Robert Meyer

I am a Data Scientist and Neuroscience researcher by training. I did my PhD at TU Berlin and simulated parts of the cat brain. A small Python open source package of mine to help with any numerical simulation can be found here:

I am interested in a variety of topics such as Machine Learning for NLP (, cryptocurrencies ( or combining both:

After working for the German unicorn Flixbus for 2 and half years building an automated bus ticket pricing pipeline, I co-founded alcemy, a Machine Learning startup for the cement and concrete supply chain.

The cement industry is one of the biggest CO2 emitters in the world, responsible for about 8% of worldwide emissions. Releasing carbon into the atmosphere is inherent to the process of cement production and cannot simply be remedied by switching from fossil fuels to renewable energy sources. Hence, to reduce emissions, CO2 heavy ingredients of cement - these are burnt limestone and slag - need to be substituted with low-carbon materials, such as limestone powder (which isn't burnt, and therefore doesn't release carbon into the atmosphere). Together with our partners we are currently piloting a novel concrete with a 60% reduced carbon footprint. However, adding low-carbon materials makes cement and concrete production much harder (!) as low-carbon mixtures require much more precision and oversight at every production step. Thus, at alcemy - besides cement and concrete recipe know-how - we provide cloud infrastructure, data intelligence, and predictive control software to enable the cement and concrete supply chain to produce low carbon building materials at scale.


Lessons learned from deploying Machine Learning in an old-fashioned heavy industry

Friday October 29 9:30 AM – Friday October 29 10:00 AM in Talks II