Panagiotis Barmpoutis' was born in Thessaloniki, Greece. He received his B.Eng./M.Eng. in Electrical and Computer Engineering from the Aristotle University of Thessaloniki in 2009. He also received his MSc in Forestry Informatics and his MSc in Medical Informatics from the Aristotle University of Thessaloniki in 2012 and 2013 respectively. In 2017 he received his PhD in Computer Vision and Machine Learning applied to challenges in Forestry Informatics from the Aristotle University of Thessaloniki. Panagiotis Barmpoutis is an ML/AI Software Engineer at Norfolk & Norwich University Hospital, a Lead ML/AI Software Engineer of the Research and Development team at Pulsar Power and Visiting Researcher at Imperial College London. His current development and research interests lie in the areas of machine-deep learning, signal processing and analysis, optimization, computer vision and applications, image and video processing, 3D modelling, data analysis and visualization. He has co-authored numerous highly cited journal and conference publications and has participated in national and European funded research projects. He has written and significantly contributed to proposals of funded projects.
Pechlivanidis E, Ginoglou D, Barmpoutis P, 2022, Can intangible assets predict future performance? A deep learning approach, International Journal of Accounting and Information Management, Vol:30, ISSN:1834-7649, Pages:61-72
et al., 2021, Tertiary lymphoid structures (TLS) identification and density assessment on H&E-stained digital slides of lung cancer, PLOS One, Vol:16, ISSN:1932-6203
et al., 2020, A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing, Sensors, Vol:20
et al., 2020, Early Fire Detection Based on Aerial 360-Degree Sensors, Deep Convolution Neural Networks and Exploitation of Fire Dynamic Textures, Remote Sensing, Vol:12
et al., 2019, Multi-lead ECG signal analysis for myocardial infarction detection and localization through the mapping of Grassmannian and Euclidean features into a common Hilbert space, Biomedical Signal Processing and Control, Vol:52, ISSN:1746-8094, Pages:111-119
et al., 2018, Wood species recognition through multidimensional texture analysis, Computers and Electronics in Agriculture