All PVAMU undergraduate seniors seeking admission into the graduate school may use the Internal Application Form instead of the ApplyTexas Application. In addition, currently enrolled PVAMU graduate students who are in their final semester of completing their first graduate degree and wish to pursue a second graduate degree may also use the Internal Application Form. However, all applicants are required to pay the $50 application fee.<\/p>\n
Internal Graduate School Application Form<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" All PVAMU undergraduate seniors seeking admission into the graduate school may use the Internal Application Form instead of the ApplyTexas Application. In addition, currently enrolled PVAMU graduate students who are in their final semester of completing their first graduate degree and wish to pursue a second graduate degree may also use the Internal Application Form. […]<\/p>\n","protected":false},"author":432,"featured_media":0,"parent":7854,"menu_order":4,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_expiration-date-status":"","_expiration-date":0,"_expiration-date-type":"","_expiration-date-categories":[],"_expiration-date-options":[],"footnotes":""},"yst_prominent_words":[375,315,317,131,279,383,289],"class_list":["post-8624","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8624","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/users\/432"}],"replies":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/comments?post=8624"}],"version-history":[{"count":1,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8624\/revisions"}],"predecessor-version":[{"id":8625,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8624\/revisions\/8625"}],"up":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/7854"}],"wp:attachment":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/media?parent=8624"}],"wp:term":[{"taxonomy":"yst_prominent_words","embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/yst_prominent_words?post=8624"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}},{"id":8579,"date":"2026-04-10T17:30:33","date_gmt":"2026-04-10T22:30:33","guid":{"rendered":"https:\/\/www.pvamu.edu\/graduateschool\/?page_id=8579"},"modified":"2026-04-10T17:30:39","modified_gmt":"2026-04-10T22:30:39","slug":"rahul-debnath-masters-thesis-defense-wednesday-april-29-2026-130-pm-central-time","status":"publish","type":"page","link":"https:\/\/www.pvamu.edu\/graduateschool\/news-events-announcements\/final-master-defense-announcements\/spring-2026-masters-final-defense\/rahul-debnath-masters-thesis-defense-wednesday-april-29-2026-130-pm-central-time\/","title":{"rendered":"Rahul Debnath Master\u2019s Thesis Defense, Wednesday, April 29, 2026 @ 1:30 pm Central Time"},"content":{"rendered":" COMMITTEE CHAIR<\/strong>: Dr. Ahmed Ahmed ABSTRACT: <\/strong>This research presents a comprehensive, three-phase hierarchical data collection framework integrating Internet of Things (IoT) sensors, aerial RGB imagery, and Electrical Resistivity Imaging (ERI) to correlate multi-scale hydro-geophysical soil parameters for comprehensive slope stability assessment. The first phase established a laboratory-scale physical slope model using Fat Clay (CH) to replicate failure-prone conditions. The prototype was instrumented with distributed IoT sensors measuring tilt, volumetric moisture content, and soil matric suction. Real-time data was transmitted to a custom cloud-based web graphical user interface (GUI) for remote visualization. Controlled artificial rainfall simulations validated the system\u2019s sensitivity to subtle kinematic and hydrological changes, establishing a foundational point-scale measurement infrastructure. To transition from lab-scale point measurements to vast field-scale analysis, the second phase focused on surface data extraction. Close-range aerial RGB imagery was captured for a targeted field location to extract multiple quantitative optical indices (e.g., Green Leaf Index (GLI), Visible Atmospherically Resistant Index (VARI), Normalized Green-Red Difference Index (NGRDI), etc.). The index exhibiting the highest statistical correlation score was selected to map surface-level variations. This optical data collection validated the visual monitoring infrastructure and serves as a foundational proxy for future, large-scale Unmanned Aerial Vehicle (UAV) deployment. The third phase conducted ERI surveys at the same field location to capture subsurface resistivity profiles. The core of this research establishes a direct data correlation framework between the extracted surface features (RGB) and subsurface geophysical conditions (soil resistivity). Utilizing Pearson correlation to assess linear relationships and Spearman