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Selection of Projects

Image by Christos Andriopoulos

My work

Over time my expertise evolved, and currently covers the following aspects:

  • Business Case Evaluation

  • Data: Acquisition, Preparation, Interpretation, Visualization

  • Machine Learning: Feature Engineering, Modeling, Training, Evaluation, Serving, Model updating, Monitoring

I work both as a coordinator of a team or create the solution alone depending on the project. 

Here are a few samples from my latest work, especially around machine learning. 

Smart appointments
Prompt engineering, Fullstack)

Fullstack app integrating ChatGPT to WhatsApp and providing an AI-based smart scheduling bot. Programming instant messaging using Communication Platform as a service (CPaaS)


  • OpenAI ChatGPT API 

  • Twilio API

  • Nylas Booking

  • PostgreSQL



Microchip Detection

In cooperation with bee produced and the event series "Industry meets makers" I created a case study on how to improve the life cycle of electronic goods. After testing both public and private Large Language Models (LLMs), an individual solution was created making use of:

  • web scraping

  • natural language processing 

  • setting up a model based on Bidirectional Encoder Representations from Transformers (BERT)

  • identifying microchips using Named Entity Recognition (NER)

Energy consumption 
(Tabular data,
/Anomaly detection)

This project was done in the frame of my Master Thesis "Domain Analysis of machine learning operations: A model architecture". 
An emphasis was put on the machine learning operations of an end-to-end outlier detection application. A classification model from energy data with a focus on renewable energy consumption was developed and made available as a web app. Throughout the development, best practices from the latest research were adopted. 

Implementation details as open source available.

Screenshot 2023-07-17 at 15.37.59.png

person localization in wilderness search and rescue
(Image data,
Image Analysis/Autoencoder-deep learning)

This is part of the exercise class "UE Computer Vision, Oliver Bimber / Indrajit Kurmi" at the JKU Austria.

The institute for computer vision has a specific research project for Search and rescue with airborne optical sectioning.

In this lab project, I implemented an unsupervised person localization algorithm.

Challenges within the implementation:

  • Data preparation of colored images from drones, 

  • Color channel extraction and modeling of outliers

  • Autoencoder approach of modeling outliers

Implementation details as open source available.

Explainability of BERT as Visual Analytics
Transformer-deep learning)

Screenshot 2023-07-17 at 16.01.09.png

This was an explainable AI  project calculating Shapley Values using the SHAP library. Shapley values in explainable AI allocate credit to each feature in a machine learning model, revealing their individual contributions to predictions, enhancing transparency, and enabling a deeper understanding of how the model makes decisions. It was used on the BERT Model, which is a language model pre-trained on a large corpus of English language data.

Classification of Explainable Artificial Intelligence according to Hohman et al.

Implementation details as open source available.


Protein Folding with alphafold
(Protein Data,
Attention network - deep learning)

This is part of the exercise class "KV Structural Bioinformatics, Alois Regl / Sepp Hochreiter" at the Johannes Kepler University in Linz, Austria.


In this project I focused on globin evolution, comparing sequences of myoglobin of various animals.

  • Visualizing the protein structures with Molviz.

  • Folding proteins with AlphaFold2 and Alphafold2-multimer.

  • Changing the Structure/Model/Chain/Residue/Atom (SMCRA) structure and predicting the protein again.

  • Using the  Local Distance Difference Test (IDDT) for evaluation

Challenges within the implementation:

  • Protein visualization

  • Protein structure prediction

  • Manipulating Aminoacids

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