Subcontract to Fortrea

Subcontract with United Launch Alliance:•Advised on AI/ML architecture in AWS•Generated data specifications and designed data base structures for Data Management and MLops•Utilized Sagemaker asynchronous batch processing endpoints to integrate anomoly detection models into curating a data lake build with AWS Glue and AWS Athena

Contract with Dynamo:•Deployed my SAM-ML LLM enabled business development tool for their use

1099 work with Valid Evaluation:• Engineered and deployed a cutting-edge microservice leveraging natural language processing to automate compliance checks for innovation competition documents for the Army. This solution significantly reduced the need for manual review, enhancing efficiency and accuracy in compliance verification. Delivered this robust solution into production within three months, utilizing AWS, Python, API Gateway, Amplify, SageMaker, Lambda, Cognito, ECS, and Neo4j.• Advised on Large Language Model (LLM) deployment in AWS, development of vector databases, statistics on vectorized text, and Retrieval Augmented Generation (RAG)• Provided statistical and data engineering support

• Led consulting services in AI, ML, and Cloud technologies, resulting in optimized operations and improved decision-making through predictive sustainment strategies. • Generated a novel ML enabled business development tool for government contractors that involved development in AWS amplify, lambda, RDS, sagemaker, ECS, ECR, API Gateway, Cognito, and Cloud Watch• Implemented Retrieval Augmented Generation and novel statistical text vectorization for matching new opportunities to relevant past performance on contract• Utilize LLM reason chain to develop technical depth evaluation rather than just technical cluster line up

• Led technical teams in responding to RFPs, overseeing the creation of tailored technical volumes that increased the company’s win rate.• Developed innovative methods to mine past performance data using text vectorization, vector databases, and privately hosted large language models (LLMs) on Azure, improving the ability to respond to specific RFP requirements.• Built a talent-matching system utilizing text vectorization and career website scraping, creating a searchable repository of talent aligned with key personnel needs in RFPs.• Performed permutation analysis on HTRO scorecards to help business development managers optimize subcontractor partnerships for improved proposal scores using python.• Collaborated with the Executive Director of the Technical Center, leveraging NLP for contract analysis and hiring optimizations through deep statistical analysis. Regularly presented data-driven insights to senior leadership for strategic decision-making.• Led a bioinformatics initiative for WRAIR, using Transformer models to cluster DNA sequences and advance predictive biology and bioinformatics applications.• Secured a $200k contract with USHUS by rapidly prototyping a data pipelining solution and crafting a successful proposal. Engaged in similar contract negotiations with WRAIR.• Spearheaded the Data Science as a service by consulting to steer a missile warning study to develop a novel machine learning algorithm to utilize physics informed neural networks to predict the most likely target of a Hyper-Glide Vehicle utilizing python and AWS

• Managed 23 professionals across five teams, including data science, system engineering, cloud services, modeling and simulation, and IT help desk support.• Hired and managed cross-functional technical teams consisting of data scientists, network engineers, cybersecurity professionals, systems engineers, software developers, and test engineers.• Resolved employee disputes, ensured training opportunities, addressed work blockages, and coordinated with customers to meet organizational needs and technical objectives.• Provided technical oversight for a development of an “on premises” cloud solution to include resolving issues with virtual machine architecture, Authority to Operate (ATO) documentation, and advising on hardware for machine learning

• Expanded the data science team by hiring three additional data scientists and overseeing complex projects across data science, systems engineering, and modeling.• Investigated part failure in the GBR Cobra Dane system, using Monte Carlo simulations, hypothesis testing, computer vision, NLP, regression analysis, clustering algorithms, and Markov chains, which resulted in $5 million annual savings.• Developed an LSTM model that predicted unscheduled maintenance of radar systems, enabling teams to anticipate and prevent system downtime.• Generated data strategies to address siloed data across the organization and built new data models to optimize data collection and usage.• Developed a full-stack C# application hosting multiple business functions for SZQ, including cost estimation, task repositories, career planning, and in/out processing modules.• Validated and verified missile warning models, enhancing confidence in the accuracy of modeling and simulation results for testing purposes.

• Delivered monthly training sessions on data science topics, covering neural networks, gradient descent, backpropagation, and activation functions, improving data literacy across the organization.• Performed agile data analysis using AWS RTC to identify unlogged issue shifts between sprints, which hindered project estimation and timely feedback loops. Improved estimation efforts by addressing these issues through data engineering.• Developed a full-stack application and data pipeline to improve financial data analysis within government departments, using zero-shot classification models to match cost actuals with estimates and automating file organization to save significant time for financial staff.• Conducted technical analysis on SPEARR data pipelining systems, advising the government on project feasibility and guiding the selection of optimal technical solutions.• Automated the collection and evaluation of Monthly Status Reports (MSRs) through a Python algorithm integrated with Google Cloud, streamlining the reporting process and improving report quality.• Analyzed Ground-Based Radar (GBR) PARCs' historical sustainment data to identify seasonal trends, resulting in an updated data model to enhance future predictions. To accomplish this, data points were classified with random forest decision trees.• Conducted novel analysis of workplace satisfaction and safety data, generating a survey that identified teams in need of intervention, improving organizational cohesion.• Built a Python and Flask-based RFI/RFP parser using REGEX to format documents into Excel, deploying the solution with Docker and CI/CD pipelines on Google Cloud Run, significantly reducing manual workload.

