Dr. Yue Yu

Investigates the role played by informational frictions in college and the workplace. We estimate a dynamic structural model of schooling and work decisions, where individuals have imperfect information about their schooling ability and labor market productivity. We take into account the heterogeneity in schooling investments by distinguishing between two- and four-year colleges, graduate school, as well as science and non-science majors for four-year colleges. Individuals may also choose whether to work full-time, part-time, or not at all. A key feature of our approach is to account for correlated learning through college grades and wages, whereby individuals may leave or re-enter college as a result of the arrival of new information on their ability and productivity. Our findings indicate that the elimination of informational frictions would increase the college graduation rate by 9 percentage points, and would increase the college wage premium by 32.7 percentage points through increased sorting on ability.

Investigates the role played by informational frictions in college and the workplace. We estimate a dynamic structural model of schooling and work decisions, where individuals have imperfect information about their schooling ability and labor market productivity. We take into account the heterogeneity in schooling investments by distinguishing between two- and four-year colleges, graduate school, as well as science and non-science majors for four-year colleges. Individuals may also choose whether to work full-time, part-time, or not at all. A key feature of our approach is to account for correlated learning through college grades and wages, whereby individuals may leave or re-enter college as a result of the arrival of new information on their ability and productivity. Our findings indicate that the elimination of informational frictions would increase the college graduation rate by 9 percentage points, and would increase the college wage premium by 32.7 percentage points through increased sorting on ability.

Investigates the role played by informational frictions in college and the workplace. We estimate a dynamic structural model of schooling and work decisions, where individuals have imperfect information about their schooling ability and labor market productivity. We take into account the heterogeneity in schooling investments by distinguishing between two- and four-year colleges, graduate school, as well as science and non-science majors for four-year colleges. Individuals may also choose whether to work full-time, part-time, or not at all. A key feature of our approach is to account for correlated learning through college grades and wages, whereby individuals may leave or re-enter college as a result of the arrival of new information on their ability and productivity. Our findings indicate that the elimination of informational frictions would increase the college graduation rate by 9 percentage points, and would increase the college wage premium by 32.7 percentage points through increased sorting on ability.

Investigates the role played by informational frictions in college and the workplace. We estimate a dynamic structural model of schooling and work decisions, where individuals have imperfect information about their schooling ability and labor market productivity. We take into account the heterogeneity in schooling investments by distinguishing between two- and four-year colleges, graduate school, as well as science and non-science majors for four-year colleges. Individuals may also choose whether to work full-time, part-time, or not at all. A key feature of our approach is to account for correlated learning through college grades and wages, whereby individuals may leave or re-enter college as a result of the arrival of new information on their ability and productivity. Our findings indicate that the elimination of informational frictions would increase the college graduation rate by 9 percentage points, and would increase the college wage premium by 32.7 percentage points through increased sorting on ability.

The indoor positioning architecture based on multisource fusion has been proven effective in improving the accuracy and robustness of indoor positioning. However, existing multisource fusion positioning methods have difficulty achieving a balance between autonomy, precision, and intelligence, particularly for large-scale and complex indoor city scenes. Although wireless positioning technology has advantages such as low cost, wide coverage, and easy maintenance, it still faces issues such as poor autonomy, low accuracy, and compatibility when applied to large indoor spaces. Acoustic positioning technology features low cost, high accuracy, and good compatibility, but requires additional base station assistance. Based on the complementarity of wireless and acoustic signals in spatial distribution and precision indicators, this project proposes a wireless-acoustic fusion positioning architecture for large-scale indoor scenes. The project aims to use AI-driven crowdsourcing technology to construct navigation databases for large scenes, and to use environment-assisted and deep learning-driven acoustic ranging technology to achieve high-precision fingerprinting and ranging. Ultimately, the project will combine wireless, acoustic, and sensor positioning sources using a multisource fusion positioning architecture assisted by transfer learning, forming a complete design theory and method for electric-acoustic-sensor combination positioning. Specific research objectives include (1) studying algorithms for constructing and updating crowdsourced navigation databases for large-scale indoor scenes; (2) designing high-precision acoustic ranging algorithms based on complex indoor environments and motion patterns; and (3) exploring a multisource fusion positioning architecture assisted by transfer learning. This project is expected to develop a new large-scale indoor positioning architecture that combines the advantages of wireless and acoustic positioning technologies.