Join us to hear from Dvira Segal from the University of Toronto!

Abstract: I am a theoretical chemist with an interest in method development towards studying open quantum system problems including quantum chemical dynamics, quantum thermodynamics, and quantum transport. Although I do not specialize in Machine Learning, I will demonstrate with some examples how machine learning tools help us interrogating research questions in these areas, adding new insights. As time allows, I will describe: reinforcement learning in quantum control, towards optimizing the performance of a quantum refrigerator; supervised learning of noise in quantum junctions, towards temperature-bias estimation; supervised learning of electrical conductance in DNA nanojunctions.