• Spearheaded the automated generation of comprehensive written reports and oral presentations, conveying critical findings from theoretical studies, lab investigations, and field experiments on radio performance. • Utilized Builder and Sage for physics-based modeling to predict radio performance• Developed Python scripts to automate data pipelines, generating SQL or Postgres queries, conducting mathematical analyses, and producing graphical representations. This automation significantly improved the efficiency of data processing and analysis.• Analyzed and visualized over 1 million network packet data points under tight deadlines, providing critical insights into network status and performance using C++.• Crafted Python-based programs for extensive database cleaning and error checking, ensuring data integrity across millions of data points. This initiative significantly reduced data discrepancies and improved the reliability of data-driven decisions.• Delivered novel analyses to customers, integrating these insights into data pipelines for enhanced decision-making. Demonstrated agility by incorporating new analyses and delivering updated results within 24 hours.• Leveraged ETL to seamlessly translate Postgres databases into SQL formats, optimizing data storage and accessibility.• Created flexible, user-friendly data visualization scripts using the Dash Python library, enabling customizable data presentations and facilitating deeper data exploration.• Enhanced data processing speeds by utilizing C++ for parsing network packet capture data, opening new avenues for business and funding through improved operational capabilities.• Worked closely with stakeholders to understand their data analytics needs and regularly presented data-driven insights to guide strategic decisions. My presentations facilitated a clear understanding of complex data, ensuring alignment with organizational goals and fostering informed decision-making.

• Employed advanced techniques in super-resolution microscopy, optogenetics, and patch clamp electrophysiology to study neuronal events. Applied time series analysis and statistical hypothesis testing using Python, SQL, JavaScript, and MATLAB for data processing.• Developed convolutional neural networks (CNNs) to identify synapses and analyze images from high-speed, high-resolution microscopy video, automating the quantification process and enhancing research efficiency.• Designed and implemented data pipelines to track and analyze sporadic events in neuron preparations, significantly improving accuracy and processing time for large datasets.• Received formal graduate training in advanced statistics and research ethics, providing a foundation for rigorous data analysis and ethical decision-making in scientific research.

• Leveraged computer science principles and ETL methodologies to develop MATLAB and Python scripts for the automated mathematical quantification, peak detection, and signal processing of electrochemical data. Enhanced existing quantification methodologies, significantly reducing inadequacies and inconsistencies in data analysis.• Integrated findings from laboratory experiments, theoretical studies, and existing literature to author and co-author scholarly articles, resulting in two first-author and one second-author publications. This work demonstrated my ability to synthesize complex research findings into compelling written communications.• Devised custom nonlinear regression models to analyze behavioral economics data, employing Bayesian analysis to highlight significant motivational differences between groups. Implemented these models in R• Characterized relationships between variables using correlation analysis and determined the magnitude of effects through chi-square and t-tests, leading to first-author publications. Employed hypothesis testing with One-way and Two-way ANOVA tests to uncover novel findings, underlining strong proficiency in statistical analysis.• Delivered insightful presentations at various local and national academic forums, effectively communicating complex conclusions and advising peers in the research community. Led a team of undergraduate researchers, guiding their work and development in the lab. This experience highlighted my leadership abilities and my skill in conveying scientific knowledge to diverse audiences.• Developed MATLAB and Python scripts for automated quantification, peak detection, and signal processing of electrochemical data, significantly improving accuracy and reducing inconsistencies in data analysis.• Applied statistical hypothesis testing, time series analysis, and Bayesian analysis using Python, MATLAB, and R to analyze behavioral economics data, resulting in multiple first-author publications.

Attended Infantry Assault Squad Leaders Course and led a squad of Marines during the second tour in Afghanistan. Participated in significant military operations, including the Battle of Sangin, demonstrating critical leadership skills under extreme conditions.Duties, Accomplishments and Related Skills:• Demonstrated exceptional leadership, resilience, and strategic thinking as a squad leader during the second tour in Afghanistan, often under high-stress and challenging conditions.• Successfully completed the Infantry Assault Squad Leaders Course, further enhancing leadership skills and tactical competencies.• Led and mentored a squad of Marines, ensuring their safety, fostering team cohesion, and achieving mission objectives.• Participated in the Battle of Sangin, one of the major operations in Afghanistan, exhibiting courage, tactical decision-making, and an ability to perform under pressure.• Developed and executed strategic plans and tactics in dynamic, uncertain environments, ensuring the successful completion of missions.• Made critical decisions under extreme pressure, demonstrating a high level of responsibility, judgment, and commitment to the mission and team.