Aravinda Raman
Jatavallabha.

Compares five proprietary models across ambiguity, safety, and multi-step tasks with repeated trials. Distinguishes stable behavior from verified task correctness and examines how pricing changes value comparisons.

A practitioner-oriented survey connecting prompting methods, automatic optimization, prompt injection, and RAG poisoning. Organizes methods around accuracy, cost, and security trade-offs.

Coauthored research on incremental BERT and dynamic graphs for changes in language over time. My primary contribution was model design and experiments. Springer lists the author as J. Aravinda Raman.

Studies LSTM and bidirectional LSTM predictions using signal strength and packet data rate to inform LTE/WiFi offloading. My primary contribution was model design and experiments.

A comparative modeling study examining preprocessing, feature engineering, and classifier performance. Reported research performance should not be interpreted as clinical validation.

Studies sensitive-information leakage and prompting strategies on synthetic inputs. These experiments examine privacy behavior; they do not establish regulatory compliance for a deployed system.

The TDGNN framework investigates prediction in dynamic interaction networks using temporal information and graph structure.

A case study applying a seven-step ethical decision-making process to user behavior, technology limitations, and policy considerations.

Examines delay components and flight characteristics using regression models and LSTM variants. Explores which factors can inform flight planning.

Compares VGG-16, InceptionV3, XceptionNet, and MobileNet using error measures on pediatric X-ray data.

Investigates multi-head attention for historical sales, promotions, pricing, and external signals. This work is under review; no publication is claimed.

Explores an MMD-based distillation objective to reduce disparity while maintaining task performance. A manuscript, with no peer-reviewed publication status claimed.

A hierarchical transformer approach integrating BERT-based text encoding and visual features. Shared as an author-hosted manuscript.

Examines SVD-based augmentation and collaborative graph representations. This listing does not claim invention of the existing LightGCL method.