Imperial College London

DrHamedHaddadi

Faculty of EngineeringDyson School of Design Engineering

Reader in Human-Centred Systems
 
 
 
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Contact

 

+44 (0)20 7594 2584h.haddadi Website

 
 
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Location

 

Dyson BuildingSouth Kensington Campus

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Summary

 

Summary

I am a Reader in Human-Centred Systems and the Director of Postgraduate Studies at the Dyson School of Design Engineering, at the Faculty of Engineering at Imperial College London. I also serve as a Security Science Fellow of the Institute for Security Science and Technology. In my industrial role, I am a Visiting Professor at Brave.


My research interest are in User-Centered Systems, IoT, Applied Machine Learning, Privacy, and Human-Data Interaction. I lead the Systems and Algorithms Laboratory (SysAl). I am also an Academic Fellow of the Data Science Institute.


Contact, BioCode


Publications: https://haddadi.github.io/publications/   (Google Scholar)

Main Page & Blog: https://haddadi.github.io/ 

News

I am keen to hear from strong students interested in doing their masters project or PhD research with me, or postdoctoral fellows interested in being hosted at Imperial College London. Please get in touch! We are currently looking for exceptional candidates for the exceptional candidates for the Imperial College Research Fellowships, the Imperial President’s PhD Scholarships, China Scholarship Council, the Islamic Development Bank – Imperial College Scholarship, the London Interdisciplinary Social Science Doctoral Training Partnership (LISS DTP), and various Centres for Doctoral Training in Imperial College London.

Publications

Journals

Malekzadeh M, Clegg R, Cavallaro A, et al., 2021, DANA: Dimension-Adaptive Neural Architecture for Multivariate Sensor Data, Acm Journal on Interactive, Mobile, Wearable and Ubiquitous Technologies (imwut)

Conference

Minto L, Haller M, Haddadi H, et al., Stronger privacy for federated collaborative filtering with implicit feedback, 15th ACM Conference on Recommender Systems

Mo F, Haddadi H, Katevas K, et al., 2021, PPFL: privacy-preserving federated learning with trusted execution environments, Mobile Systems, Applications, and Services conference, ACM, Pages:94-108

More Publications