NAGENDRA SOMANATH

Engineering expert with over 30 years of experience in aerospace, defense, and advanced technology systems at Raytheon, GE, and NASA. Demonstrated success in delivering innovative solutions through AI/ML integration and systems engineering. Proficient in translating complex technical requirements into actionable insights for mission-critical applications. Strong project management and problem-solving skills, with a focus on effective communication and teamwork to achieve successful project outcomes.

• Senior Engineer, Raytheon Technologies - Pratt & Whitney, 01/01/06, 12/31/12, Developed hierarchical integrated PMDO framework for aircraft engine architecture and optimization., Created hybrid response surface models for predictive assessment of multi-dimensional design spaces., Implemented physics-based modeling approaches for turbomachinery component architecture design.
• Senior Research Engineer, GE Corporate Research & Development, 01/01/98, 12/31/02, Developed successive optimization processes using generalized discrete-continuous variables., Created probability-based techniques to minimize variability in composite shell structures., Led design tool development for turbine blade life extension, improving high-temperature blade life by 4.5x.
• Lead Engineer, GE Corporate Research & Development, 01/01/94, 12/31/98, Designed energy storage systems for hybrid electric vehicles with 20kW storage capability., Developed discrete optimization algorithms for lightweight design of laminated solar panels for satellites.

• Ph.D., Mechanics, Virginia Tech (VPI&SU)
• M.S., Engineering Mechanics, Virginia Tech (VPI&SU)
• B.Tech., Aeronautical Engineering, Indian Institute of Technology (IIT) Bombay
• Fellow, Connecticut Academy of Science and Engineering, 2020

ANSYS, UG, iSIGHT, STAGS, EAL, PASCO, Neural Networks, Deep Learning, Genetic Algorithms, Statistical Methods, Design for Six Sigma, FMEA, Probability-Based Techniques, Sensitivity Analysis, MDO, Response Surface Methodology, Discrete-Continuous Variables, Successive Approximation, Composite Structures, Advanced Alloys, Thermal Systems, Electromagnetic Systems

• Created neural network approach for real-time monitoring of test rig data, saving $2M+ per test.
• Developed machine learning systems for material tailoring, saving $4M per alloy implementation.
• Implemented hierarchical optimization framework for engine architecture, reducing design cycle time by 30%.
• Improved high-temperature turbine blade life by factor of 4.5 using statistical methods.
• Designed robust composite structures for extreme environments including magnetic fields and thermal loads.
• Published 50+ papers in machine learning, deep learning, multidisciplinary optimization, and computational mechanics.

Computational Mechanics, Aero-propulsion Systems, Composite Structures, Physics-Based Modeling, Neural Network Implementation, Anomaly Detection Algorithms, Predictive Analytics, Machine Learning Applications, Multidisciplinary Design Optimization, Technology Portfolio Management, Cross-Disciplinary Integration, System Validation & Verification, Uncertainty Quantification, Structural Reliability, ACE/Six Sigma Methodology, Failure Mode Analysis

Citizenship: U.S. Citizen