EMPOWER 2026

In the Applied Sciences group at Microsoft, I work on on-device machine learning to improve efficiency and generalization of AI models deployed at the edge.

Previously, I was a Senior Research Scientist at Nokia Bell Labs, where I led the Device Intelligence team in the Pervasive Systems department. We developed machine learning solutions for private data and personal devices, to advance personalized healthcare applications through multimodal sensing and on-device machine learning. We prioritized multi-modality, data and compute efficiency, individual privacy, and personalization to drive innovations that elevate human well-being. Explore our research papers and open-source code: CLEF, PaPaGei, CroSSL, PRIMUS, AdaBet, SoundCollage, Centaur, and Salted DNNs.

Before that, I was a Research Associate at Imperial College London, collaborating with Prof. Deniz Gunduz on Privacy-Preserving and Trustworthy Machine Learning. I earned my PhD in Computer Science at Queen Mary University of London while concurrently holding a Research Assistant position at Imperial College London. I had the opportunity to work with Prof. Hamed Haddadi, Dr. Richard G. Clegg, and Prof. Andrea Cavallaro. My PhD research focused on developing machine learning algorithms for privacy-preserving personal data analytics, particularly for data captured by mobile and wearable devices. During my PhD, I also interned at Brave Software Research, where I explored privacy-preserving techniques to enhance content personalization.