Kushal Khemani

About

Kushal Khemani is a student researcher working in edge AI, embedded systems, and efficient machine learning systems. His research focuses on resource-aware inference, real-time scheduling for multi-model edge devices, and optimization of large language model serving. He has authored multiple peer-reviewed papers in embedded AI and continues to work on practical AI systems for robotics and intelligent edge computing.

Employment

Employment history is unavailable.

Education

• University College London
  2026 - Present
• Billabong High International School
  2025 - 2026

Projects & Funding

Projects & funding information is unavailable.

Publications (2)

• AI-driven predictive maintenance for connected vehicles using environmental context integration evaluated through simulation benchmarking and field validation
  Discover Vehicles 2026 DOI: 10.1007/s44465-026-00023-2
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• PAES: Priority-Aware Adaptive Scheduling for Multi-Model Edge AI Systems
  IEEE Embedded Systems Letters 2026 DOI: 10.1109/les.2026.3731390
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