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Biography
I'm an Assistant Professor at the 糖心原创 where I work on deployable AI which broadly includes ethical, fair, efficient and sustainable AI. Previously, I was a postdoctoral researcher in AI for Healthcare at the University of Oxford, under at the . I completed my PhD at the University of Edinburgh where I was supervised by Dr and co-supervised by Dr . I was fortunate to be partially supported through Facebook AI where I collaborated with Dr . I broadly work on computer vision applications and multimodal learning using principles of uncertainty. Before coming to Edinburgh, I spent two wonderful years as a masters student at Tsinghua University after being awarded a Chinese Government Scholarship on the recommendation of the university, under the supervision of Dr .
Expertise Summary
I specialize in developing deployable and sustainable AI systems, with a focus on energy-efficient, data-efficient, and memory-efficient deep learning. My expertise spans machine perception, computer vision, and their applications in robotics and healthcare. I have a strong research background, having published in top-tier conferences such as ECCV, ICLR, NeurIPS, AAAI, CVPR, ACCV and BMVC, and have experience addressing real-world constraints like limited labeled data and resource limitations. My work also emphasizes environmental sustainability in AI.
Research Summary
My current research focuses on developing efficient and sustainable artificial intelligence (AI) systems, particularly in the areas of machine perception and deep learning. I aim to create AI models… read more
Current Research
My current research focuses on developing efficient and sustainable artificial intelligence (AI) systems, particularly in the areas of machine perception and deep learning. I aim to create AI models that are both deployable in real-world scenarios and environmentally friendly by addressing challenges such as limited labeled data, resource constraints, and high energy consumption. This involves designing data-efficient and energy-efficient algorithms that maintain high performance while reducing computational demands. Additionally, I am exploring the environmental impact of AI technologies, advocating for Green AI practices to minimize carbon emissions associated with large-scale AI deployments. My interdisciplinary approach bridges computer vision, robotics, and healthcare applications, striving to develop AI solutions that are not only technically robust but also socially responsible and sustainable.