WORK EXPERIENCE

USDA Forest Service Oct 2022 - PRESENT

Artificial Intelligence Solutions Architect

• Led the development and implementation of Artificial Intelligence (AI) / Machine Learning (ML) strategies and solutions within the USFS Chief Information Office (CIO), establishing governance, planning, and technical engagement frameworks.

• Pioneered the first AI Project Management role, setting priorities, managing schedules, and providing technical guidance on analytical coding, statistical analysis, and visualization development.

• Proposed, developed, and taught an AI Boot Camp course to over 250 USFS FTEs, fostering an "AI Savvy" workforce after developing a curriculum for STEM engagements with junior high and high school students. The AI course provided six (6) lectures on topics ranging from fundamentals to ethics and included GCP hosted Colab sessions with active code execution for all students.

• Conducted research on Large Language Models (LLMs) with OpenAI and Gemini / Bard, advising upper management on technical matters and implementing Research and Development solutions.

• Conducted data cleaning and visualization efforts along with exploratory data analysis.

• Served as a member of the United States Department of Agriculture (USDA) Generative AI Review Board (GAIRB) and the Forest Service AI Council, actively developing use cases, and providing technology guidance.

• Led the development and implementation of twelve (12) AI-powered solutions, ranging in scope from enterprise-level to data analysis, utilizing Natural Language Processing (NLP) techniques and tools like TensorFlow, PyTorch, and spaCy.

• Performed data curation, cleansing, prompt injection defense, Named Entity Recognition (NER), and data preparation to transform raw data into AI-ready formats.

• Conducted topic analysis, sentiment analysis, and categorization of public comments using both machine learning (ML) and Generative AI (GenAI) methods.

• Supported Robot Process Automation (RPA) efforts, contract development, promoted Agile perspectives, container standardization, and pioneered GitHub Code spaces integration.

• Initiated and steered two major innovations for dynamic cloud storage (Globus) and dynamic compute (CyVerse.org) to meet enterprise-scale needs with both efforts recognized as being successful.

Naval Research Lab (NRL) Nov 2018 - Oct 2022

Computer Scientist

• Provided software solutions to Principal Investigators in support of ocean color algorithm development, sensor calibration / validation, atmospheric correction, and time-series analyses.

• Pioneered ML initiatives within the division, publishing a poster using Multivariate Regression analysis and actively supporting two ML efforts related to Logical Classification using Computer Vision and Multivariate Regression Analysis.

• Supported transitions to Navy end-users across four projects, including architecture design, documentation, test criteria, requirements management, and on-site support.

• Developed software using DI2E, Atlassian software management and development infrastructure, in Agile software development on Windows, Linux, and super-computing platforms, utilizing languages like Fortran, C, IDL, Python, and Machine Learning.

• Established and refined internal NRL standards to ensure Department of Defense (DoD) security specifications were applied to solutions.

• Developed a TensorFlow / Keras based Convolutional Neural Network (CNN) to identify plankton species captured from an In-Situ Ichthyoplankton Imaging System (ISIIS) sensor and relate plankton classification to biomass counts.

• Utilized a grid search in a Python-based ML pipeline to iterate through 189 Sk-Learn multivariate regression algorithms and hyper-parameter perturbation to determine the most effective prediction for atmospheric correction of small satellites.

• Analyzed, modified, and integrated Absorption and Backscattering inputs to the Navy Coupled Ocean Data Assimilation (NCODA) application which was successfully transitioned.

• Created a Bash / Python based modular run-time system for an Optical Forecast Model (OFM) which merged five (5) disparate projects into a single cohesive modeling environment of a three (3D) dimensional volume operationally transitioned to the Naval Oceanographic Office (NAVOCEANO).

For a full work history read my resume.