Efficient AI: Optimizing Models for Performance and Explainability in the Real World

ABSTRACT :

My research focuses on optimizing large AI models for efficient real-world use. Working at the intersection of AI and systems engineering, I develop techniques to make powerful AI run smoothly on various devices. I work on model compression methods like pruning, quantization, and knowledge distillation. These approaches shrink AI models and reduce their computational needs while maintaining high performance. An important aspect of my research is understanding how compression affects AI explainability. As we make models more efficient, it's crucial to ensure we can still interpret their decision-making processes. The goal of my work is to bridge the gap between cutting-edge AI capabilities and practical deployment constraints.

KEYWORDS : Compression, Pruning, Quantization, Knowledge Distillation, Explainability