Virginia Tech National Security Institute (VICEROY DoD)
Developing Advanced ML Models for Cybersecurity in Autonomous Vehicles:

• Cybersecurity Model Development: Created Deep Q-Network (DQN) and Reinforcement Learning (RL) models to enhance cybersecurity in autonomous vehicles, improving threat detection accuracy by 45%.
• Human-Computer Interaction Optimization: Enhanced model accuracy, reducing infrastructure costs by 30% and boosting system performance by 40%.
• High-Impact Research: Led research initiatives and prepared publications for prominent AI and cybersecurity conferences, collaborating with experts at Virginia Tech.
• Collaboration with National Security Entities: Worked closely with national security stakeholders to develop and deploy innovative AI solutions in the field of cybersecurity.
• Comprehensive Tool Usage: Leveraged advanced ML and AI tools, including PyTorch, TensorFlow, and AWS for secure data management and analysis.

Amazon Web Services (AWS)
Creating Advanced ML Research for AWS Cloud Radio Frequencies:

• Collaborative Sensing: Created multiple Neural Networks to aggregate a Fusion Model on the AWS Cloud, achieving diverse radio frequency sensing using Amazon Sagemaker, EC2, and Amazon S3.
• Developing Radio Fingerprinting Technologies: Utilizing AWS SageMaker and PyTorch frameworks for innovative model training and deployment.
• Strategic Collaboration with the National Security Institute: Producing cutting-edge research outcomes for AWS offerings to the Department of Defense and government clients, focusing on RFML and national security applications.
• Proof of Concept and Tool Development: Building AWS RFML tools, enabling AWS to provide RFML as a service for government defense clientele.
• Comprehensive Use of AWS and ML Tools: Leveraging AWS IoT Core, AWS Greengrass, AWS Kinesis, Amazon Redshift, AWS QuickSight, AWS CloudWatch, AWS IAM, and AWS KMS for secure and efficient data management and analysis.

Milestone Technologies, Inc.
Developing a Large Language Model (LLM) for internal employees and external clients, utilizing vector databases, Retrieval-Augmented Generation (RAG) pipelines, and transformer architectures. Hands-on with fine-tuning, prompt engineering, and embedding optimization. Impacting over 5,000 employees and key clients, the project enhances knowledge management, automated decision-making, and client interactions through advanced NLP, ML, and AI technologies. Key areas include scalable AI infrastructure, data-driven insights, and seamless communication.

SCMworx Business Solutions B.V.
Developed an AI model aimed at revolutionizing risk management in supply chain operations. Key responsibilities included:

• AI Model Development: Leading the design and implementation of an AI-driven solution to identify and mitigate supply chain risks.
• Strategic Oversight: Overseeing the project lifecycle from ideation to deployment, ensuring alignment with business objectives.
• Data-Driven Decision Making: Utilizing analytics to inform model development and refine risk management strategies.
• Cross-Functional Collaboration: Coordinating with engineers, data scientists, and stakeholders to ensure cohesive product development.
• Market Analysis: Conducting thorough market research to continuously adapt and innovate in response to industry trends and needs.