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Department of Computer Science and Technology

  • PhD Student

About me

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.


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 nanosheet transistors. It can significantly accelerate the design and optimization of nanoelectronic devices, leading to faster analyses in fields like high-performance computing, energy harvesting, and quantum technologies.

Beyond my academic pursuits, I'm also actively involved in engineering. As a research software engineer at MoFEM, I developed integrations between MoFEM modules and FreeCAD. This consists of using my machine learning and electronics knowledge to create tools to streamline the design and simulation of complex engineering systems.


You can read more about what I do at

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