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.
Attend and Diagnose: Clinical Time Series Analysis Using Attention Models
2018264 citationsHuan Song, Deepta Rajan et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Andreas Spanias
Since
Specialization
Citations
This map shows the geographic impact of Andreas Spanias'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 Andreas Spanias with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andreas Spanias more than expected).
This network shows the impact of papers produced by Andreas Spanias. 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 Andreas Spanias. The network helps show where Andreas Spanias may publish in the future.
Co-authorship network of co-authors of Andreas Spanias
This figure shows the co-authorship network connecting the top 25 collaborators of Andreas Spanias.
A scholar is included among the top collaborators of Andreas Spanias 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 Andreas Spanias. Andreas Spanias is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ranganath, Suhas, Jayaraman J. Thiagarajan, Deepta Rajan, et al.. (2019). Interactive Signal Processing Education Applications for the Android Platform. 10(2).1 indexed citations
Shanthamallu, Uday Shankar, Raja Ayyanar, Cihan Tepedelenlioğlu, et al.. (2019). Online modules to introduce students to solar array control using neural nets.
Shanthamallu, Uday Shankar, Andreas Spanias, Mahesh K. Banavar, et al.. (2018). Multidisciplinary modules on sensors and machine learning.4 indexed citations
13.
Berisha, Visar, et al.. (2017). A data-driven basis for direct estimation of functionals of distributions.. arXiv (Cornell University).1 indexed citations
Mehta, Shalin, Andreas Spanias, Mahesh K. Banavar, et al.. (2014). AN INTEGRATED GRAPHICAL ENVIRONMENT FOR WEB-BASED LEARNING. 5(1). 40–53.1 indexed citations
16.
Spanias, Andreas, et al.. (2013). Techniques for soundscape retrieval and synthesis. PhDT.1 indexed citations
17.
Banavar, Mahesh K., X. Zhang, A. Manikas, et al.. (2012). Sequential wireless sensor network discovery using wide aperture array signal processing. European Signal Processing Conference. 2278–2282.7 indexed citations
18.
Spanias, Andreas, et al.. (2009). Classification of ion-channel signals using neural networks. International Conference on Signal Processing. 19–22.6 indexed citations
19.
Spanias, Andreas, et al.. (2008). Transform-domain features for ion-channel sensors. International Conference on Signal Processing. 272–275.7 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.