rank correlation to evaluate monotonic trends, these distinct spatial datasets are systematically aligned. Quantifying these statistical relationships allows for the identification of underlying soil behavior patterns, enabling an in-depth analysis of near-surface anomalies and potential landslide triggers. This multi-scale approach bridges the critical gap between controlled laboratory validation and operational field mapping. By hierarchically correlating continuous IoT point-sensing with expansive RGB and ERI spatial data, this research delivers a robust, scalable data collection infrastructure. This framework addresses the limitations of conventional techniques, providing a cost-effective, multi-tiered solution for proactive anomaly detection and landslide risk mitigation.<\/p>\n Keywords: <\/strong>AI, IoT, UAV, ERI, Data Fusion<\/p>\n Room Location: <\/strong>S. R. Collins. Room 111: CS Main Conference Room.<\/p>\n","protected":false},"excerpt":{"rendered":" COMMITTEE CHAIR: Dr. Ahmed Ahmed CO-COMMITTEE CHAIR: Dr. Md. Jobair Bin Alam TITLE: MULTI-TASK GROWING INTERPRETABLE NEURAL NETWORK FOR MULTI-TARGET SYMBOLIC REGRESSION ABSTRACT: This research presents a comprehensive, three-phase hierarchical data collection framework integrating Internet of Things (IoT) sensors, aerial RGB imagery, and Electrical Resistivity Imaging (ERI) to correlate multi-scale hydro-geophysical soil parameters for comprehensive […]<\/p>\n","protected":false},"author":432,"featured_media":0,"parent":8136,"menu_order":9,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_expiration-date-status":"","_expiration-date":0,"_expiration-date-type":"","_expiration-date-categories":[],"_expiration-date-options":[],"footnotes":""},"yst_prominent_words":[133],"class_list":["post-8579","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8579","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/users\/432"}],"replies":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/comments?post=8579"}],"version-history":[{"count":1,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8579\/revisions"}],"predecessor-version":[{"id":8580,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8579\/revisions\/8580"}],"up":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8136"}],"wp:attachment":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/media?parent=8579"}],"wp:term":[{"taxonomy":"yst_prominent_words","embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/yst_prominent_words?post=8579"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}},{"id":8562,"date":"2026-04-08T23:09:59","date_gmt":"2026-04-09T04:09:59","guid":{"rendered":"https:\/\/www.pvamu.edu\/graduateschool\/?page_id=8562"},"modified":"2026-04-08T23:12:03","modified_gmt":"2026-04-09T04:12:03","slug":"maowen-tang-masters-thesis-defense-wednesday-april-22-2026-1100-am-central-time","status":"publish","type":"page","link":"https:\/\/www.pvamu.edu\/graduateschool\/news-events-announcements\/final-master-defense-announcements\/spring-2026-masters-final-defense\/maowen-tang-masters-thesis-defense-wednesday-april-22-2026-1100-am-central-time\/","title":{"rendered":"Maowen Tang Master\u2019s Thesis Defense, Wednesday, April 22, 2026 @ 11:00 am Central Time"},"content":{"rendered":" COMMITTEE CHAIR<\/strong>: Dr. Yonghui Wang<\/p>\n TITLE<\/strong>: STRUCTURED REPRESENTATION LEARNING FOR GENERALIZABLE DEEPFAKE VIDEO DETECTION<\/p>\n ABSTRACT<\/strong> Deepfake video detection has become an important problem in multimedia forensics as modern generative models produce increasingly realistic facial manipulations. Although many existing detectors achieve strong performance on the datasets on which they are trained, their performance often degrades substantially on unseen manipulation methods and evaluation conditions. A major reason for this limitation is the premature collapse of spatial structure: many vision transformer based detectors aggregate patch tokens into a single global representation, thereby suppressing the localized and temporally uneven forensic cues that characterize manipulated video. This thesis presents the Spatio-Temporal Slot Aggregation Network (ST-SAN), a video-level deepfake detection framework designed to preserve structured forensic evidence before final classification. ST-SAN extracts patch tokens from three intermediate layers of a frozen DINOv2 backbone and aligns them through a lightweight bottleneck projection. A K-slot soft aggregation module then forms multiple learned slot summaries for each frame, allowing the model to retain several localized views of manipulation evidence instead of collapsing all patch information into one vector. These slot features are further integrated through adaptive frame