
Worked on the UTDNebula/utd-trends repository over a two-month period, focusing on enhancing course search workflows and improving dashboard clarity. Leveraged React, Node.js, and TypeScript to move course aggregate queries server-side, enabling faster and more scalable searches. Implemented professor-based filtering and refined autocomplete logic to improve user experience and data accuracy. Addressed issues with duplicate search results and filtered out irrelevant courses, such as those with 'X' in course numbers, to deliver cleaner dashboards. Prioritized maintainable code and clear commit practices, resulting in more relevant search results, reduced user confusion, and streamlined decision-making for business users.
April 2026 monthly summary for UTDNebula/utd-trends. Focused on improving aggregate search relevance and user clarity in the Trends dashboard by filtering out courses with 'X' in course numbers. This delivered a cleaner overview experience and faster decision-making for business users.
April 2026 monthly summary for UTDNebula/utd-trends. Focused on improving aggregate search relevance and user clarity in the Trends dashboard by filtering out courses with 'X' in course numbers. This delivered a cleaner overview experience and faster decision-making for business users.
March 2026 monthly summary for UTDNebula/utd-trends: key search workflow enhancements, targeted bug fixes, and UI refinements focused on performance, accuracy, and business value.
March 2026 monthly summary for UTDNebula/utd-trends: key search workflow enhancements, targeted bug fixes, and UI refinements focused on performance, accuracy, and business value.

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