As Chief Technical Officer at Octozi., I bring specialized expertise in artificial intelligence and machine learning to lead the development of Octozi. My Ph.D. in Machine Learning from Rutgers University and extensive industry experience at companies like Waymo, Amazon, and Clarifai have equipped me with the technical knowledge to tackle complex pattern recognition challenges in structured data. My work on the MATTER framework demonstrated how self-supervision can achieve up to 24% performance increases and 76% faster convergence in downstream tasks, principles directly applicable to improving the efficiency of clinical data review. My research on CNN-LSTM architectures established methods for analyzing temporal patterns across sequential data points, essential for detecting complex discrepancies in clinical trials such as mistimed adverse events. Additionally, my experience developing H2O-Net, which outperformed state-of-the-art segmentation methods through adversarial domain adaptation and label refinement, provides a foundation for CDAP's ability to identify subtle patterns in multi-dimensional clinical data. As a reviewer for prestigious AI conferences, I remain at the forefront of machine learning innovations that can enhance healthcare data systems.