Publications

An interpretable adaptive sampling method that dynamically allocates inference-time compute for efficient LLM test-time scaling.

A dependency-aware auto-scaling framework for serverless computing using multi-expert consensus, workload forecasting, ensemble learning, and cost-aware resource allocation.

PerfMamba analyzes the runtime behavior, resource utilization, memory access patterns, scaling properties, and pruning opportunities of Mamba-1 and Mamba-2 selective state space models.

A fully automated LLM-driven AutoML framework for cross-lingual handwritten OCR using closed-loop neural architecture search with GPT-5, GPT-4o, and Claude Sonnet 4 across Arabic, English, and Persian scripts.