Tae-Kyun (T-K) Kim, PhD

Associate Professor (Senior Lecturer)

1017, Department of Electrical and Electronic Engineering

Imperial College London, South Kensington Campus

London, SW7 2AZ, UK

Webpage: https://labicvl.github.io

E-mail: tk.kim@imperial.ac.uk

Tae-Kyun (T-K) Kim is an Associate Professor and the director of Computer Vision and Learning Lab at Imperial College London, UK, since Nov 2010. He is also an adjunct professor at School of Computing, KAIST. He obtained his PhD from Univ. of Cambridge in 2008 and Junior Research Fellowship (governing body) of Sidney Sussex College, Univ. of Cambridge for 2007-2010. His research interests primarily lie in tree structured machine learning, on top of randomized forests and convolutional neural networks, for: articulated 3D hand pose estimation, face analysis and recognition by image sets and videos, 6D object pose estimation, active robot vision, activity recognition, object detection/tracking, which lead to novel active and interactive visual sensing. He has co-authored over 80 academic papers in top-tier conferences and journals in the field, and has co-organised HANDS workshops (in conjunction with CVPR15/CVPR16/ICCV17/ECCV18), and Object Pose workshops (in conjunction with ICCV15/ECCV16/ICCV17/ECCV18). He is the general chair of BMVC17 in London, and is Associate Editor of Image and Vision Computing Journal, and IPSJ Trans. on Computer Vision and Applications. He received KUKA best service robotics paper award at ICRA 2014, and 2016 best paper award by the ASCE Journal of Computing in Civil Engineering, and his co-authored algorithm for face image retrieval is an international standard of MPEG-7 ISO/IEC.

Research Interests:

Machine Learning for 3D Computer Vision, Man-Machine Interface, and Robotics

New open positions: Postdocs and PhD studentships in Computer Vision and Machine Learning

Imperial College London, Department of Electrical and Electronic Engineering

South Kensington, London, UK


We are looking for strong candidates for multiple postdoc positions. Topics include 6D object pose estimation, 3D object detection and tracking, GANs, data augmentation, face, deep reinforcement learning, robotics. Applicants should have a PhD in computer vision or machine learning, with a strong track record in CVPR/ICCV/ECCV, NIPS/ICML/ICLR, or PAMI/IJCV/TIP. Candidates who are able to carry highest quality research independently and to co-supervise PhD students are pursued. If you are interested, please send me an email titled ‘postdoc application 2019’, where you include your CV and the earliest starting date.

PhD studentships

We also have multiple phd studentships, which cover maintenances and Home/EU tuition fees. Overseas candidates should be able to cover the tuition fee difference themselves (see here: https://www.imperial.ac.uk/study/pg/fees-and-funding/tuition-fees/2018-19/postgraduate-research-programmes/faculty-of-engineering/). Topics include GANs, data augmentation, face, deep reinforcement learning, 2D/3D face by depth images. Ideal candidates should have a first-class MEng/MSc degree, with a clear evidence on research potentials, e.g. some publications. Our preference will be given to those who are able to carry research projects independently. If you are interested, please send me an email titled ‘phd application 2019’, where you include your CV, fee status (home/eu or overseas), and the earliest starting date.

Tae-Kyun Kim