Assistant Professor,
Department of Mechanical Engineering

Emerging technologies fail for the lack of disciplined uncertainty management. Data-driven, uncertainty-aware methodologies are developed that tell engineers and program managers where uncertainty matters the most and where limited resources can be deployed for maximum de-risking impact.
UQ & Sensitivity
Propagate component-level uncertainties through multidisciplinary models. Identify dominant variance drivers via global sensitivity analysis.
AI-Accelerated DSS
Replace expensive MDO loops with physics-informed surrogate models for near-real-time decision support systems.
Risk-Informed Certification
Create auditable, quantitative trails linking uncertainty to compliance.
UG Final Year (1 sem)
Focused module, technical report output, code repo.
ME / MTech Thesis
Full project ownership, thesis, 1 conference paper draft.
PhD (3-5 yrs)
Original pillar contributions, peer-reviewed journals, mentoring.
"For a successful technology, reality must take precedence over public relations, for nature cannot be fooled."
Richard Feynman
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