Welcome, Servus, and Hello! 👋

I'm pleased you came across my GitHub profile. Have fun while browsing through my projects.

👨🏻‍💻 M.Sc. graduate in Electrical- and Computer Engineering from Technical University of Munich (TUM).
💼 I'm employed as a research assistant in the Research Group of AI & Radiomics within Department of Radiation Oncology - Klinikum rechts der Isar.
🔭 I'm currently working on the development of AI-based clinical decision support systems for soft-tissue sarcoma patients in cooperation with the chair of Computational Imaging and AI in Medicine.
📫 How to reach out: GitHub Pages, E-Mail, LinkedIn

Johannes Kiechle's Projects

anomalydetection

Anomaly Detection: Nelson Rules for Control Chart - Python implementation

classificationforadditivemanufacturing

The overall goal of this project is about developing an algorithm using deep learning, which is capable of predicting whether a 3D-model is printable or not.

consumerproducermodel

This project uses C++ multithreading (producer-consumer model) in order to draw the well known Barnsley fern.

covid-19_and_co2

This project aims for investigating the impact of COVID-19 on the climate change using different machine learning / deep learning approaches.

distributeddynamicprogrammingopenmpi

In this project, an asynchronous value iteration using OpenMPI is presented. Different strategies are implemented and benchmarked subsequently.

explaininganatomicalshapevariability

This work showcases a variational graph autoencoder disentanglement using 3D mesh data.

modern-resume-theme

A modern static resume template and theme. Powered by Jekyll and GitHub pages.

noise-reduce-fourier-transform

Noise reduction in python using spectral gating (speech, bioacoustics, time-domain signals)

python-interface-cpp

Example code for interfacing with C and C++ from Python using Cython, SWIG, CFFI, PyPy, and pybind11

unsupervisedanomalydetection

This work showcases a holistic approach how to perform anomaly detection for rail traffic data of Finland. Therein, a so-called Isolation Forest is trained in order to detect abnormal data samples.

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