Dr Jianbo Jiao PhD, FHEA, MIEEE

Jianbo’s research interests include Computer Vision and Machine Learning, with their applications to challenging and interesting vision and healthcare problems. He obtained his MSc from Peking University in 2015, and PhD in Computer Science from City University of Hong Kong in 2018, supported by the Hong Kong PhD Fellowship Scheme. He was a visiting scholar with the Beckman Institute at the University of Illinois at Urbana-Champaign from 2017 to 2018, working with Prof Thomas S. Huang. After that, He joined the BioMedIA group and VGG in Oxford as a postdoctoral researcher, where he worked on the EPSRC projects SeeBiByte and VisualAI with Prof Alison Noble and Prof Andrew Zisserman.

Jianbo is leading the MIx (Machine Intelligence + x) group, a Fellow of the Higher Education Academy (FHEA), and was a Royal Society Short Industry Fellow, a recipient of the Amazon Research Award. He has been consistently publishing at top venues in computer vision and machine learning (e.g. ICCV/CVPR/ECCV/BMVC/TPAMI/IJCV/NeurIPS/AAAI, etc.), and holds 3 patents. He has also been actively contributing to the community, and served as the Associate Editor for The Transactions on Machine Learning Research (TMLR), IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT), The Visual Computer (International Journal of Computer Graphics), Co-Chair of the 25th Annual Conference on Medical Image Understanding and Analysis (MIUA), Area Chair of various leading international conferences (e.g. NeurIPS, ICML, ICLR, ACM MM, etc.), Senior Program Committee member of the 30^th International Joint Conference on Artificial Intelligence (IJCAI), and on the program committee of several leading international conferences and journals. He has also been recognised by several Outstanding Reviewer Awards from the leading conferences and journals (e.g. CVPR, ECCV, ICCV, NeurIPS, ICML and MICCAI, etc). 

• Computer Vision
• Machine Learning
• Healthcare
• AI4Science

Jianbo’s research interests include Computer Vision, Machine Learning, Healthcare, and AI4Science. His current research interests mainly focus on learning representations in the open world, by exploring auxiliary information from different modalities (e.g. image/video, speech/audio, text/NLP, depth/stereo, gaze/saliency, motion, etc.) and reducing the dependency on supervision (e.g. self-supervised/weakly-supervised/semi-supervised learning, transfer learning). 

More information with an updated publication list please see his Google Scholar profile.