Dr. Ates has returned from his short term mobility at the Learning Systems Group of Prof.  Jesus Cerquides. The research of this group focuses on the development of machine learning algorithms, models, software, and applications. From an algorithmic/modeling perspective, the group pursues research in Probabilistic Graphical Models,  Case-Base Reasoning, Transfer Learning, and Deep Learning.  It is part of the Artificial Intelligence Research Institute in Barcelona and stationed at the Autonomous University of Barcelona (https://www.iiia.csic.es/en-us/).

Dr. Ates also gave a lecture during his visit about "Paradigm Shift in the Engineering Design Cycle":

Abstract of the talk:

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.