Welcome to my website!
Deepak Bastola here, someone who is deeply passionate about the interplay between statistics and the mysteries of the cosmos. My educational background includes a Ph.D. in Applied Statistics and an M.S. in Astronomy and Astrophysics, both of which have equipped me to delve deeper into my quest for understanding. Throughout this academic path, I’ve been fortunate to employ statistical approaches in an effort to better understand physical processes and to create meaningful data interpretations. My primary research areas are Bayesian statistics, time series methodology, statistical computation, and machine learning.
Bayesian Statistics & MCMC
Hierarchical conjugate modeling, Forward Filtering Backward Sampling (FFBS), and variational uncertainty quantification.
Time Series & Econometrics
Multivariate state-space filtering, sparse long-horizon Transformers, and curved implied volatility manifolds.
Astro-Statistics & Physical Modeling
Global Sobol sensitivity indices, coalition-game Proportional Marginal Effects, and exoplanet transmission spectroscopy.
Statistical Machine Learning
Spectral graph theory, multi-relational document networks, and physics-informed neural architectures.