DAG Talk series: Babak Rahmani

Ever wondered what happens during a PhD? Unsure about your next career steps? Interested in academia but have many questions? 

We have created the DAG Talk series - a new “AI in Academia” talk series - to answer all of your questions and show you the world of data science in academia and research. This is the opportunity to interact first-hand with people in the field! 

In our first installment, we have invited Babak Rahmani, a PhD in electrical engineering. He presented his work on machine learning algorithms to characterize diverse physical systems!


To characterize a physical system to behave as desired, either its underlying governing rules must be known a priori or the system itself be accurately measured. The complexity of full measurements of the system scales with its size. When exposed to real-world conditions, such as perturbations or time-varying settings, the system calibrated for a fixed working condition might require non-trivial re-calibration, a process that could be prohibitively expensive, inefficient and impractical for real-world use cases.


In this talk, a learning procedure for solving highly ill-posed problems of modelling a system's forward and backward response functions are proposed. In particular, deep neural networks are used to infer the input of a system from partial measurements of its outputs or to obtain a desired target output from a physical system.