Mohammadreza Amirian

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

• 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.

  Mitigation of motion-induced artifacts in cone beam computed tomography using deep convolutional neural networks.

  Medical Physics.

  Available from: https://doi.org/10.1002/mp.16405

• 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

• 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

• 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

• Amirian, Mohammadreza; Schwenker, Friedhelm,

  2020.

  Radial basis function networks for convolutional neural networks to learn similarity distance metric and improve interpretability.

  IEEE Access.

  8, pp. 123087-123097.

  Available from: https://doi.org/10.1109/ACCESS.2020.3007337

• 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

• 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

• Amirian, Mohammadreza; Montoya, Javier; Gruss, Jonathan; Stebler, Yves D.; Bozkir, Ahmet Selman; Calandri, Marco; Schwenker, Friedhelm; Stadelmann, Thilo,

  2021.

  PrepNet : a convolutional auto-encoder to homogenize CT scans for cross-dataset medical image analysis [paper].

  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

• 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

• 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

• 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

• 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

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).

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