Nina Berry

Chevron down

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.

As Featured In

Original
Original
Original
Original
Original
Original

Partners & Attendees

Intel.001
Nvidia.001
Acc1.001
Ibm watson health 3.001
Rbc research.001
Mit tech review.001
Kd nuggets.001
Facebook.001
Graphcoreai.001
Maluuba 2017.001
Twentybn.001
Forbes.001
This website uses cookies to ensure you get the best experience. Learn more