weighting and slot weighting so that frames and slot summaries with stronger forensic content contribute more to the final decision. Training is stabilized by structural regularization terms that encourage locality, orthogonality, diversity across slot summaries, and weak coverage. Experiments show that ST-SAN achieves 0.960 AUC on FaceForensics++ under in-domain evaluation. Under cross-domain evaluation, it achieves 0.917 AUC on Celeb-DF v2, 0.872 AUC on DeepFakeDetection, and 0.890 AUC on the DeepFake Detection Challenge Preview dataset, indicating competitive cross-domain performance on the reported benchmarks. Ablation results show that parallel soft slot aggregation is an important architectural component, while adaptive weighting and structural regularization help stabilize the full model. These findings indicate that preserving multiple localized forensic summaries prior to video-level classification is a promising strategy for improving robustness under cross-domain evaluation in deepfake video detection.<\/p>\n Keywords: <\/strong>Deepfake detection, video forensics, representation learning, vision transformer, slot aggregation, adaptive weighting.<\/p>\n Room Location: <\/strong>S. R. Collins Building, Room 111L<\/p>\n","protected":false},"excerpt":{"rendered":" COMMITTEE CHAIR: Dr. Yonghui Wang TITLE: STRUCTURED REPRESENTATION LEARNING FOR GENERALIZABLE DEEPFAKE VIDEO DETECTION ABSTRACT Deepfake video detection has become an important problem in multimedia forensics as modern generative models produce increasingly realistic facial manipulations. Although many existing detectors achieve strong performance on the datasets on which they are trained, their performance often degrades substantially […]<\/p>\n","protected":false},"author":432,"featured_media":0,"parent":8136,"menu_order":8,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_expiration-date-status":"","_expiration-date":0,"_expiration-date-type":"","_expiration-date-categories":[],"_expiration-date-options":[],"footnotes":""},"yst_prominent_words":[],"class_list":["post-8562","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8562","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/users\/432"}],"replies":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/comments?post=8562"}],"version-history":[{"count":3,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8562\/revisions"}],"predecessor-version":[{"id":8565,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8562\/revisions\/8565"}],"up":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8136"}],"wp:attachment":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/media?parent=8562"}],"wp:term":[{"taxonomy":"yst_prominent_words","embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/yst_prominent_words?post=8562"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}},{"id":8557,"date":"2026-04-08T22:42:54","date_gmt":"2026-04-09T03:42:54","guid":{"rendered":"https:\/\/www.pvamu.edu\/graduateschool\/?page_id=8557"},"modified":"2026-04-08T22:43:33","modified_gmt":"2026-04-09T03:43:33","slug":"tarek-aziz-masters-thesis-defense-tuesday-april-21-2026-1000-am-central-time","status":"publish","type":"page","link":"https:\/\/www.pvamu.edu\/graduateschool\/news-events-announcements\/final-master-defense-announcements\/spring-2026-masters-final-defense\/tarek-aziz-masters-thesis-defense-tuesday-april-21-2026-1000-am-central-time\/","title":{"rendered":"Tarek Aziz Master\u2019s Thesis Defense, Tuesday, April 21, 2026 @ 10:00 am Central Time"},"content":{"rendered":" COMMITTEE CHAIR<\/strong>: Dr. Suxia Cui TITLE<\/strong>: EFFECTIVE DATA AUGMENTATION STRATEGIES FOR SMALL OBJECT DETECTION<\/p>\n ABSTRACT: <\/strong>Data augmentation remains the most effective method of improving object detection, particularly in scenes where small objects dominate and the annotated datasets are small. While geometric augmentation techniques such as flipping, cropping, and photometric augmentations such as brightness, color jittering adjustments, and content-level augmentations such as mixup and copy-paste augmentation have proved useful, current research suggests the viability of learned augmentation policies, instance-level generative augmentations, and diffusion-based augmentations. Here, we present a structured comparison of the augmentation methods on the YOLO11m detector, focusing particularly on the detection of small objects on the SODA-D dataset. We benchmark the standard YOLO11m model with models learned with (i) conventional transforms, (ii) learned augmentation policies, (iii) diffusion-based semantic editing, and (iv) instance-level redrawing of images by employing pre-trained generative models. Through different experimental configurations, we see that dataset augmentation by instance-level augmentations always outperforms the rest, achieving the highest boosts in mean Average Precision (mAP) for object categories of small size. Our research also indicates that augmentations at the object-instance level guarantee preservation of scene context and introduce visual diversity, which overcomes overfitting and improves robustness. These findings provide empirical evidence for instance-aware generative augmentations being a robust and generalizable method of detecting small objects, and leave the possibility of data-efficient training pipelines for future vision tasks.