thinking about distributed, privacy-preserving and collaborative ml 🤔


budding machine learning researcher; member of the cohere for ai community.

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.

year title authors tags paper code misc
2023 Rumour Detection in the Wild: A Browser Extension for Twitter jovanović & ross 💻🗣️🌀 NLP-OSS @ EMNLP2023 ACL
2023 EDAC: Efficient Deployment of Audio Classification Models For COVID-19 Detection jovanović*, mihaly* & donaldson* 🌳🌀 arxiv preprint

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