Dr. Michael Shekelyan, Computer Science Researcher
Bio
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).
Research
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.
Differential Privacy
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]
referenced in:
Dissertation, University of Minnesota [2022]
Technical Report, Oregon State University [2022]
ACM EdgeSys [2022]
ACM SIGMOD [2022]
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
Error Bounds
PVLDB (2017) [conference, link, slides, pdf]
referenced in:
Dissertation, Hong Kong Polytechnic University [2019]
Dissertation, Indian Institute of Science [2019]
Dissertation, Technical University Munich [2020]
PVLDB [2018, 2019, 2020]
IEEE ICDE [2021, 2021b, 2021c]
IEEE TKDE [2019]
CIDR [2019]
Data Science and Engineering [2018]
Knowledge and Information Systems [2020, 2021]
Information Systems [2022]
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 to compute sums over sub-tables for a very large table of numbers, most of which are equal to zero :
Shekelyan, Dignoes & Gamper
Sparse prefix sums: Constant-time range sum queries over sparse multidimensional data cubes
INFORMATION SYSTEMS (2019) [journal, link, slides]
Shekelyan, Josse & Schubert
Linear path skylines in bicriteria networks
DASFAA (2014) [conference, link, project, pdf]
referenced in:
Dissertation, University of Munich [2016, 2016b]
Dissertation, University of Alberta [2017, 2020]
Dissertation, Technical University of Dortmund [2018]
IEEE ICDE [2015, 2015b]
IEEE MDM [2020, 2021]
ACM SIGSPATIAL [2017, 2017b, 2017c, 2020, 2020b]
SSTD [2015, 2015b, 2015c, 2017]
EMO [2017]
VEHITS [2016]
Journal of Internet Technology [2019]
IET Intelligent Transport Systems [2019]
Geoinformatica [2017, 2018]
Information Systems [2016]
ACM Transactions on Spatial Algorithms and Systems [2020]
Shekelyan, Josse & Schubert
Linear path skylines in bicriteria networks
SSTD (2015) [conference, link, project, pdf]
referenced in:
Dissertation, University of Munich [2016, 2016b]
Dissertation, Technical University of Dortmund [2018]
ACM SIGSPATIAL [2017c]
SSTD [2015b, 2015c, 2015d]
EMO [2017]
Geoinformatica [2017]
Shekelyan, Josse & Schubert
Linear path skylines in multicriteria networks
IEEE ICDE (2015) [conference, link, project, pdf]
referenced in:
Dissertation, University of Munich [2016]
Dissertation, University of Technology Sydney [2019]
Dissertation, New Mexico State University [2021]
Dissertation, Université de Bordeaux [2021]
IEEE ICDE [2019, 2020]
IEEE MDM [2021]
IEEE HPCC / SmartCity / DSS [2016]
IEEE LifeTech [2021]
DASFAA [2018]
ACM SIGSPATIAL [2015, 2017c, 2018]
SSTD [2015b, 2015c]
EDBT [2018]
ATMOS [2020]
Mathematical Problems in Engineering [2018]
Geoinformatica [2017]
arXiv [2020]
Websites
How do we turn computer "science" into computer science? [link]
How do we get fewer papers with more quality? [link]
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]