Tutorial 1

Prof. Vangelis Metsis

Texas State University, USA

Title: Modern methods and tools for human biosignal analysis

Abstract: The term biosignal refers to any signal that can be measured from living organisms. Biosignals have been used in medicine, sports science, and psychology for diagnoses, and there have been impressive advancements in these areas. Recently, the fields of human-computer interaction and affective computing have found an interest in using biosignals as a means of understanding the human state and intention. This interest has been reinforced by the fact that acquiring information with sensors and interfacing electrically with the human body has become much easier in the past few years. Moving from large analog technologies to digital ones has led to the miniaturization of sensing devices. Wireless transmission technologies (e.g., Bluetooth low energy), which can be easily integrated with the acquisition hardware, have removed the need for bulky wiring. This tutorial will present an overview of modern applications of human biosignals and will provide practical examples of machine learning-based methods and tools for biosignal analysis. Traditional machine learning algorithms for feature extraction and classification will be compared with recent developments in deep learning and its applications to biosignal and time-series data processing in general. 


Dr. Metsis is an Assistant Professor at the Department of Computer Science at Texas State University. He joined the department in August 2014.

Dr. Metsis received his Bachelor of Science degree in Computer Science in 2005, from the Department of Informatics of Athens University of Economics and Business in Greece, and his Doctoral degree in 2011 from the Department of Computer Science and Engineering of The University of Texas at Arlington.

During 2006-2007, Dr. Metsis worked as a Research Associate at the Department of Informatics and Telecommunications of the National Center for Scientific Research (NCSR) “Demokritos” in Greece, contributing to the project MedIEQ, funded by the European Commission. After receiving his Ph.D. diploma, and until joining TxState, he was employed, as a Research Assistant Professor by UTA, and he continued to be affiliated with Heracleia Human-Centered Computing Laboratory, where he was involved in several federally-funded research projects, as a Co-PI or Senior Researcher. He also taught a number of graduate and undergraduate classes at the CSE department.

Dr. Metsis research interests span the areas of Machine Learning, Data Mining and Computer Vision with focus in applications of Smart Health and Wellbeing, Assisted Living and Bioinformatics.

Anomaly Detection in Images

Prof. Giacomo Boracchi

Politecnico di Milano, Italy

Anomaly detection problems are ubiquitous in engineering: the prompt detection of anomalies is often a primary concern, since these might provide precious information for understanding the dynamics of a monitored process and for activating suitable countermeasures. In fact, anomalies are typically the most informative regions in an image (e.g., defects in images used for quality control). Not surprisingly, anomaly detection problems have been widely investigated in the image processing and pattern recognition communities, and are key in application scenarios ranging from quality inspection to health monitoring. 

The tutorial presents a rigorous formulation of the anomaly-detection problem that fits with many imaging scenarios and applications. The tutorial describes, by means of illustrative examples, the most important anomaly-detection approaches in the literature, and their connection with the machine-learning perspective of semi-supervised and unsupervised learning/monitoring. Special emphasis will be given to anomaly-detection methods based on learned models, which are often adopted to handle images and signals. In particular, these will be divided into traditional models (including dictionaries yielding sparse representations) and deep learning models. The tutorial is accompanied by various examples from our research projects where we applied anomaly-detection algorithms to solve real world problems: visual quality inspection for monitoring chip and nanofiber production.


Giacomo Boracchi is an Associate Professor of Computer Engineering at Dipartimento di Elettronica, Informazione e Bioingegneria of the Politecnico di Milano, where he also received the Ph.D. in information technology (2008), after graduating in Mathematics (Università Statale di Milano, 2004).  His research interests concern image processing and machine learning, and in particular image restoration and analysis, change/anomaly detection, domain adaptation. Since 2015 he is leading industrial research projects concerning algorithms for X-ray inspection systems for airport security, automatic quality inspection systems for monitoring silicon wafer production (the system developed with STMicroelectronics is currently analyzing wafer production over different sites), and outlier detection in web-sessions.He is currently associate editor for IEEE Transactions on Image Processing and server as AE for IEEE Computational Intelligence Magazine and in a few special issues. In 2015 he received an IBM Faculty Award, in 2016 the IEEE Transactions on Neural Networks and Learning Systems Outstanding Paper Award and in 2017 the Nokia Visiting Professor Scholarship. He has held tutorials in major IEEE conferences: ICIP 2020, ICASSP 2018 and IJCNN 2017, 2018.