• Led the beta-to-GA transition of Luminary's geometry and meshing platform, scaling from prototype to a production system supporting enterprise Physics AI workflows.

• Architected the interface between the geometry kernel and the Physics AI system — unifying traditional CAD/meshing infrastructure with AI-driven simulation for both training and inference.

• Operated as the full-lifecycle domain owner for geometry workflows in a high-ambiguity startup environment — defining requirements, building solutions, driving UX, and validating with customers across product, engineering, and design without formal role boundaries.

• Built real-time CAD processing pipelines with 90%+ automated repair success rates and a geometry tagging system that cut user configuration time by up to 90%.

• Designed a multi-kernel geometry processing architecture abstracting ACIS, Parasolid, EGADS, and an in-house discrete kernel — with IOP for cross-kernel CAD interoperability and parametric geometry ingestion from Onshape, ESP/OpenCSM, and Blender — enabling seamless history-based kernel switching and broad file format support.

• Served as the primary technical authority for geometry across the organization — advising developers, solutions engineers, customers, and executive leadership.

• Mentored 2 junior engineers (a Sutter Hill Ventures Codepoint fellow and a PhD graduate) through full development lifecycles, accelerating project delivery by 6 months.

• Recognized with three internal awards — Rigor Award, Hackathon Velocity Award, and Speak Up Award — for technical excellence, rapid prototyping, and cross-functional initiative.

• Designed a routing optimization algorithm achieving 15% reduction in warehouse travel distances, projecting $10M+ in annual cost savings.

• Built a modular discrete-event simulation framework for robotics fleet performance analysis, enabling data-driven optimization across warehouse locations.

• Led algorithm development reporting directly to the CTO, delivering measurable impact within a 4-month timeline.

• Selected for the competitive NASA Postdoctoral Program fellowship to advance mesh optimization and adjoint methods for aerospace simulation.

• Developed metric-aligned mesh generation algorithms using an Lp-CVT approach for complex geometry handling.

• Built an automated adjoint code generation framework from tangent linearizations, reducing manual development time by 80%.

• Implemented a high-order adjoint elasticity solver for mesh deformation optimization; contributed core algorithms to NASA's eddy computational framework.

• Optimized parallel computing through multi-core code parallelization; published peer-reviewed work on high-order mesh optimization.

• Led algorithmic development for Nektar++ and NekMesh — open-source frameworks for high-order computational methods now cited 267+ times across the literature.

• Developed automated mesh generation algorithms for 2D structured and 3D semi-structured configurations, including cross-field methods for quadrilateral block-structured meshing.

• Developed variational r-adaptation for adaptive mesh refinement in spectral element methods, and rp-adaptation to reduce computational cost in compressible flow simulations.

• Published 8 peer-reviewed papers (3 journal, 5 conference) and delivered 15+ technical presentations during doctoral studies.

• Secured dual competitive funding: Marie Skłodowska-Curie fellowship (EU Horizon 2020) and EPSRC grant; completed research secondments at ONERA (France) and San Diego State University (USA).

• Led interactive tutorial sessions for Introduction to Aerodynamics, teaching fluid dynamics fundamentals to 25+ first-year undergraduate engineering students.