Christian Simonis

Project Director (PMP) | Award-winning inventor at Bosch | recognized as a TOP 50 AI Thought Leader by Global AI Hub

About Me

I am passionate about technologies that add value to business and society.

Academic training in Automotive Engineering and Business Administration. Certified Project Management Professional (PMP) with a proven track record of leading large teams and developing, launching and managing highly scalable Digital Twin and AI solutions as well as implementing Enterprise Data Excellence.

Winner of the Bosch Inventor Award for the invention and development of a physics-informed machine learning system that performs cloud-based battery analytics.

Volunteer experience Artificial Intelligence:

Certified Project Management Professional:

Published articles, listed on Google Scholar.

The focus of the published articles is Automotive Engineering, Data and AI.

Publications

Mastering Classic Machine Learning - A comprehensive guide through CRISP-DM

https://www.linkedin.com/pulse/mastering-classic-machine-learning-comprehensive-guide-simonis/

This is an article about classic Machine Learning techniques, supported with methods, tools and visualizations.

Published in 2023. The source code can be found here.

This is an article about hybrid modeling techniques in the context of battery state estimation and prediction, using physics-informed AI.

Published in 2021.

Model-based Systems Engineering

https://www.youtube.com/watch?v=fR-aqclBcok

This is a video about Model-based Systems Engineering (MBSE).

Published in 2019. More information on the underlying case study can be found here.

On Model-based Source Coding for Dynamical Systems

https://ieeexplore.ieee.org/document/8022813

This is a paper about Markov chains, aiming to minimize the expected codeword length of transmitted information by accounting for process dynamics.

Published in 2017 3rd International Conference on Event-Based Control, Communication and Signal Processing (EBCCSP).

This is a list of published patent applications, mainly with a focus on Automotive IoT applications, Anomaly Detection, Battery Analytics, Digital Twin applications, Electric Powertrain, Physics-informed AI and Predictive Diagnostics.

More information on invention analytics can be found here.

This is a list of published repositories in Github.

The focus of the published code is mainly on time series analysis, probabilistic modeling and the CRISP-DM methodology.

This is a notebook, published on Google Colab.

With this notebook, you can run Python scripts in your browser to become familiar with CRISP-DM methodology, Data Science, and Machine Learning.