J. Pablo Muñoz is an AI Research Scientist at the Systems, Software, and Architecture Research Group at Intel Labs. His research interests primarily include techniques to improve model efficiency and performance. Pablo has successfully led the design and development of Parameter-Efficient Fine-Tuning (PEFT) solutions, which combine Neural Architecture Search (NAS) and have been integrated into Intel's OpenVINO NNCF. His research has been published at top-tier conferences, including AAAI, NeurIPS, NAACL, EMNLP, and AutoML. Pablo has also contributed to projects in large-scale video analytics, intelligent agentic middleware systems for dynamic allocation of computer vision pipelines, large-scale 3D skeletal reconstruction, and video curation. He received his Ph.D. from the City University of New York, where he successfully led the design and development of award-winning localization systems to assist visually impaired individuals in reaching indoor destinations.