Bio

Carlos Francisco is a seasoned Biomedical Engineer and Data Scientist with a proven track record in startup environments and large-scale businesses, skillfully designing and enhancing materials and equipment for healthcare innovation. Known for leveraging cutting-edge technology, he constructs devices that push the boundaries of national security, rehabilitative technology, peak human performance, tissue engineering, and automated machine vision. Carlos's academic tenure marked a deep dive into the programming intricacies of medical image analysis for physiological modeling.

As an interdisciplinary researcher, his expertise spans feature engineering and applied mathematics for data analysis, distilling complex machine learning models into actionable insights. His adept use of mathematical inference, deep learning, artificial intelligence, and dynamic featurization has significantly advanced his research in neuromorphic processing. At Neurotech@Rice, Carlos led the charge in neurotechnology software research, pioneering EEG-processing methodologies and crafting educational workshops on machine learning. His endeavors in teaching have honed his pedagogical skills, shaping the future minds of the field.

His work with Local Competitive Algorithms (LCAs) and Spiking Neural Networks (SNNs) on Intel's Loihi2 neuromorphic processor revolutionized real-time adaptive learning, emulating biological processes through biomimicry inputs akin to those in Dynamic Vision Sensors (DVS) or event cameras for short. Carlos Francisco's interests are as diverse as they are profound, focusing on applied data science, deep learning applications in physiological modeling, quantum and neuromorphic computing, and the frontiers of neuroengineering where artificial intelligence meets computer vision.