Angelos Chatzimparmpas

Degree:

• Diploma (BSc + MSc) in Computer Science (Informatics & Telecommunications Engineering), from the University of Western Macedonia, GR

Short Bio:

Angelos Chatzimparmpas started as an undergraduate student at the University of Western Macedonia, Greece, in 2012. After five years of studies, he received a Master's degree in Informatics and Telecommunications Engineering in Nov 2017. During the last two years, Angelos finished two internships: in a private sector company (Web Expert) as a computer programmer and at the CTTC research center in Spain as a web developer of the IoT World project. Furthermore, he was a member of his university's Robotics Team and of a scientific group for an interregional cooperation project for improving SME competitiveness policies. In addition, he was a teaching assistant for many courses. Angelos started his PhD in February 2018 at Linnaeus University and his main goal is to research ways of how visualization could assist in explainable machine learning. Overall, he is very much interested in Visual Analytics and on how to find solutions for scientific challenges referring to the problem of Big Data analytics.

Teaching

Teaching Assistant for the following courses:

• 1DV516 - Algorithms and Advanced Data Structures (3 years)
• 4ME501/4ME502 - Programming for Digital Humanities (3 years)
• 4DV800/4DV805 - Information Visualization (3 years)
• 4DV801/4DV806 - Applied Information Visualization (3 years)
• 1IK173 - Quality Assurance in Information Systems (2 years)
• 1DV437 - Introduction to Game Programming (2 years)

Guest Lecturer for the following course:

• 1ME321 - Web Technology 1

Research

Angelos focus on Visual Analytics (VA) approaches that can be applied for making smart/complex systems more effective, for example. This topic is part of the VAESS research group within DISA that aims to better understand and engineer complex cyber-physical systems. He develop foundational visual analytics principles, techniques, and tools for analyzing data and models in those systems. Thus, trust in the analysis results is finally increased by making the analysis methods and the resulting empirical models transparent for end-users. In consequence, users are enabled to better predict the behavior of cyber-physical systems and to reconstruct the underlying models more efficiently when needed.

Commissions

Substantially contribute to the research field of Visual Analytics and to make an impact by improving complex systems used in practice.