Dr. Martin Schüle

Work at ZHAW

Position

Head Research AI & Computational Environment

Teaching

• Neural Networks and Deep Learning
• Advanced Deep Learning

Professional development teaching

• CAS in Digital Life Sciences
• Natural Language Processing Fundamentals

Projects

• Maximizing the Benefits of Organic Fertilizers: A Data-Driven Approach to Improve Efficiency and Reduce Pollution / Project leader / abgeschlossen
• Investor and Stakeholder Tools for Tracking Companies’ Climate Commitments, Greenwashing and ESG Trends / Team member / abgeschlossen
• Employing Natural Language Processing to identify inconsistencies in companies’ non-financial communication / Team member / abgeschlossen
• An integrated modelling and learning framework for real-time online decision assistance in Swiss agriculture / Project leader / abgeschlossen
• A cloud-based IoT approach for food safety and quality prediction / Team member / abgeschlossen
• Predicting investor behaviour in European bond markets through machine learning / Team member / abgeschlossen
• Next Generation: Neural Recommendation System / Team member / abgeschlossen
• European government bond dynamics and stability policies: taming contagion risks / Team member / abgeschlossen
• Efficient Urban Pluvial Flood Simulation / Project leader / abgeschlossen
• Comprehensive Sales Forecasting for Supply Chain Optimization in Food Industry / Team member / abgeschlossen
• Multi-Asset Investment Process using Bayes Ensembles of Trading Models / Team member / abgeschlossen

Publications

• Schwendner, Peter; Schüle, Martin; Hillebrand, Martin,

  2019.

  Sentiment analysis of European bonds 2016 - 2018.

  Frontiers in Artificial Intelligence.

  2(20).

  Available from: https://doi.org/10.3389/frai.2019.00020

• Schwendner, Peter; Schüle, Martin; Ott, Thomas; Hillebrand, Martin,

  2015.

  European government bond dynamics and stability policies : taming contagion risks.

  Journal of Network Theory in Finance.

  1(4), pp. 1-25.

  Available from: https://doi.org/10.21314/JNTF.2015.012

• Uwate, Yoko; Schüle, Martin; Ott, Thomas; Noshio, Yoshifumi,

  2020.

  Echo state network with chaos noise for time series prediction [paper].

  In:

  Proceedings of the 2020 International Symposium on Nonlinear Theory and its Applications.

  International Symposium on Nonlinear Theory and its Applications (NOLTA), Okinawa, Japan, 16–19 November 2020.

  pp. 274.

• Schüle, Martin,

  2020.

  The collaborative learning cellular automata density classification problem [paper].

  In:

  Proceedings of the 2020 International Symposium on Nonlinear Theory and its Applications.

  International Symposium on Nonlinear Theory and its Applications (NOLTA), Okinawa, Japan, 16–19 November 2020.

  pp. 268.

• Gygax, Gregory; Schüle, Martin,

  2020.

  A hybrid deep learning approach for forecasting air temperature [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.

  pp. 235-246.

  Lecture Notes in Computer Science ; 12294.

  Available from: https://doi.org/10.1007/978-3-030-58309-5_19

• Kaufmann, Moritz; Schüle, Martin; Smits, Theo; Pothier, Joël,

  2020.

  Typing plasmids with distributed sequence representation [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.

  pp. 200-210.

  Lecture Notes in Computer Science ; 12294.

  Available from: https://doi.org/10.1007/978-3-030-58309-5_16

• Ott, Thomas; Schüle, Martin; Fellermann, Harold; Uwate, Yoko,

  2018.

  Structural evolution in networks of coupled maps with asymmetric influence amplification [paper].

  In:

  2018 International Symposium on Nonlinear Theory and Its Applications (NOLTA2018), Tarragona, Spain, 2-6 September 2018.

  pp. 546-549.

• Schüle, Martin; Ott, Thomas,

  2018.

  Synchronization in cellular automata : the learning approach [paper].

  In:

  2018 International Symposium on Nonlinear Theory and Its Applications (NOLTA2018), Tarragona, Spain, 2-6 September 2018.

• Ott, Thomas; Schüle, Martin; Held, Jenny; Albert, Carlo; Stoop, Ruedi,

  2016.

