My research focuses on memory- and energy-efficient AI through algorithm–hardware co-design, with particular interests in hyperdimensional computing and in-memory computing. I develop hardware-aware algorithms that use sparsity, pruning, and quantization to reduce memory usage and computational cost while preserving accuracy. My current work combines dynamic sparse training with machine unlearning to remove the influence of selected data while maintaining network sparsity and performance on retained data.