Brian Imbenzi

Highly accomplished and analytical Computer Science Ph.D. Candidate with over a decade of deep technical experience specializing in large-scale data annotation, machine learning workflows, and AI training data production management. Proven expertise in designing high-quality video and text data annotation pipelines, auditing complex model outputs, and leading remote technical teams to optimize LLM performance.

Adept at bridging the gap between rigorous academic research and practical, industry-standard AI data engineering.

Video Annotation Specialist at SHELTEAN COORP (2011-05 – Present)

• Lead creation of high-quality video annotation datasets for computer vision and ML systems.

Research Assistant – Artificial Intelligence & Data Analytics at SHELTEAN COORP (2011-05 – Present)

Assisted in academic and applied research projects involving machine learning, data analytics, and artificial intelligence. Conducted data collection, preprocessing, statistical analysis, and model evaluation to support research objectives. Collaborated with faculty and research teams to develop technical reports, validate datasets, and implement data-driven solutions.

Contributed to research publications, literature reviews, and experimental design while ensuring data integrity and quality standards.

Data Annotation Specialist at SHELTEAN COORP (2011-05 – Present)

• Evaluated AI outputs, annotated conversational datasets, and improved data quality processes.

Data Science Intern at SHELTEAN COORP (2011-05 – Present)

• Supported data cleaning, exploratory analysis, and predictive modeling projects.

Ph.D. in Computer Science – Abraham Lincoln University (2017-12)

M.Sc. in Computer Science & Engineering – University of Washington (2008-06)

B.Sc. in Computer Science – Abraham Lincoln University (1995-09)