Mohammadreza Amirian

Mohammadreza Amirian
ZHAW
School of Engineering
Forschungsschwerpunkt Information Engineering
Obere Kirchgasse 2 / Steinberggasse 12/14
8400 Winterthur
Personal profile
Position at the ZHAW
Research Assistant
Expertise and research interests
Machine Learning, Pattern Recognition, Neural Networks, Affective Computing,Facial Expression Estimation, Computer Vision, Digital Signal Processing, Bio-physiological Signal Processing, Pain Estimation, Sonar, Image Processing, Compressed Sensing, Medical Imaging, Multi-modal Data Fusion, Recurrent Networks
Current research: Deep Learning and image processing
Educational background
Maseter, Comuunications Technology, 2017, Ulm University
Projects
- DIR3CT: Deep Image Reconstruction through X-Ray Projection-based 3D Learning of Computed Tomography Volumes / Team member / Project ongoing
- TAILOR - Foundations of Trustworthy AI integrating Learning, Optimisation and Reasoning / Team member / Project ongoing
- FWA: Visual Food Waste Analysis for Sustainable Kitchens / Team member / Project ongoing
- Synthetic data generation of CoVID-19 CT/X-rays images for enabling fast triage of healthy vs. unhealthy patients / Team member / Project completed
- Standardized Data and Modeling for AI-based CoVID-19 Diagnosis Support on CT Scans (SDMCT) / Project leader / Project completed
- Ada – Advanced Algorithms for an Artificial Data Analyst / Team member / Project completed
- QualitAI - Quality control of industrial products via deep learning on images / Team member / Project completed
- Libra: A One-Tool Solution for MLD4 Compliance / Team member / Project completed
- Complexity 4.0 / Team member / Project completed
Publications
-
Tuggener, Lukas ; Amirian, Mohammadreza; Benites de Azevedo e Souza, Fernando; von Däniken, Pius ; Gupta, Prakhar; Schilling, Frank-Peter ; Stadelmann, Thilo ,
2020.
Design patterns for resource-constrained automated deep-learning methods .
AI.
1(4),
pp. 510-538.
Available from : https://doi.org/10.3390/ai1040031
-
Hollenstein, Lukas ; Lichtensteiger, Lukas ; Stadelmann, Thilo ; Amirian, Mohammadreza; Budde, Lukas; Meierhofer, Jürg ; Füchslin, Rudolf Marcel ; Friedli, Thomas,
2019.
Unsupervised learning and simulation for complexity management in business operations
.
In:
Braschler, Martin; Stadelmann, Thilo; Stockinger, Kurt, eds. ,
Applied data science : lessons learned for the data-driven business.
Cham:
Springer.
pp. 313-331.
Available from : https://doi.org/10.1007/978-3-030-11821-1_17
-
Glüge, Stefan ; Amirian, Mohammadreza; Flumini, Dandolo ; Stadelmann, Thilo ,
2020.
How (not) to measure bias in face recognition networks [ paper ].
In:
Schilling, Frank-Peter; Stadelmann, Thilo, eds. ,
Artificial Neural Networks in Pattern Recognition.
9th IAPR TC 3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR'20), Winterthur, Switzerland, 2-4 September 2020.
Cham:
Springer.
Lecture Notes in Computer Science ; 12294.
Available from : https://doi.org/10.1007/978-3-030-58309-5_10
-
Amirian, Mohammadreza; Tuggener, Lukas ; Chavarriaga, Ricardo ; Satyawan, Yvan Putra; Schilling, Frank-Peter ; Schwenker, Friedhelm; Stadelmann, Thilo ,
2020.
Two to trust : AutoML for safe modelling and interpretable deep learning for robustness [ paper ].
In:
Proceedings of the 1st TAILOR Workshop on Trustworthy AI at ECAI 2020.
1st TAILOR Workshop on Trustworthy AI at ECAI 2020, Santiago de Compostela, Spain, 29-30 August 2020.
Springer.
