Yaser Banad

Yaser Banad

The end of conventional Dennard scaling and the widening gap between memory bandwidth and arithmetic throughput have made the von Neumann partition a structural bottleneck rather than a transient one. Two-dimensional (2D) materials, with atomically thin geometries, electrically tunable carrier densities, and large optical responses, offer a unified platform on which to build devices that compute where they store, process events rather than clock cycles, and shift workload into the optical domain. This perspective surveys progress along three converging thrusts, graphene and graphene nanoribbon transistors as scalable channel materials, oxide and 2D-integrated memristors for in-memory analog compute, and silicon-compatible 2D photonic and thermal-emitter structures for optical computing primitives. Our central argument is that the 2D-materials community has spent a decade producing record devices, and the next decade will be decided by who first integrates three of them on a single semiconductor wafer.

Arash Ahmadi, Sarah Sharif, Yaser Banad

Analyzing the human factors behind aviation accidents is crucial for preventing future incidents, yet traditional methods using the Human Factors Analysis and Classification System (HFACS) are limited by scalability and consistency. To address this, we introduce an automated HFACS classification framework for aviation safety analysis that utilizes Reinforcement Learning with Group Relative Policy Optimization (GRPO) to fine-tune a Llama-3.1 8B language model. Our approach incorporates a multi-component reward system tailored for aviation safety analysis and integrates synthetic data generation to overcome class imbalance in accident datasets. The resulting GRPO-optimized model achieved noticeable performance gains, including a 350% increase in exact match accuracy (from 0.0400 to 0.1800) and an improved partial match accuracy of 0.8800. Significantly, our specialized model outperforms state-of-the-art LLMs (Large Language Models), including GPT-5-mini and Gemini-2.5-fiash, on key metrics. This research also proposes exact match accuracy in multi-label HFACS classification problem as a new benchmarking methodology to evaluate the advanced reasoning capabilities of language models. Ultimately, our work validates that smaller, domain-optimized models can provide a computationally efficient and better solution for critical safety analysis. This approach makes powerful, low-latency deployment on resource-constrained edge devices feasible.