WILLIAM C. STANFORD

Spearheaded full-stack AI product development and strategic AI integration for hazardous waste profiling, leading a team of engineers and fostering an AI-first culture.

• Developed and deployed full-stack AI products, leveraging agentic workflows, fine-tuned LLMs, and web-crawling tools to ensure customer compliance with federal hazardous waste regulations.
• Managed a cross-functional team of AI engineers and software developers, successfully delivering key roadmap features and enhancing product capabilities.
• Championed and integrated an AI-first culture across WasteLinq, identifying and implementing AI solutions to significantly improve operational efficiency and drive innovation.
• Initiated a comprehensive documentation culture, standardizing company philosophy and technical design to enhance organizational clarity and efficiency.
• Drove customer-centric product development through continuous interviews and live iteration sessions, ensuring product-market fit and user satisfaction.

Co-founded and led technical strategy for an AI startup, driving product innovation, achieving 40% feature adoption increase, and securing accelerator recognition.

• Secured selection into the prestigious Techstars’23 accelerator, gaining critical industry insights and validating the startup's potential.
• Drove product innovation by conducting extensive AI literature reviews, leading to development decisions that boosted feature adoption rates by 40% and established a competitive market advantage.
• Engineered and launched an AWS-based video generation pipeline, slashing video production time by over 97% (from 48 hours to under 1 hour per project) and accelerating content delivery.
• Developed and deployed advanced AI models, including fine-tuned LLMs, Diffusion models, custom LORAs, and bespoke video generation pipelines, leveraging state-of-the-art image and video technologies.

Conducted advanced neuroscience research, developing novel ML models and large-scale data processing pipelines for medical imaging and Alzheimer's disease.

• Authored and published research in four peer-reviewed journals, contributing significant findings to the neuroscience and AI communities.
• Developed and trained machine learning models using medical imaging data to accurately predict Alzheimer's disease progression and sub-types.
• Pioneered the application of Natural Language Processing (NLP) to natural language data, exploring its potential for early Alzheimer's disease diagnosis.
• Designed and implemented large-scale pipelines for the pre-processing and analysis of medical imaging datasets, optimizing research workflows and facilitating downstream analysis.

Contributed to groundbreaking genome editing research, engineering viral vectors and gaining extensive laboratory experience, resulting in a highly cited publication.

• Engineered a novel AAV-based viral vector for genome editing, leading to a publication recognized by the scientific community with over 80 citations.
• Developed comprehensive laboratory expertise, including cell-culture techniques, animal handling, plasmid construction, and critical data analysis and interpretation skills.

Research exploring how redundancy in brain networks protects cognitive function during accelerated brain aging.

Investigation into how network redundancy influences age-related differences in brain network controllability.

Study on the role of reward network integration in vulnerability to distress.

Analysis of salience network segregation in young adults with prodromal psychosis symptoms.

Research on the core architecture of functional brain networks and its role in resilience and cognitive performance in aging adults.

Utilizing a CNN model to identify regional substrates predictive of Alzheimer's disease progression.

Development of an AAV-based CRISPR SaCas9 system for in vivo neuronal genome editing with regulatable expression.