Author

Salma Elmalaki

3 papers indexed here

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Open access Oct 2026

NeuSym-HLS: Efficient Hardware Acceleration of Deep Neural Networks With Symbolic Distillation and High-Level Synthesis

The domain-specific hardware accelerators for deep neural network (DNN) inference have been widely adopted, particularly in resource-constrained edge computing scenarios but face a fundamental tradeoff among accuracy, on-chip resource utilization, and inference latency. Traditional compression techniques, such as pruni...

Chung-Mou Pan, Swarnalata Panigrahy, Salma Elmalaki et al. · IEEE Embedded Systems Letter... · 0 citations

#natural language process... Preprint Feb 2026

PACIFIC: Can LLMs Discern the Psychometric Traits Influencing Your Preferences? Personality-Driven Preference Alignment in LLMs

PACIFIC (Preference Alignment for Choices Inference via Five-factor Identity Characterization), a personality-driven preference alignment framework that uses Big-Five (OCEAN) traits as a principled "latent"signal for organizing and reasoning over user preference history, is introduced.

Tianyu Zhao, Siqi Li, Yasser Shoukry et al. · 0 citations

#machine learning Preprint Mar 2024

PEARL: Structural Privacy-Utility Control in Human-Centric CPS via Personalized Early-Exit Deep Reinforcement Learning

PEARL (Personalized Early-exit Adaptive Reinforcement Learning) reduces adversarial state-inference accuracy by 25.67% on average with a controlled 10-16% utility cost, establishing a practical, dynamically enforceable privacy-utility tradeoff.

Mojtaba Taherisadr, Salma Elmalaki · 2 citations