Research Projects

I've had a broad interest in artificial intelligence for the real world, especially in reinforcement learning and spatio-temporal data mining. Here are some of them:

Learning to Simulate Papers: PADS'23, ERA'23, KDD'22, AAAI'21, ICDE'21, ECML-PKDD'20, AAAI'20 Workshop Realistic simulators are a step closer towards policymaking for the real world. We investigate how to build realistic simulators from real world data.

Simulator/Game Environment Building Project websites: Honor of Kings (王者荣耀), LibSignal, CityFlow, Epidemic, Product Allocator Simulators are the foundation of reinforcement learning. We built a bunch of simulators for various applications, including MOBA Game, transportation, epidemic and product allocation.

Trustworthy Deep Learning Papers: AAAI'24a, AAAI'24b, ICDM'23, CDC'23a, CIKM'23, KDD'23, IJCAI'23, ERA'23, AAAI'23, IAAI'22, IJCAI'21a, IJCAI'21b, USENIX Security'21, NeurIPS'20 Workshop The project investigates different aspects of trustworthy deep learning, including robust modeling for deep learning models with physics, and for reinforcement learning with offline data and adversarial policy training.

Deep Reinforcement Learning for Traffic Signal Control Papers: Survey (Arxiv), Survey(KDD Explorations), AAAI'24a, CDC'23a, CDC'23b, CASE'23, IJCAI'23, AAAI'23, AAAI'20, KDD'19, CIKM'19a, CIKM'19b, KDD'18 The project systematically investigates "smart" traffic light control systems using deep reinforcement learning and evaluate its effectiveness on both synthetic and real-world traffic data.

Spatio-temporal Data Mining Papers: ICDM'23, ERA'23a,, ERA'23b, AAAI'21, NeurIPS'20 Workshop, AAAI'19, TKDD'19, WWW'19, PAKDD'18, CIKM'16 This project investigated the spatial-temporal prediction problems with applications in smart cities.