Dario Jozinović

1.1k total citations · 1 hit paper
16 papers, 746 citations indexed

About

Dario Jozinović is a scholar working on Artificial Intelligence, Geophysics and Signal Processing. According to data from OpenAlex, Dario Jozinović has authored 16 papers receiving a total of 746 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 15 papers in Geophysics and 2 papers in Signal Processing. Recurrent topics in Dario Jozinović's work include Seismology and Earthquake Studies (15 papers), Seismic Waves and Analysis (8 papers) and Earthquake Detection and Analysis (6 papers). Dario Jozinović is often cited by papers focused on Seismology and Earthquake Studies (15 papers), Seismic Waves and Analysis (8 papers) and Earthquake Detection and Analysis (6 papers). Dario Jozinović collaborates with scholars based in Italy, Croatia and Switzerland. Dario Jozinović's co-authors include Alberto Michelini, Anthony Lomax, Ivan Štajduhar, Carlo Giunchi, Valentino Lauciani, Joachim Saul, Hugo Soto, Frederik Tilmann, Dietrich Lange and Florian Haslinger and has published in prestigious journals such as SHILAP Revista de lepidopterología, Geophysical Journal International and Knowledge-Based Systems.

In The Last Decade

Dario Jozinović

14 papers receiving 737 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Which Picker Fits My Data? A Quantitative Evaluation of Deep Learning Based Seismic Pickers

2022 121 citations Jannes Münchmeyer, Jack Woollam et al. Journal of Geophysical Research Solid Earth profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Dario Jozinović Italy	10	586	566	84	37	33	16	746
Martijn van den Ende France	16	212 0.4×	474 0.8×	44 0.5×	78 2.1×	17 0.5×	43	608
Thibaut Perol United States	6	554 0.9×	532 0.9×	20 0.2×	54 1.5×	23 0.7×	8	786
David Bolton United States	13	321 0.5×	466 0.8×	18 0.2×	47 1.3×	10 0.3×	18	542
Hiromitsu Nakamura Japan	11	260 0.4×	314 0.6×	114 1.4×	48 1.3×	7 0.2×	33	454
Yixiao Sheng United States	10	529 0.9×	618 1.1×	26 0.3×	64 1.7×	14 0.4×	15	692
Clara E. Yoon United States	9	332 0.6×	406 0.7×	12 0.1×	21 0.6×	41 1.2×	17	478
Feng Bao China	14	137 0.2×	314 0.6×	20 0.2×	55 1.5×	27 0.8×	56	522
Xueyi Shang China	15	309 0.5×	387 0.7×	103 1.2×	141 3.8×	10 0.3×	34	653
Stephen P. Horton United States	8	224 0.4×	494 0.9×	20 0.2×	88 2.4×	20 0.6×	17	549
H. S. Kuyuk Türkiye	10	389 0.7×	385 0.7×	53 0.6×	40 1.1×	11 0.3×	24	486

Countries citing papers authored by Dario Jozinović

Since Specialization

Citations

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

Fields of papers citing papers by Dario Jozinović

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dario Jozinović

This figure shows the co-authorship network connecting the top 25 collaborators of Dario Jozinović. A scholar is included among the top collaborators of Dario Jozinović based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dario Jozinović. Dario Jozinović is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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16 of 16 papers shown

Jozinović, Dario, John Clinton, Frédérick Massin, Maren Böse, & Carlo Cauzzi. (2024). Realtime Selection of Optimal Source Parameters Using Ground Motion Envelopes. SHILAP Revista de lepidopterología. 3(1).

Lomax, Anthony, et al.. (2024). Effects on a Deep-Learning, Seismic Arrival-Time Picker of Domain-Knowledge Based Preprocessing of Input Seismograms. SHILAP Revista de lepidopterología. 3(1). 2 indexed citations

Jozinović, Dario, et al.. (2024). Modeling the Asymptotic Behavior of Higher Order Aftershocks with Deep Learning. Seismological Research Letters. 95(6). 3295–3305.

Woollam, Jack, Jannes Münchmeyer, Frederik Tilmann, et al.. (2022). SeisBench—A Toolbox for Machine Learning in Seismology. Seismological Research Letters. 93(3). 1695–1709. 96 indexed citations

Münchmeyer, Jannes, Jack Woollam, Andreas Rietbrock, et al.. (2022). Which Picker Fits My Data? A Quantitative Evaluation of Deep Learning Based Seismic Pickers. Journal of Geophysical Research Solid Earth. 127(1). 121 indexed citations breakdown →

Jozinović, Dario, et al.. (2022). The Choice of Time–Frequency Representations of Non-Stationary Signals Affects Machine Learning Model Accuracy: A Case Study on Earthquake Detection from LEN-DB Data. Mathematics. 10(6). 965–965. 7 indexed citations

Jozinović, Dario, et al.. (2022). Graph neural networks for multivariate time series regression with application to seismic data. International Journal of Data Science and Analytics. 16(3). 317–332. 40 indexed citations

Michelini, Alberto, et al.. (2021). INSTANCE – the Italian seismic dataset for machine learning. Earth system science data. 13(12). 5509–5544. 83 indexed citations

Jozinović, Dario, Anthony Lomax, Ivan Štajduhar, & Alberto Michelini. (2021). Rapid prediction of earthquake ground shaking intensity using raw waveform data and a Convolutional Neural Network. 461–464. 4 indexed citations

10.

Jozinović, Dario, et al.. (2021). Intra-domain and cross-domain transfer learning for time series data—How transferable are the features?. Knowledge-Based Systems. 239. 107976–107976. 41 indexed citations

11.

Jozinović, Dario, et al.. (2021). Intra-domain and cross-domain transfer learning for time series. 2 indexed citations

12.

Jozinović, Dario, Anthony Lomax, Ivan Štajduhar, & Alberto Michelini. (2021). Transfer learning: improving neural network based prediction of earthquake ground shaking for an area with insufficient training data. Geophysical Journal International. 229(1). 704–718. 56 indexed citations

13.

Jozinović, Dario, et al.. (2020). Local earthquakes detection: A benchmark dataset of 3-component seismograms built on a global scale. SHILAP Revista de lepidopterología. 1. 1–10. 31 indexed citations

14.

Jozinović, Dario, Anthony Lomax, Ivan Štajduhar, & Alberto Michelini. (2020). Rapid prediction of earthquake ground shaking intensity using raw waveform data and a convolutional neural network. Geophysical Journal International. 222(2). 1379–1389. 90 indexed citations

15.

Lomax, Anthony, Alberto Michelini, & Dario Jozinović. (2019). An Investigation of Rapid Earthquake Characterization Using Single‐Station Waveforms and a Convolutional Neural Network. Seismological Research Letters. 90(2A). 517–529. 111 indexed citations

16.

Michelini, Alberto, Licia Faenza, Giovanni Lanzano, et al.. (2019). The New ShakeMap in Italy: Progress and Advances in the Last 10 Yr. Seismological Research Letters. 91(1). 317–333. 62 indexed citations

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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