<\/p>\n Keywords: <\/strong>Small Object Detection, Data Augmentation, Generative Models, YOLO11m<\/p>\n Room Location: <\/strong>Electrical Engineering Conference Room 315D<\/p>\n","protected":false},"excerpt":{"rendered":" COMMITTEE CHAIR: Dr. Suxia Cui CO-COMMITTEE CHAIR: Dr. Lujun Zhai TITLE: EFFECTIVE DATA AUGMENTATION STRATEGIES FOR SMALL OBJECT DETECTION ABSTRACT: Data augmentation remains the most effective method of improving object detection, particularly in scenes where small objects dominate and the annotated datasets are small. While geometric augmentation techniques such as flipping, cropping, and photometric augmentations […]<\/p>\n","protected":false},"author":432,"featured_media":0,"parent":8136,"menu_order":7,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_expiration-date-status":"","_expiration-date":0,"_expiration-date-type":"","_expiration-date-categories":[],"_expiration-date-options":[],"footnotes":""},"yst_prominent_words":[],"class_list":["post-8557","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8557","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/users\/432"}],"replies":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/comments?post=8557"}],"version-history":[{"count":1,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8557\/revisions"}],"predecessor-version":[{"id":8558,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8557\/revisions\/8558"}],"up":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8136"}],"wp:attachment":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/media?parent=8557"}],"wp:term":[{"taxonomy":"yst_prominent_words","embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/yst_prominent_words?post=8557"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}},{"id":8538,"date":"2026-04-01T10:41:48","date_gmt":"2026-04-01T15:41:48","guid":{"rendered":"https:\/\/www.pvamu.edu\/graduateschool\/?page_id=8538"},"modified":"2026-04-10T17:29:56","modified_gmt":"2026-04-10T22:29:56","slug":"md-symum-islam-masters-thesis-defense-thursday-april-9-2026-1230-pm-central-time","status":"publish","type":"page","link":"https:\/\/www.pvamu.edu\/graduateschool\/news-events-announcements\/final-master-defense-announcements\/spring-2026-masters-final-defense\/md-symum-islam-masters-thesis-defense-thursday-april-9-2026-1230-pm-central-time\/","title":{"rendered":"MD Symum Islam Master\u2019s Thesis Defense, Thursday, April 9, 2026 @ 12:30 pm Central Time"},"content":{"rendered":" COMMITTEE CHAIR<\/strong>: Dr. Ali Fares TITLE<\/strong>: ASSESSING THE FUTURE IMPACTS OF DROUGHT ON COTTON YIELD IN TEXAS USING MACHINE LEARNING AND CLIMATE INDICES<\/p>\n ABSTRACT: <\/strong>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 and water availability. Despite numerous studies evaluating climate impacts on agriculture, limited research integrates climate projections to predict cotton yield. There is also few research on drought indices, machine learning models, and crop simulation models to assess future cotton yield vulnerability across Texas climate divisions. Therefore, this study evaluates historical climate trends, compares predictive modeling approaches, and projects future cotton yield under climate change scenarios. Historical climate variables including maximum temperature (Tmax), minimum temperature (Tmin), precipitation (PR), potential evapotranspiration (PET), soil water storage (STOR), and drought indices (SPI-3, SPI-6, SPEI-3, and SPEI-6) were analyzed with cotton yield data from 1968 \u2013 2024. Multiple Linear Regression (MLR) and Random Forest (RF) models were developed and evaluated using correlation coefficient (R) and Root Mean Square Error (RMSE). Future climate projections from CMIP6 LOCA datasets under SSP245 and SSP585 scenarios were used to project cotton yield for 2030\u20132050. Additionally, the FAO AquaCrop model was used to simulate crop response to water availability and validate machine learning projections. Results indicate that temperature and PET were the most influential predictors of cotton yield across Texas climate divisions. RF models showed