AI Systems Lab
Our lab studies the design and algorithms for robust and scalable AI systems, and the modelling and inference challenges arise when such systems interact with a diversity of entities in the physical world.
The work of our lab often falls into the following three themes. First, the development of new Machine Learning (ML) methods that enable intelligent systems to perceive and reason about different forms of information, such as images/videos, points, trajectories and event streams. Second, the design of efficient algorithms that significantly reduce ML computation (both training and inference) with resource/hardware constraints in mind. Third, the applications of ML-powered analytics in managing and understanding data footprints generated by AI systems across spatial and temporal domains. The overarching goal of our research is to build next-gen AI systems that are ubiquitous, evolving as they work alongside us to rely less on human assistance but more on computational intelligence, and finally become dependable and reliable.
- Hongkai Wen
- Konstantin Klemmer (EPSRC Mathematical Science Research Fellow)
- Rosco Hunter
- Mo Wang (co-supervised with Quanying Liu @ SUSTech)
- Minghao Xu
- Hai Wang (visiting student from SEU, with Shuai Wang)
- Ajayi Olayinka (co-supervised with Tanaya Gupta @ Glasgow)
- Youyang Sha (MPhil)
- Lichuan Xiang
- Konstantin Klemmer (co-supervised with Stephen Jarvis, now at Microsoft Research)
- Kai Wu (co-supervised with Jiangeng Feng @ Fudan)
- Wenzhe Zhang (summer intern, now at Google, previously at UCSD)
- Tianyou Song (summer intern, now at Columbia U)
- Kun Li (summer intern, now at UIUC)
AutoCAML @ Samsung AI
We also work closely with the Automated (CAmbridge) Machine Learning (AutoCAML) lab at Samsung AI Centre Cambridge (SAIC), which is part of the Embedded AI team at SAIC.
If you are interested in joining or collaborating with us please feel free to make contact. For perspective students you might also want to check the current openings.