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GenAI Optimization
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This direction focuses on making generative AI systems efficient enough for real deployment
without treating compression as a purely mechanical size-reduction problem. I work on methods
that connect LLM/VLM compression, neural architecture search, runtime orchestration, and formal
specifications with practical constraints such as energy, latency, fairness, and thermal safety.
The core question is how to optimize GenAI systems while preserving behavior that matters:
task reliability, safety constraints, edge deployability, and predictable performance.
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