We seek to build deep learning solutions that are way beyond just data-driven accurate predictors. In our opinion, an ideal model shall at least: (1) be robust to perturbations and attacks (therefore trustworthy); (2) be efficient and hardware-friendly (for deployments in practical platforms); and (3) be designed to respect individual privacy and fairness.
We are enthusiastic about the rising field of AutoML, on both consolidating its theoretical underpinnings and broadening its practical applicability. State-of-the-art ML systems consist of complex pipelines, with choices of model architectures, algorithms and hyperparameters, as well as other configuration details to be tuned for optimal performance. They further often need to be co-designed with multiple goals and constraints. We consider AutoML to be a powerful tool and a central hub, in addressing those design challenges faster and better.
We are interested in a broad range of computer vision problems, ranging from low-level (e.g, image reconstruction, enhancement and synthesis) to high-level topics (e.g., recognition, segmentation, and vision for UAV/autonomous driving). We are also growingly interested in several interdisciplinary fields, such as biomedical informatics, geoscience, and IoT.