Tomer Lancewicki

420 citations
7 papers · 218 · 1 hit paper · h-index 4

Impact in

Papers in

    • Neural Networks and Applications 2
    • Anomaly Detection Techniques and Applications 2
    • Stochastic Gradient Optimization Techniques 1
    • Advanced Clustering Algorithms Research 1
    • Topic Modeling 1
    • Time Series Analysis and Forecasting 2

Tomer Lancewicki

6 papers receiving 212 citations

Tomer Lancewicki's Hit Papers

Practical Approach to Asynchronous Multivariate Time Series Anomaly Detection and Localization 2021 · 176 citations
1760+1+3Years since publication50100150

Peers

Tomer Lancewicki
Comparison fields: 5 of 40
  • Signal Processing 135
  • Artificial Intelligence 181
  • Computer Networks and Communications 108
  • Control and Systems Engineering 25
  • Statistics and Probability 8
Replace Kaustav Das with:
Kaustav Das United States
Panpan Zheng China
Xingkong Ma China
G. Androulidakis Greece
Ludovic Mé France
Naveen K. D. Venkategowda Norway
Hemant Sengar United States
Brett Wilson United States
Chuanpu Fu China
Tomer Lancewicki relative to Kaustav Das United States Kaustav Das's profile →
Citations per field
00.5×10×15×19.3×
Kaustav Das · 1×
Citations per year

Countries citing papers authored by Tomer Lancewicki

Since Specialization
Citations

This map shows the geographic impact of Tomer Lancewicki'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 Tomer Lancewicki with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tomer Lancewicki more than expected).

Fields of papers citing papers by Tomer Lancewicki

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tomer Lancewicki. 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 Tomer Lancewicki. The network helps show where Tomer Lancewicki may publish in the future.

Co-authors

The 6 scholars most cited alongside Tomer Lancewicki, 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 Tomer Lancewicki Line = papers co-authored together Tomer Lancewicki links everyone, so they are left out of the graph.

All Works

About Tomer Lancewicki

Tomer Lancewicki is a scholar working on Artificial Intelligence, Signal Processing, Computational Mechanics, Computer Networks and Communications and Molecular Biology, having authored 7 papers that have together received 218 indexed citations. Recurring topics across this work include Neural Networks and Applications (2 papers), Anomaly Detection Techniques and Applications (2 papers), Sparse and Compressive Sensing Techniques (2 papers), Time Series Analysis and Forecasting (2 papers), Stochastic Gradient Optimization Techniques (1 paper), Advanced Clustering Algorithms Research (1 paper), Gene expression and cancer classification (1 paper) and Topic Modeling (1 paper). The work is most often cited by research in Signal Processing (135 citations), Artificial Intelligence (181 citations), Computer Networks and Communications (108 citations), Control and Systems Engineering (25 citations) and Statistics and Probability (8 citations). Tomer Lancewicki has collaborated with scholars based in United States, Israel and Ireland. Frequent co-authors include Ahmed Abdulaal, Zhuang‐Hua Liu, Mayer Aladjem, Iñigo Urteaga, Itamar Arel and Shahram Khadivi. Their work appears in journals such as Neurocomputing and IEEE Transactions on Signal Processing.

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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