I am an Electronic Engineer specializing in hardware/software co-design for Brain-Inspired Computing. Currently pursuing a Ph.D. in Electrical and Computer Engineering at Purdue University, my research focuses on developing neuro-inspired machine learning algorithms optimized for emerging hardware technologies. Some of my recent work includes designing energy-efficient ADC-Less in-memory computing hardware for Spiking Neural Networks, resulting in substantial energy savings and latency reductions, as well as proposing a temporal local learning rule for training Deep SNNs inspired by the STDP mechanism, achieving performance comparable to BPTT.My industry experience includes an internship at Kilby Labs (Texas Instruments), where I conducted research into hardware-aware neural architecture and quantization search for low-power devices. Previously, as a Research Assistant at the National Institute for Research and Training in Telecommunications (INICTEL-UNI) in Lima, Peru, I contributed to various machine learning projects, integrating algorithms into low-power electronic systems for real-time inference.Passionate about exploring the intersection between neuroscience, VLSI, and artificial intelligence. Committed to continuous learning, I am keen on seeking opportunities to collaborate on projects in Brain-Inspired Computing