Payam Barnaghi is Chair in Machine Intelligence Applied to Medicine in the Department of Brain Sciences at Imperial College London. He is a Principal Investigator and Programme Lead for Machine Intelligence in the Care Research and Technology Centre at the UK Dementia Research Institute. He is an associate editor of the IEEE Transactions on Big Data and vice-chair of the IEEE SIG on Big Data Intelligent Networking. He is also a Visiting Professor at the Great Ormond Street Institute of Child Health at University College London.
His main research goal is to develop AI and machine learning solutions for healthcare and create affordable and scalable digital systems that can be applied across a range of health conditions. He works on machine learning, Internet of Things (IoT), semantic computing, adaptive algorithms and computational neuroscience to solve problems and develop new technologies for future healthcare systems.
Personal Homepage: https://pbarnaghi.github.io
UK DRI Homepage: https://ukdri.ac.uk/team/payam-barnaghi
- Principal Investigator, PROTECT: Predictive approaches in managing long-term conditions: from remote monitoring data to digital biomarkers, The Engineering and Physical Sciences Research Council (EPSRC)/NIHR (Sep. 2022 - Aug. 2025).
- Co-Principal Investigator and Programme Lead, Care Research and Technology Centre, The UK Dementia Research Institute (UK DRI), funded by: MRC, Alzheimer's Society, Alzheimer's Research UK, Programme Lead for: Translational Machine Intelligence (Apr. 2019 - Mar. 2023).
- Principal Investigator, Infrastructure for a Trusted Research Environment to Support Cost-effective and Flexible Processing of Healthcare Data, World-Class Labs capital equipment award, MRC, (2021-2022).
- Principal Investigator, Automated Body Monitoring System, Industry funding (Minebea Mitsumi-Intec) (Oct. 2018 - Sept. 2020).
- Outstanding Service Award, The Web Intelligence Consortium (WIC), October 2019
- IEEE Outstanding Leadership Award 2017
- The Most Outstanding Innovation, Guildford's Innovation Awards (TIHM for Dementia Project)
- HSJ 2018 Award for Improving Care with Technology (TIHM for Dementia Project)
- Regional NHS Parliamentary Award, NHS 70th Anniversary, 2018 (TIHM for Dementia Project)
- Best Mental Health Initiative Award, EHI 2017 Awards (TIHM for Dementia Project)
et al., 2022, Emotive response to a hybrid-face robot and translation to consumer social robots, Ieee Internet of Things Journal, Vol:9, ISSN:2327-4662, Pages:3174-3188
et al., 2021, Conversational affective social robots for ageing and dementia support, Ieee Transactions on Cognitive and Developmental Systems, ISSN:2379-8920
Rezvani R, Barnaghi P, Enshaeifar S, 2021, A New Pattern Representation Method for Time-Series Data, IEEE Transactions on Knowledge and Data Engineering, Vol:33, ISSN:1041-4347, Pages:2818-2832
et al., 2020, Continual learning using Bayesian neural networks, Ieee Transactions on Neural Networks and Learning Systems, Vol:32, ISSN:2162-2388, Pages:4243-4252
Fathy Y, Barnaghi P, 2019, Quality-Based and Energy-Efficient Data Communication for the Internet of Things Networks, Ieee Internet of Things Journal, Vol:6, ISSN:2327-4662, Pages:10318-10331
Fathy Y, Barnaghi P, Tafazolli R, 2019, An Online Adaptive Algorithm for Change Detection in Streaming Sensory Data, IEEE Systems Journal, Vol:13, ISSN:1932-8184, Pages:2688-2699
et al., 2019, Observing the Pulse of a City: A Smart City Framework for Real-Time Discovery, Federation, and Aggregation of Data Streams, Ieee Internet of Things Journal, Vol:6, ISSN:2327-4662, Pages:2651-2668
et al., 2019, Network analysis of the multidimensional symptom experience of oncology, Scientific Reports, Vol:9, ISSN:2045-2322, Pages:1-11
et al., 2019, Machine learning methods for detecting urinary tract infection and analysing daily living activities in people with dementia, PLOS One, Vol:14, ISSN:1932-6203
et al., 2019, Best practices for publishing, retrieving, and using spatial data on the web, Semantic Web, Vol:10, ISSN:1570-0844, Pages:95-114
et al., 2018, Learning from data to predict future symptoms of oncology patients, PLOS One, Vol:13, ISSN:1932-6203, Pages:1-17
Gonzalez-Vidal A, Barnaghi P, Skarmeta AF, 2018, BEATS: Blocks of Eigenvalues Algorithm for Time Series Segmentation, IEEE Transactions on Knowledge and Data Engineering, Vol:30, ISSN:1041-4347, Pages:2051-2064
Puschmann D, Barnaghi P, Tafazolli R, 2018, Using LDA to Uncover the Underlying Structures and Relations in Smart City Data Streams, IEEE Systems Journal, Vol:12, ISSN:1932-8184, Pages:1755-1766
Fathy Y, Barnaghi P, Tafazolli R, 2018, Large-Scale Indexing, Discovery, and Ranking for the Internet of Things (IoT), ACM Computing Surveys, Vol:51, ISSN:0360-0300
et al., 2018, A Novel Indexing Method for Scalable IoT Source Lookup, Ieee Internet of Things Journal, Vol:5, ISSN:2327-4662, Pages:2037-2054
et al., 2018, Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques, PLOS One, Vol:13, ISSN:1932-6203, Pages:1-20
et al., 2018, Congruence Between Latent Class and K-Modes Analyses in the Identification of Oncology Patients With Distinct Symptom Experiences, Journal of Pain and Symptom Management, Vol:55, ISSN:0885-3924, Pages:318-+
et al., 2018, The Internet of Things for Dementia Care, IEEE Internet Computing, Vol:22, ISSN:1089-7801, Pages:8-17
Puschmann D, Barnaghi P, Tafazolli R, 2017, Adaptive Clustering for Dynamic IoT Data Streams, Ieee Internet of Things Journal, Vol:4, ISSN:2327-4662, Pages:64-74
et al., 2020, A Digital Platform for Remote Healthcare Monitoring, 29th World Wide Web Conference (WWW), ASSOC COMPUTING MACHINERY, Pages:203-206
et al., 2020, Privacy-preserving Activity and Health Monitoring on Databox, 3rd ACM International Workshop on Edge Systems, Analytics and Networking (EdgeSys), ASSOC COMPUTING MACHINERY, Pages:49-54
et al., 2019, Transforming care for people with dementia using the Internet of Things, UBIQUITY PRESS LTD, ISSN:1568-4156
et al., 2019, IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams, 3rd Global IoT Summit (GIoTS), IEEE