Results-driven Computer Science graduate with a strong foundation in Python, Java, C++, and web development (HTML/CSS/JavaScript). Experienced in building RESTful APIs, optimizing databases, and developing data-driven dashboards (e.g., Airbnb Market Analysis, Healthcare Data). Proven ability to analyze large datasets, implement algorithm optimizations (e.g., 20% improved CPU scheduling performance), and create interactive UIs. Passionate about leveraging technical skills to solve real-world problems, with a focus on data analysis, software engineering, and user-centric design. Actively involved in promoting diversity in tech through CSGirls (ACM-W) and STEM outreach. Highly-motivated employee with desire to take on new challenges. Strong work ethic, adaptability, and exceptional interpersonal skills. Adept at working effectively unsupervised and quickly mastering new skills. Hardworking employee with customer service, multitasking, and time management abilities. Devoted to giving every customer a positive and memorable experience. An organized and motivated individual, eager to utilize time management and organizational skills across diverse settings. Seeking entry-level opportunities to enhance abilities while contributing to company growth.
Airbnb Market Analysis Dashboard, Built RESTful API with 15+ endpoints handling CRUD operations for property listings, Implemented data ingestion pipeline processing CSV/JSON files containing 5,000+ listings, Developed interactive visualizations showing pricing trends across Houston neighborhoods, Created recommendation engine suggesting optimal pricing based on amenities and location, Reduced frontend load time by 30% through query optimization Healthcare Data Analysis Dashboard, Developed interactive dashboard analyzing 8,000+ patient records across 50+ facilities, Implemented a data validation system that detected 200+ anomalies (out-of-range vitals, inconsistencies), Reduced data errors by 40% through automated range/duplicate checks, Identified 3 systemic data entry issues, leading to EHR training improvements, Visualized key metrics, linking evidence-based practices to 22% lower readmissions and 18% cost savings, Built a responsive UI with dynamic filtering by facility type/region OS Scheduler Simulation, Simulated CPU scheduling algorithms (Round Robin, SJF) in C++, Compared performance metrics (throughput, wait times) across 5+ algorithms, Achieved 20% better average wait time with optimized MLFQ implementation