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
  • Session Chair, ACM UbiComp (core A*), 2017;  IEEE ISWC(core A*), 2018
  • 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), ACM Trans. Internet of Things (TIOT), IEEE Pervasive Computing, etc.

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 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!
  • 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!
  • Newcastle University is ranked 141st in the 2019 QS World University Rankings
  • Newcastle's Computing Science is ranked 98th in the 2018 World University Rankings by the Times Higher Education
  • Our Paper "Triple Verification Network for Generalised Zero-shot Learning" has been accepted by IEEE Trans Image Processing (T-IP)
  • Yang visited Chinese Academy of Sciences Institute of Automation (CASIA) for a month. Many thanks to Prof. Liang Wang for hosting!
  • Bingzhang joins the machine learning group as the Postdoc Research Associate. Welcome!
  • Rob sucessfully passed his Viva with minor correction, and now is working as a Postdoc Research Associate 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
  • Yang's Paper "Towards Affordable Semantic Searching: Zero-shot Retrieval via Dominant Attributes" has been accepted by AAAI 2018
  • Newcastle University has officially joined the Alan Turing Instititue as an university partner.
  • Yu gave a guest lecture on "Deep Learning for Human Activity Recognition" at UCL!
  • Yang's Paper "Zero-shot Learning Using Synthesised Unseen Visual Data with Diffusion Regularisation" has been accepted by IEEE T-PAMI
  • Yang joins the machine learning group as a Postdoc Research Associate, welcome!
  • Bing joins 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, Deep Learning, 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)
  • Rob Thompson (PhD->RA, Wearables, Animal Behaviour Analysis)
  • Timur Osadchiy (PhD, Intake24, Recommender system, Dietary and Nutrition)
  • Bing Zhai (Mres->PhD, Wearables, Sleep Quality Assessment)
  • Tailin Chen(MSc->PhD, Deep Learning, Multimodal Fusion)
  • Jie Su (PhD, Deep Learning, Generative Models, Attribute Learning)
  • Yang Bai(MSc->PhD, Automated Fatigue Assessment, Wearable Sensing, Deep Learning)
  • Qiong Wang (Visiting Researcher, Deep Learning, Computer Vision)
  • Shidong Wang(Visiting Researcher, Computer Vision)
  • Peng Zhang (Visiting PhD, Reinforcement Learning)
  • Remco Benthem De Grave (Mres, Eating Behaviour Analysis, Wearable Sensing)
  • Junyan Wang(MSc, Text Mining, Computer Vision)

 

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 (Ratio), July, Oct. 2018
  • Nick Olivier (Essex University), Aug. 2018
  • Dr. Ruoshui Liu (Cambridge University), Nov. 2017
  • Tom White (Cambridge University), Feb. 2017

2018

Inference of a compact representation of sensor fingerprint for source camera identification
Li R, Li C-T, Guan Y, Pattern Recognition556-567
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
Triple Verification Network for Generalised Zero-shot Learning
Zhang H, Long Y, Guan Y, Shao L, IEEE Transactions on Image Processing506-517
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

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