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.

Services:

  • Co-director, Leverhulme Doctoral Scholarship Programme in Behaviour Informatics, Institute of Neuroscience & School of Computing, Newcastle University  (5 fully funded 4-year PhD studentship each year for 2018/2019/2020; More details)
  • Associate Editor, ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/UbiComp, core A*) 2017-Now
  • Conference Session Chair, ACM UbiComp (core A*), 2017;  IEEE ISWC(core A*), 2018
  • Conference TPC Members, ACM Int'l Symposium on Wearable Computers (ISWC, core A*), 2017, 2018,  IEEE Int'l Conf. Multimedia and Expo (ICME), 2014
  • Journal/Conference 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), ACM Trans. Internet of Things (TIOT), IEEE Pervasive Computing, etc.
  • Grant Reviewer: Leverhulme Trust

Teaching and Tutoring:

  • 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 wearable sensors, videos, text etc.). 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, T-IP, AAAI, IMWUT/UbiComp, PR, etc.  Please contact me if you want to collaborate with us in such exciting fields!

Recent News:

  • Our Paper "Towards Reliable, Automated General Movement Assessment for Perinatal Stroke Screening in Infants Using Wearable Accelerometers" has been accepted by ACM IMWUT 2019!
  • Two New PhDs Yang Bai, and Tailin Chen joined the team, Welcome!
  • Our MSc Student, Yan Gao, gave a talk "Machine Learning Applications: from clinical assessment, visual recognition, to forensic surveillance" at Department of Computer Science, Oxford University!
  • Jie Su joined the team as a new PhD student, and Bing Zhai progressed to his PhD stage.
  • With TusPark, we co-hosted the monthly-meeting for Google Developer Group for Machine Learning (GDG). Thanks Dr. Colin Tan for organising and also the support from Google!
  • Our Paper "Triple Verification Network for Generalised Zero-shot Learning" has been accepted by IEEE TIP!
  • Yang visited Chinese Academy of Sciences Institute of Automation (CASIA). Many thanks to Prof. Liang Wang for hosting!
  • Bingzhang joined the machine learning group as the Postdoc RA. Welcome!
  • Rob successfully passed his Viva with minor correction, and now is working as a Postdoc RA in our group. Congrats!
  • Yang received the 3-year MRC innovation research fellowship! Well done!
  • Our Paper "Towards Universal Representation for Unseen Action Recognition" has been accepted by CVPR 2018
  • Newcastle University has officially joined the Alan Turing Institute as a university partner.
  • Yu gave a guest lecture on "Deep Learning for Human Activity Recognition" at UCL!
  • Yang joined the machine learning group as a Postdoc RA, welcome!
  • Bing joined the machine learning group as a MRes student!
  • We move to the new Urban Sciences building (Aug. 2017)!

 

 Group Members:

  • Yu Guan (Group Leader)
  • Yang Long (RA->Research Fellow, Zero-shot Learning, Computer Vision)
  • Dan Jackson(Senior RA, OpenMovement, UbiComp and Wearables, Sensor Design, Health and Physical Activity)
  • Ivan Poliakov (RA, Intake24, Software Engineering, Dietary and Nutrition)
  • Karim Ladha (RA, OpenMovement, Hardware Sensor Design and Implementation)
  • Bingzhang Hu (RA, Deep Learning, Metric Learning, Face Analysis, GAN)
  • Timur Osadchiy (PhD, Intake24, Recommender system, Dietary and Nutrition)
  • Bing Zhai (Mres->PhD, Pervasive and Wearable Sensing, Automated Sleep Assessment)
  • Tailin Chen(MSc->PhD, Deep Learning, Multimodal Fusion)
  • Jie Su (PhD, Deep Learning, Zero-shot Learning, GAN)
  • Yang Bai(MSc->PhD, Automated Fatigue Assessment, Wearable Sensing, Deep Learning)
  • Rob Thompson (Visiting Researcher, Wearables, Animal Behaviour Analysis)
  • Qiong Wang (Visiting Researcher, Deep Learning, Computer Vision)
  • Shidong Wang(Visiting Researcher, Computer Vision)
  • Peng Zhang (Visiting PhD, Reinforcement Learning)
  • Marie-Claire Pagano (Visiting PhD, Dog's Emotion Analysis and Behaviour Understanding)
  • Junyan Wang(MSc, Text Mining, GAN)

 

Previous Visiting Researchers:

  • Remco Benthem De Grave (Institute of Neuroscience, Newcastle University), Oct.-Dec. 2018
  • Prof. Michael Little (Ratio), July, Oct. Dec. 2018
  • Nick Olivier (Essex University), Aug. 2018
  • Dr. Ruoshui Liu (Cambridge University), Nov. 2017
  • Tom White (Cambridge University), Feb. 2017

2019

Generic compact representation through visual-semantic ambiguity removal
Long Y, Guan Y, Shao L, Pattern Recognition Letters186-192

2018

Remote cloud-based automated stroke rehabilitation assessment using wearables
Halloran S, Shi JQ, Guan Y, Chen X, Dunne-Willows M, Eyre J, Proceedings - IEEE 14th International Conference on eScience, e-Science 2018302-302
Triple Verification Network for Generalised Zero-shot Learning
Zhang H, Long Y, Guan Y, Shao L, IEEE Transactions on Image Processing506-517
Mobile based continuous authentication using deep features
Centeno MP, Guan Y, van Moorsel A, EMDL 2018 - Proceedings of the 2018 International Workshop on Embedded and Mobile Deep Learning19-24
Active learning in multi-domain collaborative filtering recommender systems
Guan X, Li C-T, Guan Y, Proceedings of the 33rd Annual ACM Symposium on Applied Computing (SAC'18)1351-1357
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

2017

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

2016

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

2015

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