Dr. Dario Zanca
Department Artificial Intelligence in Biomedical Engineering (AIBE)
Lehrstuhl für Maschinelles Lernen und Datenanalytik
Carl-Thiersch- Straße, 2b
91052 Erlangen
- Phone number: +49 9131 85-20289
- Email: dario.zanca@fau.de
- Website: https://www.mad.tf.fau.de/person/dario-zanca/
- Since 11/2020
Postdoc at the FAU - 2019-2020
Postdoc at NeuroSense joint lab, Dept. of Medicine, Surgery and Neuroscience, University of Siena (Italy) - 2015 – 2019
Ph.D. in Smart Computing, University of Florence (Italy)
Supervisor: Prof. Dr. Marco Gori - 2018 (3 months)
Study abroad, Caltech, California Institute of Technology, Pasadena (United States) - 2013 – 2015
M.Sc. Mathematics, University of Palermo (Italy)
Supervisor: Prof. Dr. Gianluigi Oliveri - 2014 (2 months)
Study abroad, CiTIUS, University of Santiago de Compostela (Spain) - 2014 (4 months)
Study abroad as Erasmus, Institute of Mathematics at University of Szczecin (Poland) - 2007 – 2013
B.Sc. Mathematics for Scientific Communication, University of Palermo (Italy)
2021
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Ataxia with oculomotor apraxia type 2 (AOA2): an eye movement study of two siblings
In: Neurological Sciences (2021)
ISSN: 1590-1874
DOI: 10.1007/s10072-021-05206-1
BibTeX: Download - , , , , , , , :
Identifying untrustworthy predictions in neural networks by geometric gradient analysis
URL: https://arxiv.org/abs/2102.12196
BibTeX: Download
2020
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Neurophthalmologic and Orthoptic Ambulatory Assessments Reveal Ocular and Visual Changes in Patients With Early Alzheimer and Parkinson's Disease
In: Frontiers in Neurology (2020)
ISSN: 1664-2295
DOI: 10.3389/fneur.2020.577362
BibTeX: Download - , , :
Gravitational Laws of Focus of Attention
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, p. 2983-2995
DOI: 10.1109/TPAMI.2019.2920636
BibTeX: Download - , , :
Toward Improving the Evaluation of Visual Attention Models: A Crowdsourcing Approach
2020 International Joint Conference on Neural Networks, IJCNN 2020
DOI: 10.1109/IJCNN48605.2020.9207438
BibTeX: Download - , , , :
Gravitational models explain shifts on human visual attention
In: Scientific Reports 10 (2020), Article No.: 16335
ISSN: 2045-2322
DOI: 10.1038/s41598-020-73494-2
BibTeX: Download
2019
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Learning Activation Functions by Means of Kernel Based Neural Networks
Springer, 2019
ISBN: 9783030351656
DOI: 10.1007/978-3-030-35166-3_30
BibTeX: Download - , , :
A unified computational framework for visual attention dynamics
Elsevier B.V., 2019
(Progress in Brain Research, Vol.249)
ISBN: 9780444642547
DOI: 10.1016/bs.pbr.2019.01.001
BibTeX: Download
2018
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FixaTons: A collection of Human Fixations Datasets and Metrics for Scanpath Similarity
(2018)
BibTeX: Download
(Techreport)
2017
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Variational laws of visual attention for dynamic scenes
31st Annual Conference on Neural Information Processing Systems, NIPS 2017
In: 31st Annual Conference on Neural Information Processing Systems, NIPS 2017 2017
URL: https://papers.nips.cc/paper/2017/file/194cf6c2de8e00c05fcf16c498adc7bf-Paper.pdf
BibTeX: Download
- 2020
Special mention at the “Premio Marco Cadoli” 2020 for the best PhD thesis in Artificial Intelligence, AIxIA 2020 - 2018
1st Place at the SoBigData Soccer Challenge Hackathon about predicting football players’ modern roles from match events. - 2018
1st Place at the Mediaset Infinity Hackathon about predicting Customer Churn using Machine Learning