Statistical Data Science @ Indian Statistical Institute, Kolkata
Statistics · Machine Learning · Scientific Computing · Research Engineering
I build technical projects where the method, code, experiments, and failures can be inspected — not just summarized in a résumé bullet.
ACTIVE · REBUILDING FOR REPRODUCIBILITY
A physics-first flood simulation project being rebuilt around a validated rainfall–runoff–routing pipeline, deterministic diagnostics, and a held-out benchmark before any learned surrogate is trusted.
Current focus: physics repair → scenario validation → benchmark → surrogate modelling
The original experiment produced degenerate flood maps. Rather than preserve attractive metrics, I froze the baseline and am rebuilding the scientific pipeline from the failure upward.
Statistical / optimization-based fraud detection built around asymmetric false-positive and false-negative costs rather than accuracy alone.
EXPERIMENTAL / HISTORICAL
An experimental Hindi-language modelling project spanning data preparation, tokenization, model training, and evaluation. I retain it as evidence of the full experimental process — including where the resulting model did not perform well enough to justify inflated claims.
statistical learning → reasoning under uncertainty
scientific ML → models constrained by real structure
quantitative research → hypotheses, validation, robust evaluation
LLM / model evaluation → measuring behaviour instead of trusting demos
research engineering → reproducible experiments and inspectable systems
For serious projects I try to make the chain visible:
problem
↓
assumptions
↓
implementation
↓
experiment
↓
result
↓
failure analysis
↓
reproduction
A project earns a prominent place here only when the evidence is strong enough to support it.
I'm moving toward stronger projects with:
- reproducible experiments,
- defensible evaluation,
- clean repositories,
- technical write-ups where they add evidence,
- and honest documentation of limitations.
Interested in quantitative research, applied ML, statistical computing, model evaluation, and research-oriented software.
GitHub is the lab notebook. The portfolio will be the index.