Michael Stringer Email & Phone Number

Colorado Springs, CO, US

Salt Lake City, Utah, US

• Developed AI system to detect mental health and cancer symptoms in clinical review charts, identifying veterans needing care.
• Led ORNL-VA collaboration, processing 854,543,844 rows in 136 hours (1,748 rows/sec) using SQL server and HPC cluster in a secure offline environment with a combination of PySpark and multi-threaded SQL.
• Developed "prompt-ception" technique, using AI to create prompts for other AI models, improving overall accuracy, combining Claude 3.5 and GPT-4o outputs for optimal accuracy.
• Implemented open-source LLaMA 3.1 70B model, achieving 94% accuracy (within 1% of GPT-4o) on 13,628 rows in 19 hours with an custom PyTorch NVIDIA CUDA environment.
• Executed dictionary lookup at 33.5M words/second, demonstrating exceptional optimization skills.
• Managed secure data transfer and processing in high-security offline environment.

Armonk, New York, NY, US

- Established standard operating procedures as a Data Scientist and Scrum Master to guide effective data science collaborations, ensuring consistency and efficiency in project delivery.- Conducted exploratory data analysis on Long COVID data, uncovering valuable insights into incidence and prevalence rates across veteran populations to inform targeted.

Overlanding, catching up with friends, studying LLM's and AI, Building computer networks.

Reston, VA, US

SAIC acquire of Halfaker-Prototype daily runtime automation of NLP models into production for Text analytics of Mental health determine factors and cancer care tracking-Leveraged a secure Docker container provided by Microsoft, adding essential packages to create a robust foundation for a Spark-Standalone cluster. Developed a custom Python loop wrapper to.

– Performed training in MaskRCNN-benchmark using 18GB / 118k images with 48% the speed of a Nvidia DGX-1 box at 22% of the price on custom cluster – Competed in Kaggle Steel Defect Detection (Computer Vision) competition with a solo PyTorch program incorporating transfer learning for the base classifier. Achieved 86.7% accuracy, Top model was 90.9%–.

Reston, Virginia, US

– Helping Veterans with the Azure Cloud

Reston, Virginia, US

– Adapted Nvidia NeMo library into the Azure ML environment. Enabling a single easy to use library for Natural Language Processing (NLP) simplifying training of United States Department of Veterans Affairs (VA) employees and quickening collaboration across customer task. – Incorporated transfer learning of Nvidia BioMegatron a state-of-the-art (SOTA).

Ashburn, Virginia, US

– Implemented Amazon SageMaker Neural Topic Modeling (NTM) unsupervised learning algorithm for group name predictions from a 1.37 million record data set. – Developed a new data cleaning strategy coupled with the NTM algorithm boosted prediction accuracies from 40% up to 59.7% – Researched and Developed RoBERTa implemented in Amazon SageMaker. RoBERTa is a.

– Transfer learning using TensorFlow object recognition to analyze drone footage of cellphone towers for mechanical defects – Implementation of Nvidia - Docker with TensorFlow for easy deployment to AWS SageMaker allowing of efficient scaling up of systems – Networked and optimized in house cluster to train neural networks, saving hundreds of dollars in.

Arlington, TX, US

– Programmed computer control systems to automate the cleaning of sensitive laboratory equipment – Helped multiple teams overcome difficult design problems, leading them to project completion and ensuring budget requirements were met– Self-directed, requiring limited supervisor involvement

Southlake, US

– Led a team for a complete design package of a concrete rail tie car. This included assembly drawings, weldment assemblies and sizing, and detailed part drawings. Car had a unique requirement of a rail alongside the deck for an overhead gantry crane that traveled between cars preforming track repair. Rail car is still in-service today– Programmed impact.

Arlington, TX, US

– Taught labs for Pro/ENGINEER and Creo Elements – Integrated Solidworks into previous graphics course – Teaching assistant for the new Solidworks program Lab at UTA

– Implemented a GIS data base, so jobs entered by the company are accessible with an easy to use interface (Esri ArcGIS)– Drastically reduced cost and optimized the performance of the field crew– Implemented a central file server for streamline file sharing and drawing modification – Worked alongside North Texas Water District as GIS Technician for.

Bachelor Of Science - Bs, Mechanical Engineering

Deep Reinforcement Learning, Artificial Intelligence

Deep Learning, Artificial Intelligence