Zainab Damilola Lawal

Engineered a custom masking layer to manage irregular time intervals in clinical vitals (PHEMS dataset), effectively addressing the critical 'missing data' challenge in low-resource hospitals.

Pioneered a multimodal approach using Gramian Angular Fields (GAF) to convert 1D physiological signals into 2D images, leveraging CNNs to achieve 0.90 AUROC on imbalanced data for patient instability detection.

Architected and deployed an AI agent framework for an AI-native financial intelligence platform, utilizing FastAPI and process supervisors (MCP, stdio) to efficiently resolve complex multi-tool orchestration.

Developed automated benchmarking workflows with Spreadsheet Bench to rigorously validate AI model performance in generating complex Excel/CSV formulas.

Engineered a secure, containerized execution environment (Daytona) for AI-generated code, enabling dynamic use of Python libraries (pandas, openpyxl) with strict isolation and resource control.

Implemented multi-layered safeguards and automated error handling by integrating runtime tracebacks into the LLM, facilitating iterative debugging and retry mechanisms within the sandbox.

Executed a first-of-its-kind computational analysis on 51,407 Yoruba news articles, advancing indigenous African language NLP.

Developed a custom NLP pipeline utilizing Multilingual BERT and BERTopic for semantic topic modeling, identifying 47 distinct topics with a 0.73 coherence score.

Implemented a robust statistical and cultural validation framework to ensure research findings were contextually sound and aligned with real-world applications.

Developed a deep learning ensemble for osteoporosis detection using standard X-rays, achieving 90.10% accuracy and integrating a chatbot for interpretable clinician feedback.

Published research in IEEE Xplore on an AI-powered osteoporosis detection system, demonstrating innovation in healthcare diagnostics.

Built secure RESTful APIs and backend services, enabling robust exam management, user authentication, and real-time functionality for the online examination system.

Contributed to database schema design and optimization for scalability, actively participating in comprehensive code reviews to ensure system integrity and performance.

Collaborated within an Agile development environment, leveraging Jira for efficient task management and sprint planning across cross-functional teams.

Developed experimental simulations in C++, Python, and Pandas, validating synchronization efficiency on datasets comprising up to 32,000 states.

Authored and presented a research paper on state synchronization trends, showcasing improved results compared to traditional heuristics.

Delivered structured project guidance, contributing to a 90% project completion rate among tutored students.

Developed and optimized OCR pipelines using Tesseract OCR and Apache PDFBox, accelerating legal document processing speed by 30% for low-quality scanned documents.

Designed and implemented search and analysis systems leveraging the ELK Stack (ElasticSearch, Logstash, Kibana), resulting in a 40% reduction in query times.

Designed a scalable system architecture that efficiently processed user requests, implementing RESTful APIs with optimized query performance.

Enhanced system reliability by meticulously designing and documenting comprehensive error-handling strategies for critical API endpoints.