improved predictive performance with R values ranging from approximately 0.62 to 0.89, compared to MLR values ranging from 0.48 to 0.81. AquaCrop validation showed low to moderate correlation with observed yields (R \u02dc -0.05 to 0.48). Future projections indicate yield increases of approximately 9\u201321% in North Central and High Plains divisions, while declines of approximately 6\u201311% are projected in South Central and Upper Coast regions. Irrigated cotton showed greater resilience compared to non-irrigated systems. Overall, this study demonstrates that integrating machine learning and crop simulation models improves climate impact assessment and supports climate-resilient agricultural management strategies in Texas.<\/p>\n Keywords: <\/strong>Cotton yield, machine learning, drought indices (Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI)), AquaCrop model, Random Forest (RF), Texas agriculture<\/p>\n Room Location: <\/strong>Jesse and Mary Gibbs Jones CAFNR Research Building, Seminar Room 015<\/p>\n","protected":false},"excerpt":{"rendered":" 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 […]<\/p>\n","protected":false},"author":432,"featured_media":0,"parent":8136,"menu_order":10,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_expiration-date-status":"","_expiration-date":0,"_expiration-date-type":"","_expiration-date-categories":[],"_expiration-date-options":[],"footnotes":""},"yst_prominent_words":[],"class_list":["post-8538","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8538","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/users\/432"}],"replies":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/comments?post=8538"}],"version-history":[{"count":1,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8538\/revisions"}],"predecessor-version":[{"id":8539,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8538\/revisions\/8539"}],"up":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8136"}],"wp:attachment":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/media?parent=8538"}],"wp:term":[{"taxonomy":"yst_prominent_words","embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/yst_prominent_words?post=8538"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}},{"id":8531,"date":"2026-03-31T14:22:53","date_gmt":"2026-03-31T19:22:53","guid":{"rendered":"https:\/\/www.pvamu.edu\/graduateschool\/?page_id=8531"},"modified":"2026-03-31T14:23:47","modified_gmt":"2026-03-31T19:23:47","slug":"carl-daniel-doctoral-project-defense-monday-april-13-2026-300-pm-central-time","status":"publish","type":"page","link":"https:\/\/www.pvamu.edu\/graduateschool\/news-events-announcements\/dissertation-defense-announcements-2\/spring-2026-final-doctoral-defense\/carl-daniel-doctoral-project-defense-monday-april-13-2026-300-pm-central-time\/","title":{"rendered":"Carl Daniel Doctoral Project Defense, Monday, April 13, 2026 @ 3:00 pm Central Time"},"content":{"rendered":" COMMITTEE CHAIR<\/strong>: Dr. Sharisse Hebert<\/p>\n TITLE<\/strong>: IMPROVING HOT DEBRIEF COMPLETION RATES AFTER RAPID RESPONSE TEAM EVENTS THROUGH QR CODE ACCESS AND DIGITAL REMINDERS: A QUALITY IMPROVEMENT PROJECT<\/p>\n ABSTRACT: <\/strong>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 benefits for communication, safety, and staff well-being. At a large academic hospital in Southeast Texas, only 35.76% of RRT events in 2025 included completed hot debrief forms. A January 2025 hospital-led survey of Clinical Emergency Response Team (CERT) Advanced Practice Registered Nurses (APRNs) identified barriers such as time constraints, limited access, and forgetfulness. Purpose: This Doctor of Nursing Practice (DNP) quality improvement project (QI) evaluated whether a bundled intervention, QR code access to forms, twice daily SMS reminders (9:00 a.m. and 9:00 p.m.), and brief education, increases hot debrief documentation rates after RRT events. Methods: Guided by the Plan-Do-Check-Act (PDCA) framework, this pre- and post-intervention design was implemented at a large academic hospital in Southeast Texas. The project involved 40 CERT APRNs conducting adult inpatient debriefs across about 729 RRT events in three months. Microsoft Forms was used for secure data collection, and pre-\/post-surveys assessed perceived barriers and attitudes. Intervention activities included QR code distribution, scheduled twice-daily SMS reminders (9:00 a.m. and 9:00 p.m.), and structured debriefing education. Descriptive statistics and chi-square tests compared pre- and post-intervention documentation rates. The approach offered a scalable, cost-effective strategy to enhance communication, support staff recovery, and strengthen safety learning.