Charalampos Patrikakis, Professor at the Dept. of Electrical and Electronics Engineering of University of West Attica on the Design and Implementation of Interconnected Electronic Systems and Services, with emphasis on data collection and processing. Founding member of THINGENIOUS, a spinoff company of University of West Attica specializing in AI and DL solutions.
I have worked as an associate teacher, adjunct lecturer and researcher to several Universities and Research Institutes including the University of Paisley, the National Technical University of Athens, the Agricultural University of Athens, Piraeus University of Applied Sciences and the Institute of Communications and Computer Systems. During 2006-2007, I have served as advisor to the Deputy Minister of Development in Greece, responsible for issues related to research .
Currently, I am the Director of Computer Network & Services Research laboraTory, which researches on Artificial Intelligence and Deep Learning, Cloud Computing and Networking, Web of Things and Blockchain technologies, implementing tools and solutions through mobile and wearable applications and services. I am also the Director of the MSc Program “Artificial Intelligence and Deep Learning”.
My experience in R&D includes 30 years of involvement in international research activities, during which I have participated in over 50 research projects, working mostly as technical coordinator, principal researcher or scientific responsible. I have over 250 publications in chapters of books, international journals and conferences, and 2 contributions in national legislation. I have acted as editorial/organizing committee member in international journals and conferences, as editor of the publication of special issues in international journals, and have co-edited three books.
Currently Editor in Chief of IEEE IT Professional Magazine, Senior Member of IEEE, an IEEE Computer Society Distinguished Contributor, Member of the Technical Chamber of Greece, and Counselor of the IEEE Student Branch of the University of West Attica.
My research and interests focus on making things and devices that we use smarter and more easy to be used by not-tech experts. In this course, technologies that relate to smart electronics and wearables, Internet and Web of things, Artificial Intelligence and Deep Learning are the domains in which me and the research lab (CONSERT) which I lead are active. At practical level, this is translated to using mobile and wearable applications, empowered by AI and exploiting the IoT in order to design, implement and evaluate tools and applications in different domains (i.e. Citizen safety and security, human wellbeing, animal welfare, and First Responder Tools).
DVP term expires December 2025
The programmer who didn’t know how to code.
The advent of AI is bringing changes to all domains in our lives, and when it comes to the topics of interest to an Electrical and Electronics Engineer, the changes are even greater. Combined with the advances in IoT/WoT and cloud computing and networking, the changes that AI is already bringing are enormous. Code development is one domain that even though we still have not realized how it is affected, is already witnessing these changes, equivalent (or even greater I would argue) than these introduced by Object Oriented Programming in the 80s. And it is not only the automatic (or if you prefer AI) code generation. It is the shift between the need to code in a machine centric manner to a human wise approach: directives and rules creation for smart machines. The introduction of Behavior-Driven Development tools and platforms (i.e Gherkin), or even commercial platforms linking tens of platforms in an orchestrated environment through a few clicks (i.e, IFTTT) proves that perhaps the future of programming lies more in understanding the logic and dynamics of smart autonomous devices, and reasoning on their harmonized collaboration, rather than micro-planning their operation (as traditional programming still does).
In this presentation, the future of programming will be discussed, through examples of the platforms and tools mentioned above, and with a reference to the insight of Isaac Asimov’s laws of Robotics.
Using AI for sensor based activity recognition: How to avoid common problems
The plethora of sensors available on mobile and wearable devices, combined with the use of AI, provide the ability for detection of human and animal activities and behavior, without the use of cameras (therefore without the need of violating personal privacy or risk of violating GDPR directives). Even so, personal data issues still exist, and what is more important, in order to produce applications which can accurately and successfully detect behaviors and activities, there is the need of correctly planning the combined use of sensors and having an accurate and clean dataset. Furthermore, energy management issues are also important, as constant use of sensors for monitoring activity can easily lead to battery drain, while processing of the collected data balances between the dilemma of local (privacy safe/energy consuming) processing or remote/cloud based (privacy challenging/ unclear about energy demands, based on the cost of communication). In this talk, all these issues will be presented and discussed through practical examples of real life applications created by the CONSERT lab of the University of West Attica and THINGENIOUS, a University spin-off company, both specializing on the design and implementation of such applications.
- The programmer who didn’t know how to code
- Using AI for sensor based activity recognition: How to avoid common problems