Engineers have been dealing with massive amounts of data accumulated over decades of fundamental experiments and field measurements, vitalized in the form of cleverly organized charts, tables and heuristic laws. In the last few decades, our capability to generate data has increased even further with the developments in (i) the digital measurement techniques including sensing technologies, (ii) computational power, (iii) faster, easier and cheaper data transfer and storage and (iv) post-processing tools and algorithms. On the other side, the problems that are needed to be addressed today, such as the food-water-energy security, pandemics and diseases, or global warming, are massive and at a completely different scale. More drastically, we have comparably much less time to find sustainable solutions. Therefore, we need a paradigm shift in how to interpret the data we collect and solve our problems, which can speed up our hypothesis test cycle. In this talk, we will visit some case studies relevant to the energy problem and discuss how the expertise of AI specialists can tip the scales in our favour.  

Cihan is a junior research group leader at KIT, under the Institute of Thermal Turbo Machinery, Multiphase Flow & Combustion group. With his group, he is working on the design and optimization of energy intensive processes. He is also a PI at the Graduate School Computational and Data Science and KIT Emerging Field of Health Technologies.