer, and Dr. Sujit Ghosh (NCSU).

My primary research focus is on spatial analysis (images of cancer cells, environment, and forensic footwear), Time series analysis and modeling, Computer vision, High-Dimensional Analysis, Conformal analysis, Deep learning, Generative Modeling, Bayesian modeling, computation, and theory development. I have worked in environmental data, stock market prediction, forensic analysis, footwear image data (spatial), and claim data modeling. Recently, I have been working on Uncertainty Quantification in Image Segmentation and Classification using deep learning models.

I do collaborative research and lead different projects including my RA in Travelers with my statistical knowledge and computational skills. I firmly believe in bridging the gap from theory to practical application and ultimately integrating valuable insights from collaborative research into real-world applications.

I have included my research works in Google scholar. A brief description of my current projects is here. I've included my featured publications below.

Email: alokesh[dot]manna@uconn.edu . Find my CV.

Publications:

1. Manna, A. & Ghosh, S. K. (2025). Bayesian models for joint selection of features and auto-regressive lags: Theory and applications in environmental and financial forecasting. Bayesian Analysis [Under review]. https://arxiv.org/abs/2508.10055

2. Manna, A., Spencer, N., & Dey, D. K. (2025). Point process modeling of accidentals in forensic shoeprint data. Annals of Applied Statistics [Under review]. Poster

3. Manna, A., Sett, A., Dey, D. K., Gu, Y., Schifano, E., & He, Jichao (Travelers Insurance) (2025). Distribution-free inference for LightGBM and GLM with Tweedie loss. Applied Stochastic Models in Business and Industry [Under review]. https://arxiv.org/abs/2410.01008

4. Manna, A., Mehan, S., & Amatya, D. (2025). Development of a statistical predictive model for daily water table depth and important variables selection for inference. Environmental Modelling & Software [Under review].Poster https://arxiv.org/abs/2410.01001

5. Manna, A., Huang, Z., Dey, D. K., Gu, Y., & He, Robin (Travelers Insurance) (2025). Interval estimation of coefficients in penalized regression models of insurance data. In Decision Sciences and Data Analytics for Operations and Business Excellence. Springer. https://arxiv.org/abs/2410.01008

6. Dawn T., Roy, A., Manna, A., & Ghosh, A. K. (2025). Some clustering-based change-point detection methods applicable to high-dimensional, low sample size data. Journal of Statistical Planning and Inference, 234, 106212. https://doi.org/10.1016/j.jspi.2024.106212