Mike Sips

1.1k citations
38 papers · 690 · h-index 15

Impact in

Papers in

Mike Sips

37 papers receiving 657 citations

Peers

Mike Sips
Comparison fields: 5 of 97
  • Computer Vision and Pattern Recognition 490
  • Signal Processing 198
  • Computer Graphics and Computer-Aided Design 48
  • Geography, Planning and Development 72
  • Ecological Modeling 38
Replace Jorge Poco with:
Jorge Poco Brazil
Mikael Jern Sweden
Johannes Kehrer Norway
Sebastian Bremm Germany
Rick Walker United Kingdom
Masahiro Takatsuka Australia
Aritra Dasgupta United States
Dong Hyun Jeong United States
Harish Doraiswamy United States
Kristin Potter United States
Mike Sips relative to Jorge Poco Brazil Jorge Poco's profile →
Citations per field
00.5×1.5×
Jorge Poco · 1×
Citations per year

Countries citing papers authored by Mike Sips

Since Specialization
Citations

This map shows the geographic impact of Mike Sips's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mike Sips with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mike Sips more than expected).

Fields of papers citing papers by Mike Sips

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mike Sips. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mike Sips. The network helps show where Mike Sips may publish in the future.

Co-authors

The 25 scholars most cited alongside Mike Sips, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Mike Sips Line = papers co-authored together Mike Sips links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 38 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2009153
2 200447
3 200442
4 200441
5 200640
6 201237
7 201636
8 200530
9 201628
10 201227
11 200624
12 201422
13 200421
14 201520
15 201314
16 200613
17
FP-Viz: Visual Frequent Pattern Mining
200511
18 200310
19 20049
20 20079

About Mike Sips

Mike Sips is a scholar working on Computer Vision and Pattern Recognition, Signal Processing, Artificial Intelligence, Geography, Planning and Development and Computer Networks and Communications, having authored 38 papers that have together received 690 indexed citations. Recurring topics across this work include Data Visualization and Analytics (26 papers), Data Management and Algorithms (11 papers), Time Series Analysis and Forecasting (6 papers), Geographic Information Systems Studies (5 papers), Data Analysis with R (4 papers), Scientific Computing and Data Management (4 papers), Species Distribution and Climate Change (4 papers) and Image Retrieval and Classification Techniques (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (490 citations), Signal Processing (198 citations), Computer Graphics and Computer-Aided Design (48 citations), Geography, Planning and Development (72 citations) and Ecological Modeling (38 citations). Mike Sips has collaborated with scholars based in Germany, United States and Switzerland. Frequent co-authors include Daniel A. Keim, Christian Panse, John Lewis, Pat Hanrahan, Boris Neubert, Jörn Schneidewind, Stephen C. North, Doris Dransch, Norbert Marwan and Hans-Peter Seidel. Their work appears in journals such as IEEE Transactions on Visualization and Computer Graphics, Information Visualization, Computer Graphics Forum, Computers & Geosciences and Cartography and Geographic Information Science.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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