Publications

Kidney transplantation is the preferred treatment for people suffering from end-stage renal disease. Successful kidney transplants still fail over time, known as graft failure; however, the time to graft failure, or graft survival time, can vary significantly between different recipients. A significant biological factor affecting graft survival times is the compatibility between the human leukocyte antigens (HLAs) of the donor and recipient. We propose to model HLA compatibility using a network, where the nodes denote different HLAs of the donor and recipient, and edge weights denote compatibilities of the HLAs, which can be positive or negative. The network is indirectly observed, as the edge weights are estimated from transplant outcomes rather than directly observed. We propose a latent space model for such indirectly-observed weighted and signed networks. We demonstrate that our latent space model can not only result in more accurate estimates of HLA compatibilities, but can also be incorporated into survival analysis models to improve accuracy for the downstream task of predicting graft survival times.

The stochastic block model (SBM) is one of the most widely used generative models for network data. Many continuous-time dynamic network models are built upon the same assumption as the SBM: edges or events between all pairs of nodes are conditionally independent given the block or community memberships, which prevents them from reproducing higher-order motifs such as triangles that are commonly observed in real networks. We propose the multivariate community Hawkes (MULCH) model, an extremely flexible community-based model for continuous-time networks that introduces dependence between node pairs using structured multivariate Hawkes processes. We fit the model using a spectral clustering and likelihood-based local refinement procedure. We find that our proposed MULCH model is far more accurate than existing models both for predictive and generative tasks.

 This research paper introduces a new learning tool to increase student interests in power engineering. Insufficient visualization of the real power system operation and boring theory knowledge cause a general lack of interest in power systems. This paper realized the design of 3D virtual power system using 3ds Max and Unity3D software. Microgrid system, one significant example of a power system, is selected to demonstrate the 3D visualization. The built 3D visualization of microgrid can vividly show the details and function of each component in the Microgrid. Therefore, it can greatly increase students’ enthusiasm for learning the power engineering. Moreover, the first time application of Unity3D in the microgrid system laid the foundation for the feasibility of virtual technology in the application of engineering education to further attract more students in all engineering areas. 

In order to improve cognitive skills, we developed a series of training programs using the Unity 3D Game Engine through the EPOC headset with the EEG technique from EMOTIV. To keep participants’ interest and improve training efficiency, each program was developed as a game-like application with three levels of difficulty. The programs allow users to play games according to the game rules. Our programs focus on such cognitive abilities as reaction speed, flexibility, attention span, memory and problem solving. The detection system is able to analyze the user’s training performance, thus automatically channeling the user to a suitable training level. A preliminary testing was conducted for a short term, and the result was positive. This pilot study is used as proof of principles and serves as a basis for developing more comprehensive cognitive training programs in the future.