The field of homomorphic encryption and secure computation is rapidly advancing, with a focus on improving the efficiency and practicality of Fully Homomorphic Encryption (FHE) schemes. Researchers are exploring new approaches to bootstrapping, a critical component of FHE, and developing innovative frameworks for characterizing and optimizing FHE performance. Additionally, there is a growing interest in applying homomorphic encryption to emerging areas such as machine learning and spiking neural networks, with a focus on enabling secure and private inference. Notable papers in this area include: Bootstrapping as a Morphism, which introduces a new geometric perspective on bootstrapping, achieving a significant asymptotic improvement over the state of the art. CryptOracle, a modular framework for characterizing FHE, which provides a comprehensive benchmark suite, hardware profiler, and predictive performance model. FHEON, a configurable framework for developing privacy-preserving neural networks using homomorphic encryption, which achieves high accuracy and low latency on various CNN architectures. PrivSpike, a privacy-preserving inference framework for deep spiking neural networks, which supports arbitrary depth SNNs and introduces novel algorithms for evaluating activation functions.