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To give an impressive presentation, we always use visual aids to emphasize key points. However, we usually use presentation slides. Then our team came up with a question – Why don’t we combine AR technology with presentations? MAXAR is a mobile application which utilizes AR technology to facilitate presentations. Users prepare their visual aids in our application by adding 3D elements or 3D text. They can either record their presentations and upload them to the Internet or deliver their presentations directly in front of the audience using their own device.
Our application shows an incredible potential in STEM education, e-learning, and entertainment.

The demand for property is steadily rising with the significant market size of real estates across the world. Looking at the existing property searching applications, they only provide simple filtering functionalities about the properties, such as price, size, etc. However, in reality, users might have some complex requirements about the surroundings of their desired properties. To deal with this unsatisfied demand, we choose to facilitate an advanced query type – Spatial Pattern Matching (SPM), which is a new type of spatial group keyword query raised in recent research. Led by our supervisor, Dr. Reynold C. K. Cheng, its solutions have been well-discussed in theory. In our project, we want to fit these theories in this real-life scenario by resolving practical issues, as well as realizing some additional functionalities related to property searching. In the end, we have developed a standalone web-based application to demonstrate the importance and practicality of this newly proposed functionality.

This project investigates how mutual funds leverage credit derivative by studying their routine filings to the U.S. Securities and Exchange Commission. Credit derivatives are used to transfer credit risk related to an underlying entity from one party to another without transferring the actual underlying entity. Therefore, we developed both rule-based and NLP based methods to extract CDS information from thousands of files and aggregate into a dataset and further developed a one of its kind search engine to provide quick access to historic data

In our project, we implement neuro-evolution using NEAT and reinforcement learning using Sarsa(λ) on micromanagement scenarios in StarCraft II involving the small-scale precise control of combat units. Using our developed training framework for applying NEAT to StarCraft II, we evolved neuroevolutionary agents that learned to demonstrate precise hit-and-run strategies to beat the in-game AI in ranged vs melee matchups. Our reinforcement learning agents using Sarsa(λ) learned to be successful in more complex micromanagement scenarios involving enemy engagement selection and timing. Our results serve as a proof-of-concept of the benefits and potential of the applications of these techniques in video games and represent meaningful contributions to the wider video gaming and artificial intelligence communities.