Ezer Patlan

Developed mobile features and user interfaces using Swift, Xcode, UIKit, and SwiftUI to deliver responsive iOS applications for community-facing projects. Implemented photogrammetry analysis leveraging iPhone LIDAR technology to capture and process 3D reconstruction images, enabling advanced spatial data visualization capabilities. Designed and engineered authentication systems and Firestore NoSQL database architecture to provide secure, scalable backend services for mobile applications. Collaborated with cross-functional teams by supporting DSA-hosted community events, gaining exposure to organizational operations while contributing technical expertise to enhance program delivery.

• Develop and select relevant features for machine learning models. • Experiment with different types of models (e.g., linear models, decision trees, neural networks) to find the best fit for the data and problem. • Optimize algorithms for efficiency, accuracy, and scalability. • Work with neural networks, including CNNs, RNNs, and other advanced architectures. • Perform hyperparameter tuning to enhance model performance. • Develop and maintain dashboards to track key performance metrics and model results.

Situation: Identified the need for a more user-friendly interface and stable backend to improve customer experience and application performance. Task: Improve frontend accessibility, maintain backend stability, and reduce API-related issues. Action: Enhanced frontend accessibility for a smoother user experience, maintained and tested backend systems to address server-side issues, and utilized Angular, SQL, and BI Visualization tools. Outcome: Achieved a 10% reduction in API-related issues, increased application reliability, and provided a more seamless interaction for users.

Situation: Recognized the need for enhanced personalization and responsiveness in user interactions, as well as data-driven insights for business decisions. Task: Build a recommendation engine, optimize a chatbot’s backend, and deliver actionable findings to stakeholders. Action: Developed a user behavior-based recommendation engine, optimized the chatbot with PostgreSQL for better engagement, and conducted experiments to gather insights. Outcome: Enhanced user personalization, increased chatbot responsiveness, and provided stakeholders with data-driven trend predictions, enabling more informed business decisions.

We built a Next.js portal that can solve problems related to patient satisfaction and managing my health. Hackathon: https://bigdataforsandiego.github.io/ Demo: https://ytl-lty.vercel.app/ Github: https://github.com/BigDataForSanDiego/Team-141

Situation: Faced challenges in translating business concepts into technical solutions and improving API performance for the HP Smart App's RF wireless functionality. Task: Create scalable platform solutions, identify connectivity issues, and enhance API reliability and performance. Action: Designed scalable solutions by translating business and product concepts into technical implementations, used REST APIs and Postman to detect RF wireless defects, and integrated GraphQL into the REST API infrastructure. Result: Achieved a 20% reduction in API issues, improved system performance, and delivered a more reliable user experience for the HP Smart App.

Swift, Android Java, PHP, HTML5, and CSS3

Microseismic and Ambient Noise Cross-Correlation Study of Menengai Caldera and Silali Volcano: Geothermal Prospect in the Central Kenya Dome

San Miguel volcanic seismicity and structure in Central America: Insight into the physical processes of volcanoes