• Developed and deployed robust data pipelines to extract, preprocess and visualize terabytes of mesh-based data, enabling the efficient training of distributed deep learning models on high-performance computing clusters• Architected and standardized MLOps/DevOps environment for growing team, leveraging MLflow and CI/CD pipelines to orchestrate Docker/Singularity image builds, Python wheel creation, automated testing, and model training workflows• Lead four diverse mission-critical machine learning initiatives, effectively leading cross-functional teams of 3-6 engineers through Agile methodologies, to enhance Air Force Research Laboratory’s operational workflow• Engineered an advanced predictive model to forecast timesteps of complex physics simulations, and developed an automated and comprehensive visualization suite to provide actionable insights into the system’s internal dynamics• Implemented and refined our generation processes for simulation data, actively monitoring and logging dataset metadata for class imbalances, sampling, and overgeneralization biases to improve and maintain model performance• Leveraged advanced statistical methods to develop and implement machine learning approaches, contributing to novel solutions resolving multi-dimensional correlation and uni-dimensional uniformity issues in our sampling methods• Authored multiple awarded proposals securing over $1 million in contract funding. Presented findings to government clients showcasing the impact of our machine learning solutions on mission-dependent decision-making

• Engineered and delivered a comprehensive curriculum for an introductory applied statistics course, leveraging a mix of lectures, lab work, and projects to enhance the educational experience for a cohort of 20 undergraduate students• Played a key role in the planning and delivery of a SAS-centric course, focusing on data manipulation techniques and algorithms crucial in statistical analyses, resulting in a 15% improvement in student project quality• Directed academic advisement for a diverse group of 150+ students in statistical inference resulting in 95% of advised students successfully completing projects that involved machine learning-relevant analytics and statistical models

• Designed and executed a data-centric business model and growth strategy, which increased subscription numbers by 15%• Expanded and maintained partnerships through lead generation, database management, and community events• Implemented the business model and growth strategy through a data-centric approach to maximize customer retention

• Managed and maintained a critical database, streamlining data entry and developing rigorous quality checks• Implemented systematic processes for data verification and correction, enhancing reliability for key business decisions • Orchestrated an independent healthcare study on the executive talent in Florida; delivered actionable insights through data analytics to influence Q1 2018 operations, resource allocation, and project prioritization in healthcare and medical sector

• Designed an automated image analysis pipeline using MATLAB, employing statistical algorithms to quantify target concentrations, thereby enabling faster and more scalable data analysis for neuroprosthetics research• Analyzed and quantified large datasets of temporal neural data through digital signal processing software