<\/p>\n Keywords<\/strong>: Hot Debrief, Rapid Response Team, QR Code, Digital Reminder, Advanced Practice Registered Nurse, Quality Improvement, Emergency Response, Documentation Compliance, PDCA Cycle<\/p>\n Location Online:<\/strong><\/p>\n Zoom Link<\/strong>: https:\/\/pvpanther.zoom.us\/j\/96912933947?pwd=7JtIHymgoSbo0fIJCofRaQJAiTVN4N.1<\/a><\/p>\n Meeting ID<\/strong>: 933 2678 4995<\/p>\n Passcode<\/strong>: 467899<\/p>\n<\/div>\n 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 […]<\/p>\n","protected":false},"author":432,"featured_media":0,"parent":8149,"menu_order":3,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_expiration-date-status":"","_expiration-date":0,"_expiration-date-type":"","_expiration-date-categories":[],"_expiration-date-options":[],"footnotes":""},"yst_prominent_words":[],"class_list":["post-8531","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8531","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/users\/432"}],"replies":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/comments?post=8531"}],"version-history":[{"count":2,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8531\/revisions"}],"predecessor-version":[{"id":8533,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8531\/revisions\/8533"}],"up":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8149"}],"wp:attachment":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/media?parent=8531"}],"wp:term":[{"taxonomy":"yst_prominent_words","embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/yst_prominent_words?post=8531"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}},{"id":8528,"date":"2026-03-31T14:20:20","date_gmt":"2026-03-31T19:20:20","guid":{"rendered":"https:\/\/www.pvamu.edu\/graduateschool\/?page_id=8528"},"modified":"2026-03-31T14:20:40","modified_gmt":"2026-03-31T19:20:40","slug":"hussein-rajabu-masters-thesis-defense-friday-april-17-2026-230-pm-central-time","status":"publish","type":"page","link":"https:\/\/www.pvamu.edu\/graduateschool\/news-events-announcements\/final-master-defense-announcements\/spring-2026-masters-final-defense\/hussein-rajabu-masters-thesis-defense-friday-april-17-2026-230-pm-central-time\/","title":{"rendered":"Hussein Rajabu Master\u2019s Thesis Defense, Friday, April 17, 2026 @ 2:30 pm Central Time"},"content":{"rendered":" COMMITTEE CHAIR<\/strong>: Dr. Xishuang Dong<\/p>\n TITLE<\/strong>: MULTI-TASK GROWING INTERPRETABLE NEURAL NETWORK FOR MULTI-TARGET SYMBOLIC REGRESSION<\/p>\n ABSTRACT: <\/strong>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 how decisions are made. This lack of transparency poses significant challenges for deploying deep learning techniques in high-stakes applications such as healthcare and business analytics, where interpretability is essential. To address this issue, explainable artificial intelligence (XAI) and interpretable AI techniques have been developed to provide insights into model behavior. However, most existing approaches primarily capture statistical associations between inputs and outputs rather than uncovering the underlying functional mechanisms driving predictions. Symbolic regression (SR) has recently gained attention as a promising approach within interpretable AI. Unlike traditional methods, SR aims to discover explicit mathematical expressions that describe the relationships between variables, offering both interpretability and competitive predictive performance. Although SR has advanced significantly in recent years, two major challenges remain. First, SR methods are primarily developed and validated on scientific datasets, such as those from physics and chemistry, where underlying relationships are relatively well understood. This limits their applicability to broader, data-driven machine learning tasks. Second, most SR approaches focus on single-target regression, while many real-world problems involve multiple correlated outputs that share common information. To address these limitations, this thesis proposes Multi-Task Regression GINN-LP (MTRGINN-LP), a novel neuro-symbolic framework for multi-target symbolic regression. Building upon GINN-LP, the model introduces Power-Term Approximator Blocks to effectively capture power-law relationships in data. It further integrates multi-task learning through a shared backbone combined with task-specific output layers, enabling the discovery of shared symbolic representations while maintaining task-level interpretability. Additionally, a symbolic loss function is introduced to align symbolic predictions with regression outputs during training. The proposed method is evaluated on diverse multi-target regression tasks, including energy efficiency analysis and sustainable agriculture. Experimental results demonstrate that the approach achieves competitive predictive performance while preserving strong interpretability, effectively bridging the gap between symbolic regression and practical multi-output learning scenarios.