Democratizing Data Science and Machine Learning to the End-User
Machine learning techniques represent powerful tools and has resulted in hypotheses that we use to focus and test the innovative concepts behind these solutions: (1) measurable benefits (efficiency, accuracy, and discovery) to end-users; (2) including machine learning models improves speed and accuracy; (3) comparing traditional data science against new machine learning techniques will identify changes in data science productivity. We funded investments against real-world problems using algorithmic solutions including deep learning, predictive analytics, translation, classification & clustering, and facial/object recognition. The results of this effort answer the hypothesis and determine strengths and weaknesses of exposing algorithms directly to non-expert end-users.
Nina is a Computer Science Software R&D Advisor from the DOE Sandia National Laboratories, detailed as a contractor to JIDO for over ten years. Her multi-disciplinary research background covers applied agent-based and artificial intelligence in diverse domains such as distributed computing, enterprise systems, advanced analytics, frameworks for cognitive encapsulation, computational terrorist recruitment models, video processing, wireless smart sensor, and pervasive computing devices. She provides JIDO with technical guidance for selecting software analytics and architectures used to detect and understand the Counter Threat Network domains, by modeling disparate big data to extract, integrate, visualize, mine, and fuse.