Munich Datageeks

 
 
 

We are incredibly happy to announce our next Meetup on March 30th at EON.

Format:

• 2 talks (each ca. 40 min incl. discussion)
• Time for networking + food + drinks before, in between and after the presentations
• Talks are held in English
• We will be taking photos and/or film footage at the event. These will be used to share news about our meetups, and to publicize upcoming events.

The lineup:

Roland Rodde - Vegetation management for powerlines with remote sensing data

Abtract:
Growing vegetation causes risks to overhead powerlines. Therefore vegetation needs to be cut in a corridor near these powerlines. We developed a solution to support the risk assessment and the planning of future clearings based on satellite images and helicopter based Lidar data. In this talk I will give an overview on the different data sources, the models and algorithms we apply and how we implemented the solution in Azure.

Bio:
Roland is working as a data scientist at E.ON since 2018. Before that he obtained a PHD in physics from the CAU Kiel in 2009 and worked for several consulting companies. His main focus is developing data driven solutions in the energy networks area. From a data perspective he works a lot with image data and applies computer vision algorithms to extract actionable insights. He is also interested in good software development practices like unit testing, design patterns and agile development methods.

Second talk:

Florian Pfisterer - Fairness in automated decision making

Abstract:
Decisions derived from automated systems, e.g. machine learning models increasingly affect our lives. Ensuring that those systems behave fairly, and e.g. do not discriminate against majorities is an important endeavour. In the talk, I would like to give a brief intro to the field of algorithmic fairness. This includes harms that might arise from the use of biased ML models and some intuition regarding how "un-" fairness could be measured along with approaches towards how we might be able to mitigate biases in such systems.

Bio:
Florian Pfisterer recently finished his PhD in Statistics at LMU Munich. The broader goal of his research is to support domain experts in responsibly creating and deploying machine learning models. As a result, the focus of his research is on automated machine learning (AutoML), algorithmic fairness, benchmarks and developing open-source software. Florian has been a Munich Datageek since 2015.