Dr Michael Shekelyan

Lecturer in Computer Science
School of Electronic Engineering and Computer Science
Queen Mary University of London
Queen Mary University of London
Research
Federated Learning, Differential Privacy, Random Sampling, Data Summaries, Query Processing
Interests
My research focuses primarily on algorithms, data structures and summaries to manage very large or sensitive data. The overall goal is to build a full data pipeline that feeds end users with easily interpretable facts which provide novel insights and aid decision making processes. Reducing the data complexity either through sampling or summarisation plays a crucial role to support exploratory interactions with the data that involve a lot of probing, while still providing an intuitive approximation model of the data. Sensitive data motivated privacy-preserving techniques such as differential privacy & federated learning to facilitate data sharing between organisations whilst reducing risks to the privacy of patients, users, customers and employees whose personal information is collected. Even though the big picture in the data is barely affected by a single individual, how to keep privacy leakage at a negligible level when revealing the big picture remains a challenging problem that requires further research.Publications
2023

Pissis SP, Shekelyan M, Liu C and Loukides G
345-352.
20-09-2023
2022

Shekelyan M, Cormode G, Ma Q, Shanghooshabad AM and Triantafillou P
26th International Conference on Extending Database Technology (EDBT 2023).
02-11-2022