Boaz Lerner

1.8k citations
67 papers · 1.2k indexed · h-index 21

Impact in

    • Human Mobility and Location-Based Analysis
    • Urban Transport and Accessibility
    • Neural Networks and Applications
    • Bayesian Modeling and Causal Inference

Papers in

Boaz Lerner

58 papers receiving 1.1k citations

Peers

Boaz Lerner
Comparison fields: 5 of 136
  • Transportation 119
  • Artificial Intelligence 400
  • Computer Vision and Pattern Recognition 176
  • Biophysics 49
  • Signal Processing 74
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Citations per field
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Citations per year

Countries citing papers authored by Boaz Lerner

Since Specialization
Citations

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

Fields of papers citing papers by Boaz Lerner

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Boaz Lerner, 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 Boaz Lerner Line = papers co-authored together Boaz Lerner links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20245
2 20231
3 20230
4 20223
5 202213
6 201926
7 20193
8
Temporal modeling of ALS using longitudinal data and long-short term memory-based algorithm.
20182
9
Learning human behaviors and lifestyle by capturing temporal relations in mobility patterns.
20173
10
Learning latent variable models by pairwise cluster comparison Part II: algorithm and evaluation
20161
11
Learning latent variable models by pairwise cluster comparison Part I: theory and overview
20161
12
Adaptive thresholding in structure learning of a Bayesian network
20134
13
Learning Latent Variable Models by Pairwise Cluster Comparison
20122
14 20118
15 200716
16 200710
17 200763
18
Recursive Autonomy Identification for Bayesian Network Structure Learning.
20051
19 20017
20 199896

About Boaz Lerner

Boaz Lerner is a scholar working on Artificial Intelligence, Biophysics, Medical Laboratory Technology, Transportation and Signal Processing, having authored 67 papers that have together received 1.2k indexed citations. Recurring topics across this work include Gene expression and cancer classification (19 papers), Bayesian Modeling and Causal Inference (13 papers), Neural Networks and Applications (10 papers), Machine Learning and Data Classification (7 papers), Machine Learning in Bioinformatics (6 papers), Parkinson's Disease Mechanisms and Treatments (6 papers), Data Quality and Management (5 papers) and Blind Source Separation Techniques (4 papers). The work is most often cited by research in Transportation (119 citations), Artificial Intelligence (400 citations), Computer Vision and Pattern Recognition (176 citations), Biophysics (49 citations) and Signal Processing (74 citations). Boaz Lerner has collaborated with scholars based in Israel, United Kingdom and United States. Frequent co-authors include Hugo Guterman, I. Dinstein, Yitzhak Romem, Mayer Aladjem, Irad Ben‐Gal, Eran Toch, Yair Meidan, Gad Rabinowitz, Maj Hultén and William F. Clocksin. Their work appears in journals such as Pattern Recognition Letters, Journal of Crohn s and Colitis, Artificial Intelligence in Medicine, npj Parkinson s Disease and Pattern Recognition.

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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2026