Available from : https://doi.org/10.21256/zhaw-20217
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Tuggener, Lukas ; Amirian, Mohammadreza; Rombach, Katharina; Lörwald, Stefan; Varlet, Anastasia; Westermann, Christian; Stadelmann, Thilo ,
2019.
Automated machine learning in practice : state of the art and recent results [ paper ].
In:
2019 6th Swiss Conference on Data Science (SDS).
6th Swiss Conference on Data Science (SDS), Bern, 14. Juni 2019.
IEEE.
pp. 31-36.
Available from : https://doi.org/10.21256/zhaw-3156
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Stadelmann, Thilo ; Amirian, Mohammadreza; Arabaci, Ismail; Arnold, Marek; Duivesteijn, Gilbert François; Elezi, Ismail; Geiger, Melanie; Lörwald, Stefan; Meier, Benjamin Bruno; Rombach, Katharina; Tuggener, Lukas ,
2018.
Deep learning in the wild [ paper ].
In:
Proceedings of the 8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR).
8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR), Siena, Italy, 19-21 September 2018.
IAPR.
Available from : https://doi.org/10.21256/zhaw-3872
-
Meier, Benjamin Bruno; Elezi, Ismail; Amirian, Mohammadreza; Dürr, Oliver; Stadelmann, Thilo ,
2018.
Learning neural models for end-to-end clustering [ paper ].
In:
Proceedings of the 8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR).
8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR), Siena, Italy, 19-21 September 2018.
IAPR.
Available from : https://doi.org/10.21256/zhaw-3850
-
Amirian, Mohammadreza; Schwenker, Friedhelm; Stadelmann, Thilo ,
2018.
Trace and detect adversarial attacks on CNNs using feature response maps [ paper ].
In:
Proceedings of the 8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR).
8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR), Siena, Italy, 19-21 September 2018.
IAPR.
Available from : https://doi.org/10.21256/zhaw-3863
-
Amirian, Mohammadreza; Rombach, Katharina; Tuggener, Lukas ; Schilling, Frank-Peter ; Stadelmann, Thilo ,
2019.
Efficient deep CNNs for cross-modal automated computer vision under time and space constraints [ paper ].
In:
ECML-PKDD 2019, Würzburg, Germany, 16-19 September 2019.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Available from : https://doi.org/10.21256/zhaw-18357
Amirian, Mohammadreza; Kächele, Markus; Palm, Günther; Schwenker, Friedhelm. Support Vector Regression of Sparse Dictionary-based Features for View-Independent Action Unit Intensity Estimation. Facial Expression Recognition Challenge (FERA 2017).
Amirian, Mohammadreza; Kächele, Markus; Thiam, Patrik; Kessler, Viktor; Schwenker, Friedhelm. Continuous Multimodal Human Affect Estimation Using Echo State Networks. Proceedings of the 6th International Workshop on Audio/Visual Emotion Challenge (ACM 2016).
Amirian, Mohammadreza; Kächele, Markus; Schwenker, Friedhelm. Using Radial Basis Function Neural Networks for Continuous and Discrete Pain Estimation from Bio-physiological Signals. IAPR Workshop on Artificial Neural Networks in Pattern Recognition (2016).
Kächele, Markus; Thiam, Patrik; Amirian, Mohammadreza, Schwenker, Friedhelm; Palm, Günther. Methods for Person-Centered Continuous Pain Intensity Assessment from Bio-Physiological Channels. IEEE Journal of Selected Topics in Signal Processing (2016).
Kächele, Markus; Mohammadreza, Amirian; Thiam, Patrick; Werner, Philipp; Walter, Steffen; Palm, Günther; Schwenker, Friedhelm. "Adaptive Confidence Learning for the Personalization of Pain Intensity Estimation Systems." Evolving Systems.
Kazemi, Kamran; Amirian, Mohammadreza; Dehghani, Mohammad Javad. The S-transform Using a New Window to Improve Frequency and Time Resolutions. Signal, Image and Video Processing (2014).