  Clustered multidimensional scaling with Rulkov neurons [paper].

  In:

  2016 International Symposium on Nonlinear Theory and Its Applications.

  Nonlinear Theory and Applications 2016 (NOLTA), Yugawara, Japan, 27-30 November 2016.

  IEICE.

  pp. 389-392.

  Available from: https://doi.org/10.21256/zhaw-3532

• Schüle, Martin; Ott, Thomas; Schwendner, Peter,

  2016.

  Forecasting correlation structures [paper].

  In:

  Proceedings of the 2016 international symposium on nonlinear theory and its applications.

  2016 International Symposium on Nonlinear Theory and Its Applications (NOLTA2016), Yugawara, Japan, 27-30 November 2016.

  IEICE.

• Schüle, Martin,

  2018.

  Introduction to artificial neural network theory : lecture notes.

• Schwendner, Peter; Schüle, Martin; Hillebrand, Martin,

  2019.

  Correlation influence networks for sentiment analysis in European sovereign bonds.

  In:

  Financial Revolution - Sentiment Analysis, AI and Machine Learning, London, United Kingdom, 25-26 June 2019.

• Ott, Thomas; Glüge, Stefan; Schüle, Martin; Hill, Christopher,

  2018.

  A dynamic network approach for the analysis of pathogen transmission chains.

  In:

  The 26th Nonlinear Dynamics of Electronic Systems Conference, (NDES 2018), Acireale, 11-13 June 2018.

• Schüle, Martin; Ott, Thomas; Schwendner, Peter,

  2018.

  Influence networks in financial markets : forecast scenarios.

  In:

  NDES 2018, 26th Nonlinear Dynamics of Electronic Systems Conference, Acireale, Italy, June, 11-13 2018.

• Schwendner, Peter; Schüle, Martin; Hillebrand, Martin,

  2018.

  Correlation influence networks for sentiment analysis in European sovereign bonds.

  In:

  Financial Revolution - Sentiment Analysis, AI and Machine Learning, Zürich, Switzerland, 30 October 2018.

• Schwendner, Peter; Schüle, Martin; Ott, Thomas; Hillebrand, Martin,

  2018.

  Sentiment in European sovereign bonds.

  In:

  3rd European COST Conference on Mathematics for Industry in Switzerland, Winterthur, 6 September 2018.

  Available from: https://www.zhaw.ch/storage/engineering/institute-zentren/iamp/sp_acss/Schwendner_20180906.pdf

• Schüle, Martin; Ott, Thomas; Schwendner, Peter,

  2017.

  Forecasting correlation structures.

  In:

  NDES 2017, 25th Nonlinear Dynamics of Electronic Systems Conference, Zernez, 5-7 June 2017.

  Available from: https://www.ini.uzh.ch/~lorimert/NDES2017/assets/NDES2017_programme_booklet.pdf

• Schwendner, Peter; Schüle, Martin; Hillebrand, Martin,

  2017.

  Network analytics of sovereign bond dynamics.

  In:

  Frankfurt Summit on Network Analysis, Frankfurt, Germany, 26 October 2017.

• Schwendner, Peter; Schüle, Martin; Hillebrand, Martin,

  2017.

  Sovereign bond network dynamics.

  In:

  Mathfinance Conference, Frankfurt, Germany, 20-21 April 2017.

• Hillebrand, Martin; Ott, Thomas; Schüle, Martin; Schwendner, Peter,

  2016.

  European government bond dynamics and stability policies.

  In:

  ADEMU Workshop on Risk-Sharing Mechanisms for the European Union, Fiesole, Italy, 20-21 May 2016.

• Schüle, Martin; Schwendner, Peter,

  2016.

  European government bond dynamics and stability policies : taming contagion risks.

  In:

  9th Financial Risks International Forum, Paris, France, 21 March 2016.

• Schwendner, Peter; Schüle, Martin; Hillebrand, Martin,

  2015.

  European government bond dynamics and stability policies : taming contagion risks.

  In:

  Financial Risk and Network Theory, Cambridge, United Kingdom, 9 September 2015.

  Available from: https://www.jbs.cam.ac.uk/fileadmin/user_upload/research/centres/risk/downloads/150909_slides_schwendner.pdf

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