Mohammadreza Amirian

Tätigkeit an der ZHAW als

Wissenschaftlicher Assistent

Arbeits- und Forschungsschwerpunkte, Spezialkenntnisse

Maschinelles Lernen, Mustererkennung, Neuronale Netze, Affective Computing, Gesichtsausdruck, Computer Vision, Digitale Signalverarbeitung, Bio-Physiologische Signalverarbeitung, Schmerzschätzung, Sonar, Bildverarbeitung, Komprimierte Erkennung, Medizinische Bildgebung, Multi-modale DatenFusion, Recurrent Networks

Aktuelle Forschungsinteressen: Deep Learning, Bildverarbeitung

Aus- und Fortbildung

Master, Kommunikationstechnik, 2017, Universität Ulm

• FWA: Visual Food Waste Analysis for Sustainable Kitchens / Teammitglied / Projekt laufend
• Ada – Advanced Algorithms for an Artificial Data Analyst / Teammitglied / Projekt laufend
• QualitAI - Quality control of industrial products via deep learning on images / Teammitglied / Projekt laufend
• Libra: A One-Tool Solution for MLD4 Compliance / Teammitglied / Projekt abgeschlossen
• Complexity 4.0 / Teammitglied / Projekt abgeschlossen

Buchbeiträge, peer-reviewed

• 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, Hrsg.,

  Applied data science : lessons learned for the data-driven business.

  Cham:

  Springer.

  S. 313-331.

  Verfügbar unter: https://doi.org/10.1007/978-3-030-11821-1_17

Konferenzbeiträge, peer-reviewed

• 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 June 2019.

  IEEE.

  S. 31-36.

  Verfügbar unter: 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, 19-21 September 2018.

  IAPR.

  Verfügbar unter: 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, 19-21 September 2018.

  IAPR.

  Verfügbar unter: 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, 19-21 September 2018.

  IAPR.

  Verfügbar unter: https://doi.org/10.21256/zhaw-3863

Weitere Publikationen

• 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.

  Verfügbar unter: https://doi.org/10.21256/zhaw-18357

Publikationen vor Tätigkeit an der 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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