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Jie Wei, professor of computer science at T��ɫ��Ƶ, is the recipient of a $299,000 grant from the United States Air Force Office of Scientific Research to improve situational assessment and awareness (SAAW), which is critical to military operations, humanitarian assistance and disaster response (HADR) applications. The three-year project, in collaboration with Professor of SUNY Stony Brook’s Department of Computer Sci-ence, is entitled “Efficient, Robust and Explainable Situational Assessment and Awareness AI/ML System Using Multi-Modal Sensing and Deep Learning Ap-proaches.”

Wei and Ling will focus on enhancing three aspects of artificial intelligence and machine learning (AI/ML) methods:

• Data and computing efficiency so that the system can perform well with small data size and readily available computers, which are the norm in most AI military and HADR missions; 
   
• A robust defense against noisy data, human errors, and intentional adversarial attacks when the AI/ML system is deployed; and
   
• Models and systems that will provide means to justify and certify decisions with various techniques so that they are not simply black boxes but transparent, interpretable, justifiable and certifiable. 

“In this work, we will theoretically model four subsystems, namely, Sensor Parameter Model (SPM), Exploratory Data Analysis (EDA), Back-end Deep Learning (BDL) and Front-end Data Classification (FDC),” said Wei, whose research interests include multi-modal computing, computer vision, medical imaging, and machine learning.

“SPM utilizes theoretical models for sensing based on hardware estimation techniques to determine the salient processing required for feature and parameter alignment for multi-modal systems in transparent AI,” he added, “EDA enables academic and Air Force researchers and engineers to better visualize and explore data processing for interpretable AI of these multi-sensor multi-modal data.”

BDL, meanwhile, accumulates and generates more multi-sensor multi-modal data and users’ feedback to add new knowledge to the multi-sensor multi-modal FDC subsystem via adaptive training and cross-validation. And FDC verifies and validates theoretical performance with a prototype subsystem, and will be demonstrated in a monitored scene to achieve SAAW. 

“In this proposed research and development, more experience will be gained on the use of multi-sensor multi-modal sensors by use of cutting--edge signal/image processing, computer vision and machine learning, especially deep learning, techniques. This will lay out a solid foundation for our team at CCNY and SUNY Stony Brook to further collaborate with AF researchers,” said Wei.

About t��ɫ��Ƶ
Since 1847, T��ɫ��Ƶ has provided a high-quality and affordable education to generations of New Yorkers in a wide variety of disciplines. CCNY embraces its position at the forefront of social change. It is ranked #1 by the Harvard-based Opportunity Insights out of 369 selective public colleges in the United States on the overall mobility index. This measure reflects both access and outcomes, representing the likelihood that a student at CCNY can move up two or more income quintiles. Education research organization DegreeChoices ranks CCNY #3 nationally for social mobility. In addition, the Center for World University Rankings places CCNY in the top 1.8% of universities worldwide in terms of academic excellence. puts at $1.9 billion CCNY’s annual economic impact on the regional economy (5 boroughs and 5 adjacent counties) and quantifies the “for dollar” return on investment to students, taxpayers and society. At City College, more than 15,000 students pursue undergraduate and graduate degrees in eight schools and divisions, driven by significant funded research, creativity and scholarship. This year, CCNY launched its most expansive fundraising campaign, ever. The campaign, titled “Doing Remarkable Things Together” seeks to bring the College’s Foundation to more than $1 billion in total assets in support of the College mission. CCNY is as diverse, dynamic and visionary as New York City itself. View CCNY Media Kit.