athanasioskoutras
about me publications teaching research projects supervision leadership talks & presentations
athanasioskoutras

Teaching Philosophy & Experience

Teaching has been one of the most rewarding aspects of my academic career. Since 2008, I’ve had the privilege of guiding students through the fascinating worlds of digital signal processing, computer engineering, and multimedia technologies. What began as a passion for solving complex technological problems has evolved into an equally strong passion for nurturing the next generation of engineers and researchers.

My Approach to Teaching

I believe that effective learning happens at the intersection of theory and practice. Engineering, by its very nature, is an applied discipline, and my teaching philosophy reflects this reality. In my courses, students don’t just learn concepts—they implement them, test them, break them, and rebuild them. This hands-on approach helps transform abstract mathematical theories into tangible skills that students can apply throughout their careers.

Technology is constantly evolving, and I strive to ensure my teaching evolves with it. I regularly update course materials to incorporate cutting-edge research and emerging technologies, particularly in rapidly advancing fields like deep learning, digital communications, computer vision and brain-computer interfaces. This approach ensures that my students graduate with not just foundational knowledge, but also an understanding of the current state of the art.

Current Courses

I currently teach the following courses:

Undergraduate Level

  • Digital Communications (ECE_K510)

    Covers the theoretical foundations of digital communication systems, including baseband and passband transmission, digital modulation schemes, synchronization, channel capacity, and coding techniques such as convolutional and block codes. (Department of Electrical & Computer Engineering, University of Peloponnese)

  • Digital Image Processing (ECE_TEL750)

    Focuses on the mathematical principles of image representation, spatial and frequency domain filtering, image restoration, morphological operations, compression algorithms, segmentation techniques, and feature extraction. (Department of Electrical & Computer Engineering, University of Peloponnese)

  • Pattern Recognition (ECE_TEL830)

    Presents statistical and machine learning approaches to pattern classification, including Bayes theory, linear and nonlinear classifiers, dimensionality reduction, feature selection, and clustering algorithms such as k-means and DBSCAN. (Department of Electrical & Computer Engineering, University of Peloponnese)

  • Sound and Music Processing (ECE_TEL860)

    Analyzes the structure and characteristics of audio and music signals, covering acoustics, auditory perception, time-frequency representations, audio descriptors, and algorithms for music information retrieval and content-based analysis. (Department of Electrical & Computer Engineering, University of Peloponnese)

Postgraduate Level

  • Biomedical Signal Processing (MSc in Biomedical Engineering)

    Advanced techniques for analyzing physiological signals, feature extraction, and pattern recognition in biomedical applications (Department of Electrical & Computer Engineering, University of Patras)

  • Medical Imaging (MSc in Biomedical Engineering)

    Principles of medical image formation, reconstruction algorithms, and computer-aided diagnosis systems (Department of Electrical & Computer Engineering, University of Patras)

  • Digital Transformation and Intelligent Tourism Systems (MSc in Tourism Business and Destination Management)

    Examines the theoretical foundations and strategic frameworks of digital transformation in tourism, focusing on intelligent systems, big data analytics, cloud platforms, IoT, and AI technologies that optimize operational efficiency, enhance service personalization, and drive innovation in tourism ecosystems. (Department of Tourism Management, University of Patras)

European Learning Exchange

  • Decoding Life Signals: Innovations in Biomedical Signal Processing

    A cross-university learning initiative connecting Bachelor’s and Master’s students from across Europe through the EUNICE alliance. This intensive, project-driven course explores biomedical signal processing (EEG, ECG, EMG), machine learning, and ethical innovation—empowering participants to decode life signals and drive healthcare technology forward (EUNICE Alliance)

Previous Teaching Experience

My teaching journey has included positions at:

  • University of Ioannina (2006-2008): Taught Digital Image, Audio, and Video Processing at the Department of Cultural Environment Management and New Technologies
  • University of Patras Medical School (2004-2015): Led courses in Neuroinformatics and Digital Signal Processing for brain studies in the “Life Sciences Informatics” MSc Programme
  • TEI of Western Greece (2008-2019): Taught a variety of courses in Informatics and Mass Media Department including: Computer Graphics, Digital Signal Processing, Advanced Topics of Computer Graphics, Web Programming, Educational Multimedia, Multimedia Production, Digital Sound Processing, Digital Image Processing.

Student Projects

One of the most fulfilling aspects of teaching is supervising student research projects. Over the years, I’ve had the honor of supervising more than 160 graduate theses on topics ranging from breast cancer detection algorithms to music emotion recognition systems. I find tremendous joy in watching students discover their own research interests and develop into independent thinkers.

Some recent student projects currently under supervision include:

  • Deep learning approaches for recognizing mental states from patient drawings
  • EEG-based anxiety prediction systems for academic environments
  • Development of audio processing tools for music genre classification
  • Computer vision applications for medical image analysis

Feel free to visit our Research Group’s Github repository to meet our work.

Looking Forward

As technologies continue to evolve, so too will my teaching. I’m particularly excited about incorporating more hands-on experience with brain-computer interfaces into my courses, and developing new material on generative AI applications in signal processing. I also hope to expand collaborative learning opportunities across disciplines, believing that the most interesting engineering challenges often exist at the boundaries between traditional fields.

If you’re a current or prospective student interested in digital signal processing, biomedical engineering, or multimedia technologies, I encourage you to reach out. I’m always eager to discuss potential projects, research opportunities, or simply share my enthusiasm for these fascinating fields.