The talent gap, a mismatch between the workers that employers need and the workforces that labor markets provide, exists across many sectors of labor markets, and is even worse for STEM-related labor markets and minority groups. There is a critical need to understand and study the talent gap and develop helpful and actionable recommendations for different stakeholders including employees, employers, and education institutes to bridge the gap. This project collects and deeply analyzes the large wealth of career, education and job (CEJ) data, and develops quantitative methods for measuring the micro and macro level talent gap and a suite of recommendation techniques for filling the gap. This study contributes significantly to the limited knowledge base in the area of CEJ data analytics. The findings, tools and documents will help researchers in fields such as education and economics better study the talent gap challenge of U.S. labor markets. This project involves, through courses and thesis projects, graduate and undergraduate students to enhance their knowledge and skills in solving real problems. This project also involves K-12 students through summer camps and encourages them to earlier prepare their education and career development. This project focuses on three major tasks: collecting and modeling heterogenous CEJ data with machine learning methods, measuring and interpreting the talent gap, and developing recommendation solutions to bridge the gap. To tackle the first task, this project collects a variety of CEJ data from multiple sources such as online professional networks and develops advanced embedding methods. To solve the second task, this project develops novel quantitative measurements and customized visualization techniques based on the embedding results. To handle the third task, the project develops sophisticated recommendation methods for generating actionable suggestions for employees, employers and education institutes. The results of this project will be disseminated in the form of peer-reviewed publications, open-source software, tutorials, seminars, and workshops. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.