The field of computer science is witnessing significant advancements in efficient computing and privacy-preserving techniques. Researchers are exploring innovative methods to reduce computational costs, improve model performance, and safeguard sensitive information. A key direction in this area is the development of model compression techniques, such as quantization and knowledge distillation, which enable the deployment of large models in resource-constrained environments while maintaining their performance. Additionally, there is a growing interest in designing hardware-aware optimizations and novel architectures that can efficiently execute machine learning workloads. Noteworthy papers in this area include: The paper on quantization's impact on privacy risk in large language models for code, which demonstrates a significant reduction in privacy risk and a positive correlation between task performance and privacy risk. The paper on TROOP, which proposes a set of hardware optimizations to achieve at-the-roofline performance for vector processors on low operational intensity workloads, resulting in significant speedups and improved energy efficiency.