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Michael Shekelyan
compsci.science
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:

Sampling

How to directly jump along the selected positions of a simple random sample storing only a handful of values
compsciscience.org
Python code for sampling iterator : Shany came up with the really cool idea of posing join sampling via PGMs:

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
DigitHist summary of spatial data
(zoomed in on UK and Germany) : 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!): How to approximate arbitrary rectangles with a few pre-selected rectangles :

Query Processing

How to compute sums over sub-tables for a very large table of numbers, most of which are equal to zero : How to find all paths between two network nodes that could be best for some user preference
Optimality for some linear scalarization :

Websites



compsci.science How do we turn computer "science" into computer science? [link]

gatekeeping.science How do we fix peer-review? [link]

slow.science How do we get fewer papers with more quality? [link]

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