Preslav Aleksandrov

About

I'm pursuing a PhD at the University of Cambridge, exploring the potential of federated learning for various applications. In simpler terms, federated learning allows multiple devices to train a machine learning model collaboratively without sharing private data. This is a powerful approach for tasks like improving healthcare diagnostics or optimizing energy usage in smart grids, where data privacy is paramount. Previously I am a founding member of DeepNano, a nanoelectronics research team at the University of Glasgow. One of my key contributions was the creation of ML-NEGF, a novel simulation approach that merges machine learning with non-equilibrium Green's function (NEGF) simulations. ML-NEGF leverages a convolutional generative network to 'learn' the underlying physics governing nanosh