Yu Guan

Yu.Guan@ncl.ac.uk | Faculty

Yu Guan

I am a lecturer in data science and I lead the machine learning group in open lab. My research interests are machine learning and its various applications such as behaviour analysis, wearable and ubiquitous computing, computer vision and biometrics, etc. My publication list can be found through google scholar citations. Before joining Newcastle University, I received my PhD degree at the department of computer science, University of Warwick in 2015.


  • Associate Editor, ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/UbiComp, core A*) 2017-Now
  • Session Chair, ACM UbiComp (core A*), 2017
  • TPC Members, ACM Int'l Symposium on Wearable Computers (ISWC, core A*), 2017, 2018,  IEEE Int'l Conf. Multimedia and Expo (ICME), 2014
  • Regular Reviewers, IEEE Trans. Pattern Analysis and Machine Intelligence (T-PAMI, core A*), International Journal on Computer Vision (IJCV, core A*), Pattern Recognition (PR, core A*), ACM Multimedia (MM, core A*), ACM ISWC(core A*), ACM UbiComp (core A*), IEEE Trans. Mobile Computing (TMC core A*), IEEE Trans. Information Forensics and Security (TIFS), IEEE Trans. Circuits and Systems for Video Technology (TCSVT), IEEE Trans. Cybernetics (TCYB), IEEE Pervasive Computing, etc.

Teaching and Tutoring:

  • Module Leader, CSC8635 "Machine Learning with Project", School of Computing, Newcastle University, 2018
  • Module Leader, CSC8111 "Machine Learning", School of Computing, Newcastle University, 2017-Now
  • Guest Lecturer, "Deep Learning for Human Activity Recognition", for MSc Module "Introduction to Deep Learning", Department of Computer Science, UCL, London, Oct. 2017
  • Tutorial Lecturer, "Deep Learning For Ubiquitous Computing", ACM UbiComp, (Co-hosting with T. Ploetz, Georgia Tech, N. Lane, Oxford University&Bell Labs, and S. Bhattacharya, Bell Labs), Maui, Hawaii, US. Sept. 2017.
  • Invited Speaker, "Machine Learning for Human Behaviour Analysis", Bell Labs, Cambridge, Feb. 2017
  • Guest Lecturer, "Deep Neural Networks", for MSc Module "Machine Learning", School of Computing Science, Newcastle University, Dec. 2016
  • Tutorial Lecturer, "Deep Learning and its Mobile Applications", the 8th Int'l Conference on Mobile Computing, Application and Services (MobiCASE), (Co-hosting with T. Ploetz, Georgia Tech, N. Lane, UCL&Bell Labs, and S. Bhattacharya, Bell Labs), Cambridge, Nov. 2016.



About Machine Learning Group

Our research agenda is to develop state-of-the-art machine learning algorithms for real-world applications. One of our major research directions is computational behaviour analysis in different scenarios (e.g., identification/activity recognition/anomaly detection) based on various data sources (e.g., from videos/wearable sensors). In terms of methodologies, we are very interested in deep learning, zero-shot learning, and multimodal fusion. Our group members have substantial experience in publishing papers at top-tier (applied) machine learning venues such as T-PAMI, CVPR, AAAI, IMWUT/UbiComp, PR, etc.  Please contact me if you want to collaborate with us in such exciting fields!

Recent News:


 Group Members:

  • Yu Guan (Faculty and Group Leader, Deep Learning, Behaviour Analysis, Wearables, Computer Vision and Biometrics)
  • Yang Long (Research Fellow, Deep Learning, Zero-shot Learning, Computer Vision, Wearables)
  • Rob Thompson (Research Associate, Wearables, Animal Behaviour Analysis)
  • Bingzhang Hu (Research Associate, Deep Learning, Metric Learning)
  • Bing Zhai (PhD, Wearables, Sleep Quality Assessment)
  • Peng Zhang (Visiting PhD, Reinforcement Learning)


BSc/MSc Student Projects (2017/2018):

  • Yang Bai (Attention models for statefull deep LSTM for human activity recognition using wearables)
  • Tailin Chen (On combining data from multiple sources for robust automated recognition)
  • Joss Cousins (Automated Archery Skill Assessment using Wearables)
  • Xinchao Cheng (Improved Deep LSTM Ensemble for Human Activity Recognition using wearables)
  • Steve Cathcart (A comparison of sensing technology for the analysis of equine gait)
  • Wenhua Chen (Designing smart slope in cities: a machine learning approach)
  • Shaoxuan Dong (Automated Compensation Detection during Robotic Stroke Rehabilitation Therapy)
  • Yan Gao (Infant Perinatal Stroke Detection using Wearables)
  • Christopher Gill (An AI-based traffic analysis system for future smart city)
  • Kristian Kalda (Face Landmarking and Expression Analysis using Deep Learning)
  • Robert Nixon (Behaviour Analysis for Tennis Players using Wearables)
  • Guillermo Chibas Puente (Stock Market Prediction using Machine Learning Techniques)
  • Cameron Smith (Object detection using deep learning)
  • Junyan Wang (Depression Detection through social networks analysis)


Previous Visiting Researchers:

  • Prof. Michael Little (Co-director, Dartington Social Research Unit), July 2018
  • Dr. Ruoshui Liu (Hanwei Ltd & Cambridge University), Nov. 2017
  • Tom White (Cambridge University), Feb. 2017


Inference of a compact representation of sensor fingerprint for source camera identification
Li R, Li C-T, Guan Y, Pattern Recognition556-567
Deep Learning for Human Activity Recognition in Mobile Computing
Plotz T, Guan Y, Computer50-59
Generic compact representation through visual-semantic ambiguity removal
Long Y, Guan Y, Shao L, Pattern Recognition LettersEpub ahead of print


Matrix Factorization with Rating Completion: an Enhanced SVD Model for Collaborative Filtering Recommender Systems
Guan X, Li C, Guan Y, IEEE Access27668-27678
Ensembles of deep LSTM Learners for Activity Recognition using Wearables
Guan Y, Ploetz T, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies


Enhanced SVD for Collaborative Filtering
Guan X, Li CT, Guan Y, Advances in Knowledge Discovery and Data Mining (PAKDD 2016)503-514


On Reducing the effect of Covariate Factors in Gait Recognition: A Classifier Ensemble Method
Guan Y, Li C-T, Roli F, IEEE Transactions on Pattern Analysis and Machine Intelligence1521-1528
Copyright 2018 Open Lab