<\/p>\n Keywords: <\/strong>Interpretable AI, Multi-task Learning, Multi-target Regression, Symbolic AI<\/p>\n Room Location: <\/strong>Electrical Engineering Conference Room 315D<\/p>\n","protected":false},"excerpt":{"rendered":" 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 […]<\/p>\n","protected":false},"author":432,"featured_media":0,"parent":8136,"menu_order":6,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_expiration-date-status":"","_expiration-date":0,"_expiration-date-type":"","_expiration-date-categories":[],"_expiration-date-options":[],"footnotes":""},"yst_prominent_words":[],"class_list":["post-8528","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8528","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/users\/432"}],"replies":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/comments?post=8528"}],"version-history":[{"count":1,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8528\/revisions"}],"predecessor-version":[{"id":8529,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8528\/revisions\/8529"}],"up":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8136"}],"wp:attachment":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/media?parent=8528"}],"wp:term":[{"taxonomy":"yst_prominent_words","embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/yst_prominent_words?post=8528"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}},{"id":8523,"date":"2026-03-31T14:14:11","date_gmt":"2026-03-31T19:14:11","guid":{"rendered":"https:\/\/www.pvamu.edu\/graduateschool\/?page_id=8523"},"modified":"2026-03-31T14:14:11","modified_gmt":"2026-03-31T19:14:11","slug":"temitayo-ogunsusi-masters-thesis-defense-friday-april-17-2026-1100-am-central-time","status":"publish","type":"page","link":"https:\/\/www.pvamu.edu\/graduateschool\/news-events-announcements\/final-master-defense-announcements\/spring-2026-masters-final-defense\/temitayo-ogunsusi-masters-thesis-defense-friday-april-17-2026-1100-am-central-time\/","title":{"rendered":"Temitayo Ogunsusi Master\u2019s Thesis Defense, Friday, April 17, 2026 @ 11:00 am Central Time"},"content":{"rendered":" COMMITTEE CHAIR<\/strong>: Dr. Xishuang Dong<\/p>\n TITLE<\/strong>: LLMS-BASED TEXT-TO-SQL FOR GEOSPATIAL INFORMATION RETRIEVAL<\/p>\n ABSTRACT: <\/strong>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 learning have achieved strong performance by jointly encoding user questions and database schemas. However, these approaches typically require large annotated datasets and specific model architectures. With the emergence of large language models (LLMs), such as GPT-4, Llama, and Gemma, Text-to-SQL systems can leverage powerful natural language understanding capabilities to generate SQL queries using zero-shot or few-shot prompting. Despite these advancements, existing research has largely focused on conventional relational databases, with limited attention given to geospatial databases that involve specialized spatial data types and functions. This thesis addresses this gap by investigating LLM-based Text-to-SQL for geospatial information retrieval. We construct a new benchmark dataset with a PostGIS spatial database, containing natural language questions paired with SQL queries that incorporate spatial operations such as distance calculations, spatial joins, and geometric predicates. To expand the dataset and improve diversity, additional question-query pairs are generated through LLM-based data augmentation. Furthermore, building on this benchmark, we develop a Text-to-SQL pipeline that integrates multiple state-of-the-art LLMs to translate natural language queries into executable spatial SQL statements. The system incorporates database schema information within prompts to improve query generation. Experimental results demonstrate that the proposed pipeline can effectively retrieve geospatial information using natural language queries, achieving competitive performance regarding Execution Accuracy and Valid Efficiency Score.