Altaf Ahmad

• •Led global clustering architecture for snap-ins enabling cross-region deployments with distributed service design.
• •Developed template management platform, enhancing efficiency for enterprise customers.
• •Implemented specialized operations in Workflow Builder including time-based executions like sleep-for, wait-for, and observability features.
• •Helped to build comprehensive developer platform and documentation leading to successful third-party integrations.
• •Enhanced system reliability through automated testing achieving 60% coverage across different microservices and 100% API coverage.
• •Established developer workshops and tutorials, contributing to successful third-party adoption.

Focused on applying machine learning techniques to climate modeling and prediction using spatial data patterns.

• •Developed deep neural networks integrating thermodynamics knowledge to model climate uncertainties.
• •Analyzed high-resolution IMDAA dataset with 12km spatial and 1-hour temporal resolution for regional climate patterns.
• •Created models incorporating multiple variables including geographical factors, historical data, and human-induced changes.
• •Implemented ANN models for accurate regional climate prediction at kilometer-scale resolution.

Worked on DST-sponsored research project RP215: "Space-time domain decomposition methods for non-linear Cahn-Hilliard equation and their implementations in parallel computers"

• •Implemented numerical solutions for non-linear Cahn-Hilliard equations describing phase separation in binary mixtures.
• •Developed MATLAB code to solve fourth-order partial differential equations with first-order time derivatives.
• •Applied Newton-Raphson technique to solve block tridiagonal systems of equations.
• •Implemented space-time domain decomposition methods for efficient parallel computation.