Upcoming Events

Guess what, Panthers! 🐾 The deadline for the PVAMU 2026–2027 General Scholarship Application has been EXTENDED to May 15th! 🎉 There’s still time to secure funding for your future. Don’t miss this opportunity. 🔗Apply today: https://pvamu.academicworks.com/ Best regards,   Default Prevention - Financial Aid  

Tuesday, Apr 7, 11:00 am Mr. Graham will be presenting on Identity Leadership on April 7th at 11:00 am in the Don Clark (Juvenile Justice) Auditorium. This event is a collaborative effort led by Student Affairs, in partnership with the Development Office, the Graduate School, and the President's Office. Event Link https://www.pvamu.edu/event/identity-leadership-with-mr-stedman-graham/

COMMITTEE CHAIR: Dr. Ali Fares CO-COMMITTEE CHAIR: Dr. Ripendra Awal TITLE: ASSESSING THE FUTURE IMPACTS OF DROUGHT ON COTTON YIELD IN TEXAS USING MACHINE LEARNING AND CLIMATE INDICES ABSTRACT: Climate change and increasing drought frequency pose significant challenges to agricultural sustainability in Texas. It particularly impacts cotton production, which is highly sensitive to temperature variability [...]

COMMITTEE CHAIR: Dr. Sharisse Hebert TITLE: IMPROVING HOT DEBRIEF COMPLETION RATES AFTER RAPID RESPONSE TEAM EVENTS THROUGH QR CODE ACCESS AND DIGITAL REMINDERS: A QUALITY IMPROVEMENT PROJECT ABSTRACT: Rapid Response Teams (RRTs) are critical for stabilizing acutely deteriorating patients. However, structured hot debriefs, which are brief reflections held immediately after emergencies, are inconsistently documented, despite [...]

COMMITTEE CHAIR: Dr. Xishuang Dong TITLE: LLMS-BASED TEXT-TO-SQL FOR GEOSPATIAL INFORMATION RETRIEVAL ABSTRACT: Text-to-SQL aims to translate natural language questions into SQL queries that can be executed on databases, enabling non-expert users to retrieve information without learning formal query languages. Early Text-to-SQL systems relied on rule-based methods and semantic parsers, while recent advances in deep [...]

COMMITTEE CHAIR: Dr. Xishuang Dong TITLE: MULTI-TASK GROWING INTERPRETABLE NEURAL NETWORK FOR MULTI-TARGET SYMBOLIC REGRESSION ABSTRACT: Over the past decade, deep learning has achieved remarkable success across a wide range of domains, including computer vision and natural language processing. Despite their strong performance, these models often operate as black boxes, making it difficult to understand [...]