Mohammadreza Amirian

Mohammadreza Amirian
ZHAW
School of Engineering
Centre for Artificial Intelligence
Technikumstrasse 71
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
- AC3T – AI powered CBCT for improved Combination Cancer Therapy / Team member / Project ongoing
- TAILOR – Trustworthy and sample efficient vision transformers / Project leader / Project completed
- 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
- DIR3CT: Deep Image Reconstruction through X-Ray Projection-based 3D Learning of Computed Tomography Volumes / Team member / Project completed
- TAILOR – Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization / Team member / Project completed
- FWA: Visual Food Waste Analysis for Sustainable Kitchens / Team member / 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
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Amirian, Mohammadreza; Montoya-Zegarra, Javier A.; Herzig, Ivo; Eggenberger Hotz, Peter; Lichtensteiger, Lukas; Morf, Marco; Züst, Alexander; Paysan, Pascal; Peterlik, Igor; Scheib, Stefan; Füchslin, Rudolf Marcel; Stadelmann, Thilo; Schilling, Frank-Peter,
2023.
Medical Physics.
Available from: https://doi.org/10.1002/mp.16405
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Wehrli, Samuel; Hertweck, Corinna; Amirian, Mohammadreza; Glüge, Stefan; Stadelmann, Thilo,
2021.
Bias, awareness, and ignorance in deep-learning-based face recognition.
AI and Ethics.
2(3), pp. 509-522.
Available from: https://doi.org/10.1007/s43681-021-00108-6
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Thiam, Patrick; Kessler, Viktor; Amirian, Mohammadreza; Bellmann, Peter; Layher, Georg; Zhang, Yan; Velana, Maria; Gruss, Sascha; Walter, Steffen; Traue, Harald C.; Schork, Daniel; Kim, Jonghwa; Andre, Elisabeth; Neumann, Heiko; Schwenker, Friedhelm,
2021.
Multi-modal pain intensity recognition based on the SenseEmotion database.
IEEE Transactions on Affective Computing.
12(3), pp. 743-760.
Available from: https://doi.org/10.1109/TAFFC.2019.2892090
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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
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Amirian, Mohammadreza; Schwenker, Friedhelm,
2020.
IEEE Access.
8, pp. 123087-123097.
Available from: https://doi.org/10.1109/ACCESS.2020.3007337
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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
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Herzig, Ivo; Paysan, Pascal; Scheib, Stefan; Züst, Alexander; Schilling, Frank-Peter; Montoya, Javier; Amirian, Mohammadreza; Stadelmann, Thilo; Eggenberger Hotz, Peter; Füchslin, Rudolf Marcel; Lichtensteiger, Lukas,
2022.
Deep learning-based simultaneous multi-phase deformable image registration of sparse 4D-CBCT [poster].
In:
AAPM Annual Meeting, Washington, DC, USA, 10-14 July 2022.
American Association of Physicists in Medicine.
pp. e325-e326.
Available from: https://doi.org/10.1002/mp.15769
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Amirian, Mohammadreza; Montoya, Javier; Gruss, Jonathan; Stebler, Yves D.; Bozkir, Ahmet Selman; Calandri, Marco; Schwenker, Friedhelm; Stadelmann, Thilo,
2021.
In:
Proceedings of CISP-BMEI’21.
14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Shanghai, China, 23-25 October 2021.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Available from: https://doi.org/10.21256/zhaw-23318
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Amirian, Mohammadreza; Tuggener, Lukas; Chavarriaga, Ricardo; Satyawan, Yvan Putra; Schilling, Frank-Peter; Schwenker, Friedhelm; Stadelmann, Thilo,
2021.
Two to trust : AutoML for safe modelling and interpretable deep learning for robustness [paper].
In:
Postproceedings 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-22061
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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
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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
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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
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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
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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
Publications before appointment at the ZHAW
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).