thinking about distributed, privacy-preserving and collaborative ml 🤔


ML Research @ ISTA;
community lead: ml-theory @ cohere for ai.

Research
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At present, my machine learning interests are centred around distributed, privacy-preserving, efficient and collaborative machine learning. In particular, I have a great affinity towards paradigms such as federated learning and neural network compression, branching off into areas such as mechanistic interpretability. Generally speaking, the motivating factor for this is that I am circumspect towards the promise of centralised machine learning paradigms, which require enormous amounts of data and computational resources to achieve their state-of-the-art performance. Instead, I believe that there is much benefit to be derived from investigating alternate paradigms that can scale while promoting privacy inherently, supported by sound mathematical foundations.

I am actively looking for PhD opportunities to start in 2025 Spring/Autumn. If you know of any vacancies, please do not hesitate to reach out.

resources + posts



education

experience

collaboration
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I have had the fortune to work with many kind and talented individuals throughout my young career. In particular, thank you to the following individuals for inspiring me (in no particular order):
alessandro palmarini
simon yu
sree harsha nelaturu