Dr Michael Shekelyan

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

2022