Michael Shekelyan

Dr. Michael Shekelyan is a Lecturer (Assistant Professor) in Computer Science at Queen Mary University of London (2023–present), part of the School of Electronic Engineering and Computer Science. He holds a PhD in Computer Science from the Free University of Bozen-Bolzano (2018) and a Diploma in Media Informatics from Ludwig Maximilian University of Munich (2014). Previously, he worked as a Research Associate at King's College London (2021–2023) and a Research Fellow at the University of Warwick (2018–2021). His academic journey includes roles as a Meta-Reviewer for NeurIPS and ICDT Proceedings Chair (2024), with extensive service as a reviewer for top conferences like ICML, NeurIPS, ICLR, and journals such as TKDE and IEEE T-IFS.

Research Interests: Focuses on algorithms and data structures for managing large/sensitive datasets, including differential privacy, random sampling (e.g., Hidden Shuffle Method), and multidimensional data summarization (e.g., DigitHist). His work bridges theoretical guarantees with practical applications in data management and machine learning. Notable contributions include privacy-preserving top-k selection, efficient sampling over joins, and error-bounded data summaries.

Publications: Over 15 peer-reviewed articles in top venues including SIGMOD, ICDE, EDBT, AISTATS, and PVLDB. Key works include 'Streaming Weighted Sampling over Join Queries' (EDBT'23), 'Sequential Random Sampling Revisited' (AISTATS'21), and 'Sparse Prefix Sums' (Information Systems'19, which won an ADBIS award).

Grants/Awards: Received the ADBIS'17 Award for Sparse Prefix Sums. Active in organizing conferences and mentoring early-career researchers through reviewing roles. Current projects include a PhD studentship on privacy-preserving algorithms for medical data sharing.

Labs/Teams: Leads the Privacy-Preserving Algorithms initiative at Queen Mary, collaborating with international research networks in database systems and data privacy.