Research Overview
My research interests are centered on harnessing artificial intelligence methodologies to address complex spatiotemporal challenges in modern transportation systems, with a vision to advance operational intelligence in the era of big data. At the intersection of computational science and urban mobility, my work primarily focuses on three pillars:
1) Deep learning-based time series prediction and spatiotemporal anomaly detection;
2) Big Data Analytics for urban mobility pattern mining;
3) Deep reinforcement learning-based control and optimization;
2) Big Data Analytics for urban mobility pattern mining;
3) Deep reinforcement learning-based control and optimization;