Mario Michael Krell

449 total citations
23 papers, 276 citations indexed

About

Mario Michael Krell is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Mario Michael Krell has authored 23 papers receiving a total of 276 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Cognitive Neuroscience, 9 papers in Artificial Intelligence and 8 papers in Signal Processing. Recurrent topics in Mario Michael Krell's work include EEG and Brain-Computer Interfaces (9 papers), Blind Source Separation Techniques (8 papers) and Neural dynamics and brain function (4 papers). Mario Michael Krell is often cited by papers focused on EEG and Brain-Computer Interfaces (9 papers), Blind Source Separation Techniques (8 papers) and Neural dynamics and brain function (4 papers). Mario Michael Krell collaborates with scholars based in Germany, United States and United Kingdom. Mario Michael Krell's co-authors include Su Kyoung Kim, Sirko Straube, Elsa Andrea Kirchner, Frank Kirchner, Jan Hendrik Metzen, Manfred Fahle, Csaba Andras Moritz, Dhruva K. Chakravorty, Zhenhua He and T.M. Cockerill and has published in prestigious journals such as PLoS ONE, IEEE Transactions on Biomedical Engineering and Pattern Recognition Letters.

In The Last Decade

Mario Michael Krell

23 papers receiving 270 citations

Peers

Mario Michael Krell
Fan He China
Rahul Upadhyay India
Andrews Samraj India
Abdullah Çavuşoğlu Türkiye
Deng Wang China
Ruimin Peng China
Elnaz Lashgari United States
Chuangquan Chen China
Mehmet Kuntalp Türkiye
Trilok Chand India
Fan He China
Mario Michael Krell
Citations per year, relative to Mario Michael Krell Mario Michael Krell (= 1×) peers Fan He

Countries citing papers authored by Mario Michael Krell

Since Specialization
Citations

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

Fields of papers citing papers by Mario Michael Krell

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mario Michael Krell. 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 Mario Michael Krell. The network helps show where Mario Michael Krell may publish in the future.

Co-authorship network of co-authors of Mario Michael Krell

This figure shows the co-authorship network connecting the top 25 collaborators of Mario Michael Krell. A scholar is included among the top collaborators of Mario Michael Krell 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 Mario Michael Krell. Mario Michael Krell is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Bilbrey, Jenna A., Mario Michael Krell, Tom Murray, et al.. (2024). Acceleration of Graph Neural Network-Based Prediction Models in Chemistry via Co-Design Optimization on Intelligence Processing Units. Journal of Chemical Information and Modeling. 64(5). 1568–1580. 2 indexed citations
2.
Krell, Mario Michael, et al.. (2022). Hardware-accelerated Simulation-based Inference of Stochastic Epidemiology Models for COVID-19. ACM Journal on Emerging Technologies in Computing Systems. 18(2). 1–24. 3 indexed citations
3.
Lawrence, Richard, Zhenhua He, Xin Yang, et al.. (2022). Benchmarking the Performance of Accelerators on National Cyberinfrastructure Resources for Artificial Intelligence / Machine Learning Workloads. Practice and Experience in Advanced Research Computing. 1–9. 13 indexed citations
4.
Krell, Mario Michael, et al.. (2020). Accelerating Simulation-based Inference with Emerging AI Hardware. 126–132. 4 indexed citations
5.
Krell, Mario Michael, et al.. (2018). Empirical Comparison of Distributed Source Localization Methods for Single-Trial Detection of Movement Preparation. Frontiers in Human Neuroscience. 12. 340–340. 6 indexed citations
6.
Krell, Mario Michael, et al.. (2017). Classifier transfer with data selection strategies for online support vector machine classification with class imbalance. Journal of Neural Engineering. 14(2). 25003–25003. 12 indexed citations
7.
Krell, Mario Michael, et al.. (2017). Learning coupled dynamic models of underwater vehicles using Support Vector Regression. OCEANS 2017 - Aberdeen. 1–7. 7 indexed citations
8.
Krell, Mario Michael, et al.. (2017). Online model identification for underwater vehicles through incremental support vector regression. 4173–4180. 7 indexed citations
9.
Krell, Mario Michael, et al.. (2017). Learning magnetic field distortion compensation for robotic systems. 9. 3516–3521. 11 indexed citations
10.
Krell, Mario Michael, et al.. (2015). Concept of a Data Thread Based Parking Space Occupancy Prediction in a Berlin Pilot Region.. National Conference on Artificial Intelligence. 15 indexed citations
11.
Krell, Mario Michael, et al.. (2015). Comparison of Data Selection Strategies for Online Support Vector Machine Classification. 59–67. 1 indexed citations
12.
Krell, Mario Michael, et al.. (2015). raxDAWN: Circumventing Overfitting of the Adaptive xDAWN. 68–75. 2 indexed citations
13.
Metzen, Jan Hendrik, et al.. (2015). Accounting for Task-Difficulty in Active Multi-Task Robot Control Learning. KI - Künstliche Intelligenz. 29(4). 369–377. 2 indexed citations
14.
Krell, Mario Michael, et al.. (2014). New one-class classifiers based on the origin separation approach. Pattern Recognition Letters. 53. 93–99. 7 indexed citations
15.
Krell, Mario Michael, et al.. (2013). pySPACE—a signal processing and classification environment in Python. Frontiers in Neuroinformatics. 7. 40–40. 29 indexed citations
16.
Krell, Mario Michael, et al.. (2013). Comparison of Sensor Selection Mechanisms for an ERP-Based Brain-Computer Interface. PLoS ONE. 8(7). e67543–e67543. 17 indexed citations
17.
Kirchner, Elsa Andrea, et al.. (2013). On the Applicability of Brain Reading for Predictive Human-Machine Interfaces in Robotics. PLoS ONE. 8(12). e81732–e81732. 26 indexed citations
18.
19.
Krell, Mario Michael, et al.. (2013). Balanced Relative Margin Machine — The missing piece between FDA and SVM classification. Pattern Recognition Letters. 41. 43–52. 7 indexed citations
20.

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