UCL Department of Electronic and Electrical Engineering


Lecture on Multi-device Intelligence

15 March 2024, 10:00 am–11:00 am

Event flyer for lecture on multi-device intelligence with a data-centric approach to federated learning

Join us for a discussion on multi-device intelligence with a data-centric approach to federated learning.

This event is free.

Event Information

Open to







Anna Maria Mandalari – Electronic and Electrical Engineering


IALS Lecture Theatre
Charles Clore House
United Kingdom

In today's multi-device environment, enhancing human experience requires learning from diverse modalities and circumstances. However, challenges arise due to limited data availability and edge device computational constraints for training deep neural networks.

This event delves into efficient and effective neural network training using data from personal devices. Explore topics like label efficiency, data selection, model partitioning, handling missing modalities, and more.

Don't miss this opportunity to expand your knowledge in this area and be part of the conversation.

About the Speaker

Mohammad Malekzadeh

Senior Research Scientist, Device Research department at Nokia Bell Labs

Dr Mohammad Malekzadeh is a Senior Research Scientist in the Device Research Department at Nokia Bell Labs in Cambridge, UK. He leads the Device Intelligence team, focusing on Machine Learning solutions for personal devices, with an emphasis on efficiency, collaboration, adaptability, and privacy. Before joining Bell Labs, Mohammad worked as a Postdoctoral Research Associate at Imperial College London's Information Processing and Communications Lab, closely collaborating with Prof. Deniz Gunduz on Privacy-Preserving and Trustworthy Machine Learning. Mohammad pursued his PhD in Computer Science at Queen Mary University of London as a member of the Centre for Intelligent Sensing, concurrently holding a Research Assistant position at Imperial College London contributing to the Databox Project in the Systems and Algorithms Laboratory. In his PhD, Mohammad collaborated with esteemed advisors such as Prof. Hamed Haddadi, Dr Richard G. Clegg, and Professor Andrea Cavallaro to develop Machine Learning Algorithms for Privacy-Preserving Personal Data Analytics, particularly for data captured by mobile and wearable devices. During his PhD journey, Mohammad interned at Brave Software Research, where he explored privacy-preserving techniques to improve content personalization in web browsers. Before his PhD, Mohammad earned an MSc degree in Computer Engineering from Sharif University of Technology in Iran.

More about Mohammad Malekzadeh