Define and execute on the vision for the future of airbreathing engine mission simulation for life limited parts (LLPs) and allied hardware, including traditional part lifing, as well as off- and on-board real time parts life tracking programs. Identify opportunities to create new simulation technologies for hot gas path and structures hardware to improve accuracy and reduce risk from sources of variation in the field.Educate and drive adoption of machine learning techniques in traditional engineering spaces.

As the Continuous Improvement focal for applied lifing identified opportunities to implement standard work, tool improvements, and quality-of-life updates for many life processes, especially in the simulation and probabilistic lifing space. Worked across the life management community to empower individual contributors to implement and democratize their own CI projects. Coordinated and cross-pollinated ideas and tools across the methods, mission analysis, and multiple applied lifing sites. Led a process and value stream mapping of the powder metal probabilistic process.Collaborating with the mission analysis and digital teams architected and developed the Automated Mission Analysis (AMA) processes for the F110, F101 and F118 engine lines and supported the implementation of a simpler AMM system for the F414 on USN customer servers.Co-developed GE Aviation's first onboard near real-time damage system (nRTD) for the T901 engine program. Ideated and wrote a reduced order implementation of GEA's transient thermal algorithm for use onboard for LM9000 test engines in less than a month.Maintain and upgrade numerous GEA lifing tools including: LASTS tool suite (mission simulation), MISSYDD (PM lifing), PDS (simplified probabilistic fracture mechanics), and work across organizational boundaries to support blade lifing, clearances, performance and control organizations by leveraging life management technology. Technical owner and model reviewer for the LASTS tool suite, technical owner of the MISSYDD tool.Stay in touch with the literature and new technology on the horizon. Drove the initial adoption and expansion of Python into life management. Partnered internally and with outside organization to develop and give training for Python and various machine learning technologies. Actively mentor other engineers on SIESTA, programming, and engineering in general; actively learn from those around me.

As a Lead and then Senior Engineer in Life Management's Life Methods team I worked to advance Aviation's reduced order modeling and simulation technology to reduce analysis time and increase derived value from customer-derived data. Developed multiple demonstrator and fielded Prognostics and Health Management programs for life-limited parts. Built strong relationships with the Mission Analysis subsection being the major customer for mission simulation tools and models.Worked closely with the probabilistic lifing focals to advance GE Aviation's internal probabilistic lifing tool technology. Developed a simplified probabilistic lifing tool suite to improve probabilistic lifing capability and capacity.Led the physics-based modeling and analytics tools adoption sub-committees for the Analytics design boards (DB22/43). Active participant in Life Management Design Board activities (DB21).In my life methods role I pioneered the use of machine learning techniques (neural networks, Principal Components Analysis, EM clustering, hybrid Bayesian modeling, and random forests) across a variety of areas including PCA for automated statistic representative mission selection and neural networks for cycle deck simulation.Actively work with the intellectual property community to monetize GE's IP portfolio and develop next-generation technologies for the Aviation business.Mentor new engineers technically and instruct the ACE Elasticity module.

Rotated through a series of assignments including Combustor Methods, Rotating Parts Life Methods, Internal Audit, and Advanced Preliminary Design.

Performed a variety of tasks related to maintenance and installation of large-scale ammonia-based refrigeration systems.