Dr. Ying Zhao is a research professor at the Naval Postgraduate School (NPS). Her research focused on data sciences, machine learning, and artificial intelligence methods including lexical link analysis (LLA), collaborative learning agents (CLA), and reinforcement learning for search, visualization, and analysis for defense military applications such as semantic and social networks, common tactical air pictures, combat identification, logistics, and mission planning. Since joining NPS, Dr. Zhao has been a principal investigator (PI) on many projects awarded for DoD research projects listed below. Dr. Zhao is a co-author of four U.S. patents in knowledge pattern search from networked agents, data fusion, and visualization for multiple anomaly detection systems. She received her PhD in mathematics from MIT and is the co-founder of Quantum Intelligence, Inc.
The projects in the NPS include the following:
Cognitive and Agile Radio, funded by
ONR's Naval Enterprise Partnership Teaming with Universities for National
Excellence - NEPTUNE 2.0 (2021).
• Deep Analytics for Expeditionary Advanced Base Operations (EABO) Shaping Flexible C2 Organizational Structure, funded by NPS Naval Research Program (2021).
• Deep Analytics for Readiness Impacts of Underfunding Spares Backlogs, funded by NPS Naval Research Program (2021).
• Supply and Maintenance Predictive Modeling and Simulation Analysis Tool, funded by NPS Naval Research Program (2020).
• Leverage Artificial Intelligence (AI) to Learn, Optimize, and Win (LAILOW) for a Complex Enterprise - Navy Logistics and Supply Chain, funded by ONR's Naval Enterprise Partnership Teaming with Universities for National Excellence - NEPTUNE 2.0 (2020).
• Design, Demonstrate and Proof of Concept of Using the Explainable Reinforcement Learning (xRL) in Soar for Combat Identification (CID) (2019, 2020), participated the Rim of the Pacific (RIMPAC) Exercise.
• Data Mining, Machine Intelligence, and Al for Application in Over-The-Horizon Targeting (OTH-T) for Anti-Access Area Denied (A2AD) Environments (2019, 2020)
• Demonstrating a Tactical Server Concept Leveraging Big Data, Deep Analytics, Machine Learning (ML), and Artificial Intelligence (AI) Algorithms (2019)
• Leverage Artificial Intelligence (AI) to Optimize Global Material Distribution (2019) Report Poster
• Modeling Large-Scale Warfighter Cognitive Reasoning and Decision- Making Using Machine Learning (ML), Artificial Intelligence (AI), and Game Theory (GT) (2019)
• Deep Analytics for MarineNet with Personalized Learning - Using the Pilot Data (Continuation) (2019)
• Big Data ML and AI for Combat ID and Combat Systems – Design, Demonstrate and Proof of Concept (2018)
• Deep Analytics for Content Management System (CMS) (2018)
• Reinforcement learning for Modeling Large-Scale Cognitive Reasoning (2017,2018)
• Big Data Architecture and Analytics for Common Tactical Air Picture – Efficient Implementation (2017)
• The Hatch LLA Data Depiction with MMOWGLI (2017)
• A Big Data and Deep Learning Model for the CSAAC RDK Cloud (2017)
• Applying the SOAR Architecture to Model Cognitive Functions in a Kill Chain (2016)
• Big Data Architecture and Analytics for Common Tactical Air Picture – Practical Applications (2016)
• Big Data Architecture and Analytics for Common Tactical Air Picture (2015)