There are moments on campus when individual achievement becomes collective momentum. The recent induction ceremony for the Beta Mu chapter of the Alpha Alpha Alpha Honor Society – Tri-Alpha – was one of them, as graduate students from Rutgers Graduate School–Camden stood alongside undergraduate peers from across Rutgers–Camden to be recognized for academic excellence as… continue reading
Balancing model accuracy with interpretability remains a central challenge in machine learning, particularly for classification tasks involving large datasets and continuous variables. This work examined how optimal sparse decision tree frameworks can be made more efficient and accessible without sacrificing their explainability or theoretical guarantees. The research was presented at the Graduate Poster Exhibition during… continue reading
Debates over censorship, curriculum, and literary value continue to shape how certain texts are taught in U.S. classrooms. This work examined why The Bluest Eye remains both contested and essential, focusing on how its narrative structure and thematic complexity invite deeper engagement with race, class, and gender rather than justifying its exclusion. The analysis was… continue reading
Writing teaches you to notice what others skim past. It teaches you to listen for subtext, to sit with uncertainty, and to understand that meaning is shaped as much by care as by structure. Those instincts run through the way Alexandra Liebman thinks about teaching. A student in the Teacher Education MAT program, she came… continue reading
Controlling when and where drugs are released inside biological systems remains a central challenge in targeted therapy. This work investigated how light-responsive nano-polymersomes could enable precise, on-demand cargo release by using pulsed laser irradiation to disrupt vesicle membranes with high spatiotemporal control. The research was presented at the Graduate Poster Exhibition during the 2025 SPARK!… continue reading
Designing and training deep learning models often requires advanced expertise, particularly when defining loss functions, optimizers, and model architectures. This work explored how large language models could lower those barriers by automating the creation of custom loss functions tailored to specific optimization goals. The capstone was presented at the Graduate Poster Exhibition during the 2025… continue reading
