Hello there! My name is Marc Müller.
I’m chief technology officer and founder at goedle.io, a scholarship-backed startup based on machine learning which helps app developers manage retention and conversion by predicting churn and other intelligent metrics.
Specialities: Java, Data Mining, Innovation, Databases, Distributed Systems, Pattern Recognition, App Development
2015 - present
Co-Founder & CTO at goedle.io
Chief techonology officer and founder at goedle.io, a scholarship-backed startup based on machine learning which helps app developers manage retention and conversion by predicting churn and other intelligent metrics.
research assistant, Bonn-Rhein-Sieg University of Applied Sciences, project STELLA
In the context of the research project Stella, I develop a universal stochastic model to identify safety- and reliability critical stats of energy storage devices.
- Computer Science
research assistant, Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS
data-mining, mobility-mining, research and development of an embedded system tracking device, development of map matching and classification algorithms, development of Java tools and mobile applications
- Knowledge Discovery
student trainee, NetCologne
development of a java application for automatized measure analysis in realtime, development and implementation of a monitoring software for datacenters
- Site Management, Schoolsupport
Bonn-Rhein-Sieg University of Applied Sciences, Master of Science (M.Sc.), Computer Science
Cologne University of Applied Sciences, Bachelor of Science (B.Sc.), Information Engineering
Werner-von-Siemens-Schule Cologne, electronics assistant / advanced technical college entrance qualification
Coding & Programming
- Objectiv C0
- Oracle Database0
- Machine Learning0
- Data Mining0
- Stochastic Modelling0
- Pattern Recognition0
- Reliability Analysis0
- Embedded Systems0
- Signal Processing0
- Measurement Technology0
safety and reliability research through stochastic modelling
In the context of the research project Stella at the University of Applied Sciences Bonn-Rhein-Sieg, I developed a universal stochastic model to identify safety- and reliability critical states of energy storage devices. In account to a realistic quantification of these states, the model covers up the control and power electronics, the electric circuit, the communication bus, the sensors, the battery management system and the motor controller with its software. Critical states while loading the energy storage devices are detected in real-time and passed to a fault preventing system. The Objective of the project was to act before the energy storage devices react irreversibly to its environment. Furthermore the model will be integrated to a framework consisting of standards and recommendations to increase the safety. To mathematical verify the model, it was formalized and CTL model checking was applied to it.
- University of Applied Science Bonn-Rhein-Sieg
mobility research with signal processing
The objective of this project was to show that the data of tracked bluetooth devices are sufficiently dense to detect commuter patterns based on a Fourier analysis.
- Fraunhofer IAIS
image recognition system
A prototype web-service to find similar fashion based on uploaded images. The uploaded image is transformed to hue color space and afterwards features are builded with signal processing methods. Thus I was able to search a preprocessed image database to propose the user in nearly realtime (>1s) the similar fashion results, based on the features.
bluetooth tracking device
The objective of this project was to use a reliable sensor system based on a single-board computer (BeagleBoard and RaspberryPi) that is able to collect Bluetooth data autonomously and send the data to a server via UMTS. Furthermore, the system has a maintenance interface that is secured with a public key infrastructure. This was especially important when it comes to sensor placements in public spaces since the data has to be protected from illegal external access. Furthermore, different reliability and availability mechanisms are implemented in order to receive continuous sensor data. For example, a disconnection of the UMTS stick has to be intercepted, and after a reconnection the sensor has to send the incurred data to the server. The whole process runs autonomously since the sensor’s accessibility is limited once setup up.
- Fraunhofer IAIS
Fashion.Gram is an self developed app of me and colleague to shop popular fashion from Instagram. It enables an easily way to shop the hottest fashion trending on Instagram. Therefor fashion streams are crawled and the data is processed to present it to the user. In late 2014 there were about 4.000 Android and 20.000 iOS downloads, with more than 1.500 inapp views per day (source: google analytics).
Timothy Ellersiek, Gennady Andrienko, Natalia Andrienko, Dirk Hecker, Hendrik Stange and Marc Mueller. Using bluetooth to track mobility patterns: depicting its potential ba- sed on various case studies. In Proceedings of the Fifth ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness, pages 1–7. ACM, 2013.
Marc Mueller, Daniel Schulz, Michael Mock and Dirk Hecker. Detecting mobility patterns with stationary bluetooth sensors: A real-world case study. In Proceedings of 18th AGILE Conference on Geographic Information Science, 2015.
Marc Mueller. Fallstudie zur Analyse von Mobilitätsverhalten auf Basis von Bluetoothdaten. Hochschule Bonn-Rhein-Sieg
Marc Mueller. Detecting safety critical operation states of electrical energy storages by modelled stochastic processes. Bonn-Rhein-Sieg University of Applied Sciences
Get in touch
RT @CologneAIML: A huge thank you to Sören Nikolaus for his amazing talk on relational learning at #CAIML No. 19 🙏 For those who missed Sör…
@DeutschePostDHL euer ePost Service ist wirklich klasse. Könnt ihr vielleicht eine Signatur-Option (sie Mock o. Ä.… t.co/qaTv2IuJlP