Title: Graph Neural Network-Based Anomaly Detection in Multivariate Time Series
Speaker: Yunan Huang
Given high-dimensional time series data (e.g., sensor data), how can we detect anomalous events, such as system faults and attacks? More challengingly, how can we do this in a way that captures complex inter-sensor relationships, and detects and explains anomalies which deviate from these relationships? Recently, deep learning approaches have enabled improvements in anomaly detection in high-dimensional datasets; however, existing methods do not explicitly learn the structure of existing relationships between variables, or use them to predict the expected behavior of time series. Our approach combines a structure learning approach with graph neural networks, additionally using attention weights to provide explainability for the detected anomalies. Experiments on two real-world sensor datasets with ground truth anomalies show that our method detects anomalies more accurately than baseline approaches, accurately captures correlations between sensors, and allows users to deduce the root cause of a detected anomaly.
Supervisor: Yongfang Dai
Title: Real-Time Rendering
Speaker: Jiahao Chen
Shadows are important for creating realistic images and in providing the user with visual cues about object placement. Because shadows are so important for realistic rendering, this area is getting a lot of attention, especially in real time, where we not only have to think about how to get a more realistic scene, but also have to be limited by hardware performance. These two goals — realism and performance — are difficult to reconcile completely, especially with shadow algorithms. Therefore, a large number of shadow algorithms have been proposed in recent years. In particular, the algorithm of soft shadow correlation has increasingly become a major research direction because it can obtain more physically reliable results, and the related works are frequently published.
Supervisor: Xiao Liang