I was born in Moscow, but I grew up in Hamburg and then later moved to Munich. I studied at the University of Munich, where I worked with Prof. Kriegel's (now Prof. Seidel's) database group. I did my PhD in Italy under the supervision of Prof. Johann Gamper (Free University of Bozen-Bolzano) and then went to the UK for postdoctoral research under Prof. Graham Cormode (University of Warwick) and Dr. Grigorios Loukides (King's College London).
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.
How to select the top items based on sensitive scores in a privacy-preserving manner:
Shany came up with the really cool idea of posing join sampling via PGMs:
Shanghooshabad, Kurmanji, Ma, Shekelyan, Almasi & Triantafillou
PGMJoins: Random Join Sampling with Graphical Models
ACM SIGMOD (2021) [conference, link]
Dissertation, University of Minnesota 
Technical Report, Oregon State University 
ACM EdgeSys 
ACM SIGMOD 
arXiv [2022a, 2022b, 2022c]
Multidimensional Data Summaries
How to build tiny data models that empirically tend to be good at approximating the number of points in a rectangular range
DigitHist summary of spatial data
(zoomed in on UK and Germany)
Shekelyan, Dignoes & Gamper
DigitHist: a Histogram-Based Data Summary with Tight
PVLDB (2017) [conference, link, slides, pdf]
Dissertation, Hong Kong Polytechnic University 
Dissertation, Indian Institute of Science 
Dissertation, Technical University Munich 
PVLDB [2018, 2019, 2020]
IEEE ICDE [2021, 2021b, 2021c]
IEEE TKDE 
Data Science and Engineering 
Knowledge and Information Systems [2020, 2021]
Information Systems 
How to build compact data models that are theoretically guaranteed to be good at approximating the number of points in a rectangular range (not just asymptotically!):
Shekelyan, Dignoes, Gamper & Garofalakis
Approximating Multidimensional Range Counts with Maximum Error Guarantees IEEE ICDE (2021) [conference, pdf]
How do we get fewer papers with more quality? [link]
London Nightvoucher Project
Currently just an idea, born out of my own experiences living in London, that I am trying to turn into a reality. I still have to learn a lot more about the intricacies involved and potential stumbling blocks ahead and expect it to take some time and require a lot of help. Do not hesitate to reach out to me, if you are in any way interested in a (initially very small scale) project that aims to make it easier to donate money (directly in person) towards providing accommodation. More details can be found on the project website [nightvoucher.org.uk].
Note: The views and opinions expressed on this site are those of the authors and do not necessarily reflect the official policy or position of their employers. [back]