I am an Associate Professor in the Dept. of Computer Science (DCS) at Warwick, and I lead Ubiquitous & Visual Computing Lab (UVLab) . I received my PhD degree from DCS, Warwick in 2015, and later worked as RA and lecturer in Newcastle University, before moving back to Warwick at 2022.

Research:

My research agenda is centred on machine learning for practical applications. That is, I develop robust machine learning algorithm for data modelling (e.g., for time-series sensor data, image/video data, etc.) and processing with an emphasis on solving the challenges facing in various real-world scenarios, to bridge the gap between theoretical research of machine learning and its practical applications. I am particularly interested in Computational Behaviour Analysis (e.g., activity/action recognition, 3D pose estimation, gait analysis), Human-AI Interaction (HAII), AI healthcare (e.g., wearable-based health assessment), Applied Machine Learning (e.g., cross-modal learning, physics informed learning, domain generalisation). Currently, I am an Associate Editor of ACM Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), an Associate Editor of Frontier in Computer Science. I've published 70+ peer-reviewed papers including top-venues like IEEE T-PAMI, T-IP, CVPR, ECCV, ACM IMWUT, ACM Multimedia, etc. My paper (collaborated with Prof. Thomas Ploetz, Georgia Tech) "Ensembles of Deep LSTM Learners for Activity Recognition using Wearable" ranks top-1 in citations in IMWUT (out of 1500+ papers)!

Selected Publications:

• S. Shao, Y. Guan, B. Zhai, P. Missier, T. Ploetz, “ConvBoost: Boosting ConvNets for Sensor-based Activity Recognition” ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (Ubicomp/IMWUT), 2023
• T. Chen, D. Zhou, J. Wang, S. Wang, Q. He, C. Hu, E. Ding, Y. Guan, X. He “Part-aware Prototypical Graph Network for One-shot skeleton-based Action Recognition”, IEEE Conf. Automatic Face and Gesture Recognition (FG), 2023 (Best Student Paper Award)
• Y. Bai, D. Zhou, S. Zhang, J. Wang, E. Ding, Y. Guan, Y. Long, J. Wang “Action Quality Assessment with Temporal Parsing Transformer”, ECCV, 2022
• J. Su, T. Lin, Z. Wen, Y. Guan “Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition”, ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (Ubicomp/IMWUT), 2022
• B. Zhai, Y. Guan, M. Catt, and T. Ploetz, “UbiSleepNet: Advanced Multimodal Fusion Techniques for Three-stage Sleep Classification using Ubiquitous Sensing”, ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (Ubicomp/IMWUT), 2021
• Y. Bai, J. Wang, Y. Long, B. Hu, Y. Song, M. Pagnucco, and Y. Guan “Discriminative Latent Semantic Graph for Video Captioning”, ACM Multimedia (MM), 2021
• T. Chen, D. Zhou, J. Wang, S. Wang, Y. Guan, E. Ding, and X. He “Learning Multi-granular Spatio-temporal Graph Network for Skeleton-based Action Recognition”, ACM Multimedia (MM), 2021
• S. Wang, Y.Ren, G. Parr, Y. Guan, and L. Shao “Invariant Deep Compressible Covariance Pooling for Aerial Scene Categorisation”, IEEE Trans. Geoscience and Remote Sensing (T-GRS), 2021
• A. Ratcliffe, B. Zhai, Y. Guan, D. Jackson, SWARM, S. Sneyd “Patient-Centred Measurement of Recovery from Day-case Surgery using Wrist-worn Accelerometers: A Pilot and Feasibility Study”, Anaesthesia, 2021
• J. Wang, Y. Bai, Y. Long, B. Hu, Z. Cai, Y.Guan, X. Wei “Query Twice: Dual Mixture Attention Meta Learning for Video Summarisation”, ACM Multimedia (MM), 2020
• B. Zhai, I. Perez-Pozuelo, E.Clifton, J. Palotti, and Y. Guan, “Making Sense of Sleep: Multimodal Sleep Stage Classification in a Large, Diverse Population using Movement and Cardiac Sensing”, ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (Ubicomp/IMWUT), 2020
• Y. Bai, Y. Guan, and WF. Ng., “Fatigue Assessment using ECG and Actigraphy Sensors”, ACM International Symposium on Wearable Computers (ISWC), 2020
• J. Wang, Y.Guan, and L. Shao “Multi-Granularity Canonical Appearance Pooling for Remote Sensing Scene Classification”, IEEE Trans. Image Processing (T-IP), 2020
• L. Tang, S.Halloran, J.Shi, Y. Guan, C. Cao, and J.Eyre “Evaluating Upper Limb Function after Stroke using the Free-living Accelerometer Data”, Statistical Method in Medical Research, 2020
• Y. Gao, Y. Long, Y. Guan, A. Basu, J. Baggaley, and T. Ploetz, “Towards Reliable, Automated General Movement Assessment for Perinatal Stroke Screening in Infants using Wearable Accelerometers”, ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (Ubicomp/IMWUT), 2019
• H. Zhang, Y. Long, Y. Guan and L. Shao “Triple Verification Network for Generalised Zero-shot Learning”, IEEE Trans. Image Processing (T-IP), 2019
• R. Li, C.-T.Li, and Y. Guan “Inference of a Compact Representation of Sensor Fingerprint for Source Camera Identification”, Pattern Recognition (PR), 2018
• Y. Zhu, Y. Long, Y. Guan, S. Newsam, and L. Shao, “Towards Universal Representation for Unseen Action Recognition”, IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2018
• Y. Guan, and T. Ploetz, “Ensemble of Deep LSTM Learners for Activity Recognition using Wearables”, ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (Ubicomp/IMWUT), 2017
• Y. Guan, C.-T.Li, and F. Roli “On Reducing the Effect of Covariate Factors in Gait Recognition: a Classifier Ensemble Method”, IEEE Trans. Pattern Analysis and Machine Intelligence(T-PAMI), 2015

A full list of my publications can be found via my google scholar citationLink opens in a new window.

Contact Details Email: yu.guan[at]warwick.ac.uk Address: Office 2.32, Department of Computer Science, University of Warwick, Coventry, UK, CV4 7AL Office Hour (24/25, term 1) 1.30pm-3.30pm, Tuesday