PhD student, Physics and Machine Learning
University of Cambridge
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I am a physicist specialising in machine learning for materials discovery. I work on both the experimental side (creating and testing materials in the lab) and on the statistical betterment of these materials with machine learning models. The key aim is to improve crucial performance metrics for next-generation materials. I am currently focussing on new materials for solar cells and increasing their overall efficiency through a machine-learning driven experimental search.
I am undertaking a PhD in Physics at the University of Cambridge, under the supervision of Alpha Lee and Felix Deschler.
These are some wonderful courses and books that got me started in machine learning and relevant programming:
Completed or in progress:
|Introduction to Machine Learning||Stanford||My solutions to programming assignments (use only in the most dire of cases!): here|
|deeplearning.ai specialisation||deeplearning.ai||Great course specialisation to complete after introduction to ML.|
|Python 3 programming||University of Michigan||A very beginners friendly specialisation in Python 3 starting from the basics.|
|Advanced Machine Learning specialisation||National Research University Higher School of Economics||Much higher difficulty than previous courses listed as a lot of prior knowledge is assumed.|
|Information Theory||David MacKay (Cambridge)||Fantastic textbook with lots of exercises.|
|TensorFlow Specialisation||Beginner's introduction to using TensorFlow.|
Please feel free to email me at:
“It’s quicker, easier, and involves less licking.”