<\/p>\n Keywords: <\/strong>Text-to-SQL, Large Language Models, Information Retrieval, Geospatial Database<\/p>\n Room Location: <\/strong>Electrical Engineering Conference Room 315D<\/p>\n","protected":false},"excerpt":{"rendered":" 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 […]<\/p>\n","protected":false},"author":432,"featured_media":0,"parent":8136,"menu_order":3,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_expiration-date-status":"","_expiration-date":0,"_expiration-date-type":"","_expiration-date-categories":[],"_expiration-date-options":[],"footnotes":""},"yst_prominent_words":[535],"class_list":["post-8523","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8523","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/users\/432"}],"replies":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/comments?post=8523"}],"version-history":[{"count":1,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8523\/revisions"}],"predecessor-version":[{"id":8524,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8523\/revisions\/8524"}],"up":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/pages\/8136"}],"wp:attachment":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/media?parent=8523"}],"wp:term":[{"taxonomy":"yst_prominent_words","embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/yst_prominent_words?post=8523"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}},{"id":8484,"date":"2026-03-19T00:55:18","date_gmt":"2026-03-19T05:55:18","guid":{"rendered":"https:\/\/www.pvamu.edu\/graduateschool\/?page_id=8484"},"modified":"2026-03-19T00:55:26","modified_gmt":"2026-03-19T05:55:26","slug":"susan-breland-dissertation-proposal-defense-thursday-march-26-2026-900-am-central-time","status":"publish","type":"page","link":"https:\/\/www.pvamu.edu\/graduateschool\/news-events-announcements\/proposal-doctoral-defense-announcements\/spring-2026-proposal-doctoral-defense\/susan-breland-dissertation-proposal-defense-thursday-march-26-2026-900-am-central-time\/","title":{"rendered":"Susan Breland Dissertation Proposal Defense, Thursday, March 26, 2026 @ 9:00 am Central Time"},"content":{"rendered":" COMMITTEE CHAIR<\/strong>: Dr. Stella Smith<\/p>\n TITLE<\/strong>: BEYOND BORDERS: THE IMPACT OF STUDY ABROAD ON BELONGING, MOTIVATION, AND DEGREE PROGRESSION<\/p>\n ABSTRACT:<\/strong> Study abroad experiences have been associated with improved student retention, academic engagement, and graduation outcomes, particularly for students from historically underrepresented groups in higher education. International learning opportunities expose students to diverse perspectives, enhance intercultural competence, and may strengthen students\u2019 connection to their academic institutions. Guided by Vincent Tinto\u2019s Student Integration Theory and Alexander Astin\u2019s Input\u2013Environment\u2013Outcome (I\u2013E\u2013O) Model, this proposed qualitative phenomenological study will examine how participation in study abroad programs shapes students\u2019 sense of belonging, academic motivation, and persistence toward degree completion. These theoretical frameworks provide a lens for understanding how students\u2019 pre-college characteristics, institutional environments, and academic and social integration interact to influence educational outcomes. The purpose of this study is to explore the lived experiences of students who have participated in study abroad programs and to examine how these experiences influence their academic engagement, retention, and progression toward graduation. Particular attention will be given to identifying structural and social factors that support or hinder participation and success among underrepresented student populations. By centering student narratives, the study aims to better understand how international academic experiences contribute to students\u2019 personal, academic, and professional development. A phenomenological research design will be used to capture and interpret students lived experiences. Data will be collected through semi-structured interviews with students who have completed study abroad programs. Interview data will be analyzed using descriptive coding to identify emergent themes related to academic engagement, social integration, institutional support, and persistence. Themes will be interpreted through the theoretical constructs of Tinto\u2019s academic and social integration model and Astin\u2019s I\u2013E\u2013O framework. The study is expected to provide insights into how study abroad participation may enhance students\u2019 academic confidence, cultural awareness, and institutional engagement. Findings may also highlight the importance of financial support, mentoring, and culturally responsive advising in facilitating equitable access to international learning opportunities. Implications from this research may inform institutional strategies designed to expand access to study abroad and strengthen retention and student success initiatives for diverse student populations.<\/p>\n
\nCO-COMMITTEE CHAIR<\/strong>: Dr. Md. Jobair Bin Alam
\n
\nTITLE<\/strong>: MULTI-TASK GROWING INTERPRETABLE NEURAL NETWORK FOR MULTI-TARGET SYMBOLIC REGRESSION<\/p>\n
\nCO-COMMITTEE CHAIR<\/strong>: Dr. Lujun Zhai<\/p>\n
\nCO-COMMITTEE CHAIR<\/strong>: Dr. Ripendra Awal<\/p>\n