I’m lume, and if there’s a problem that needs solving—be it in mathematics, computer science, or teaching someone to survive Topology—I’m the person people call. Whether it’s bridging algebraic topology with cutting-edge machine learning or guiding students through the labyrinth of advanced mathematics, I thrive where complexity meets curiosity.

My professional playgrounds have ranged from research labs and lecture halls to industries that trust neural networks more than people. I’ve published on topics like persistent homology and neural network dimensionality in venues like Springer Lecture Notes, worked on signal classification that powers real-world systems, and even taught seminars that turn abstract theories into tools people can actually use. Python, TensorFlow, CUDA—these are not just tools; they’re languages I speak fluently, alongside German, English, and Italian.

And while the academic world is my arena, teaching is where the real fun happens. Whether it’s helping a high school student nail their Abitur or lecturing on the finer points of algebraic persistence, I’ve built a career on making the impossible not just possible, but engaging. If it involves a whiteboard, a question, and a few nervous laughs from the audience, you’ll find me at home.

Outside work, I’m a functional training enthusiast and a self-taught chef, which means I approach fitness and cooking the same way I approach mathematics—with precision, experimentation, and just enough chaos to keep things interesting. Give me a challenge, and I’ll give you a solution, minus the fluff and full of flair.

1. Luciano Melodia and Richard Lenz (2022): Homological Time Series Analysis of Sensor Signals from Power Plants. Machine Learning for Irregular Time Series. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. In Michael Kamp, Irena Koprinska, Adrien Bibal et al. (ed.): Communications in Computer and Information Science. Springer Nature, Switzerland.
2. Luciano Melodia and Richard Lenz(2021): Estimate of the Neural Network Dimension Using Algebraic Topology and Lie Theory. Image Mining. Theory and Applications VII. Pattern Recognition and Information Forensics. In Alberto Del Bimbo, Rita Cucchiara, Stan Sciaroff et al. (ed.): Lecture Notes in Computer Science. Springer Nature, Switzerland
3. Luciano Melodia and Richard Lenz (2020): Persistent Homology as Stopping-Criterion for Voronoi Interpolation. Proceedings of the International Workshop on Combinatorial Image Analysis. In Tibor Lukić, Reneta Barneva, Valentin Brimkov et al. (ed.): Lecture Notes in Computer Science. Springer, Cham.
4. Luciano Melodia (2015): Zur Verwendung des Paradigmas brauchen mit und ohne zu mit Infinitiv. In Katešina Šichovà, Reinhard Krapp, Rössler Paul et al. (ed.): Standardvarietät des Deutschen – Fallbeispiele aus der sozialen Praxis, Logos, Berlin.

1. Luciano Melodia (2025): Spectral Sequences - Leray-Serre Spectral Sequence. Graduate Seminar on Spectral Theory in Mathematical Physics, Friedrich-Alexander Universität Erlangen-Nürnberg.
2. Luciano Melodia (2024): Beschränkte Fremdholmoperatoren und deren Fremdholmindex auf separablen Hilberträumen. Graduate Seminar on Spectral Flow in Functional Analysis, Friedrich-Alexander Universität Erlangen-Nürnberg.
3. Luciano Melodia (2023): Notes on Simplicial and Singular Homology. Graduate Seminar on Topics in Topology, Friedrich-Alexander Universität Erlangen-Nürnberg.
4. Luciano Melodia (2023): Natürliche Transformationen, Äquivalenzen von Kategorien, darstellbare Funktoren und das Lemma von Yoneda. Graduate Seminar on Sheaf Theory, Friedrich-Alexander Universität Erlangen-Nürnberg.

1. Luciano Melodia (2024): Algebraic and Topological Persistence. Bachelor Thesis in Mathematics supervised by Prof. Dr. Kang Li, Library of the Friedrich-Alexander Universität Erlangen-Nürnberg.
2. Luciano Melodia (2017): Deep Learning Schätzung zur absorbierten Strahlungsdosis für die nuklearmedizinische Diagnostik. Master Thesis in Information Science supervised by Prof. Dr. Elmar Lang, Prof. Dr. Bernd Ludwig, Library of the University of Regensburg.
3. Luciano Melodia (2015): Entwicklung einer Interpunktionsplattform mit linguistischen Moduln für das Information Retrieval. Bachelor Thesis in German Linguistics supervised by Prof. Dr. Paul Rössler, Library of the University of Regensburg.