Universität Leipzig
Fakultät für Mathematik und Informatik
Abteilung Neuromorphe Informationsverarbeitung
Augustusplatz 10, Raum P 533
D-04109 Leipzig

Postanschrift: Postfach 10 09 20, D-04009 Leipzig

Telefon: +49 (341) 97-32136
Telefax: +49 (341) 97-32252

E-Mail: schmid@informatik.uni-leipzig.de

Sprechstunde: nach Vereinbarung

Forschungsinteressen

  • Maschinelles Lernen / konstruktivistisches maschinelles Lernen
  • Künstliche neuronale Netze
  • Datenanalyse und Merkmalserkennung
  • Angewandte Signalverarbeitung und Datenanalyse in Medizin und Biologie

Publikationen

  • M. Kukushkin, M. Bogdan, T. Schmid
    On optimizing morphological neural networks for hyperspectral image classification | LINK

    International Conference on Machine Vision (ICMV-2023), 15-18. Nov, Yerevan, Armenien, 2023

  • F. Erichsmeier, M. Kukushkin, J. Fiedler, M. Enders, S. Goertz, M. Bogdan, T. Schmid, R. Kaschuba
    Automating the purity analysis of oilseed rape through usage of hyperspectral imaging | LINK

    Proc. SPIE 12879, Photonic Technologies in Plant and Agricultural Science, 128790E, 2024

  • M. Kukushkin, M. Bogdan, T. Schmid
    BiCAE – A Bimodal Convolutional Autoencoder for Seed Purity Testing | LINK

    AAAI-2024: Workshop on AI to Accelerate Science and Engineering, 22-25 Feb., Vancouver, Kanada, 2024

  • M. Kukushkin, M. Enders, R. Kaschuba, M. Bogdan, T. Schmid
    Canola seed or not? Autoencoder-based Anomaly Detection in Agricultural Seed Production | LINK

    Kolloquium Landwirtschaft der Zukunft (KoLaZ-2023), 26-29 Sep., Berlin, Deutschland, 2023

  • B. Schindler, D. Günzel, T. Schmid
    Variational In-Silico Tissue Modelling Using Two-Path Impedance Spectroscopy and Normalizing Flows | LINK

    Proceedings of the 23rd International Conference on Biomedical Applications of Electrical Impedance Tomography (EIT), 2023

  • M. Mendikowski, B. Schindler, T. Schmid, R. Möller, M. Hattis
    Improved Techniques for Training Tabular GANs Using Cramer’s V Statistics | LINK

    Proceedings of the 36th Canadian Conference on Artificial Intelligence (CANAI), 2023

  • B. Schindler, D. Günzel, T. Schmid
    Neural Noise Module: Automated Error Modeling Using Adversarial Neural Networks | LINK

    Proceedings of the International Conference on Bioelectromagnetism, Electrical Bioimpedance, and Electrical Impedance Tomography 2022

  • B. Schindler, D. Günzel, T. Schmid
    Transcending Two-Path Impedance Spectroscopy with Machine Learning: A Computational Study on Modeling and Quantifying Electric Bipolarity of Epithelia | LINK

    International Journal on Advances in Life Sciences 13(3-4), S. 134-148, 2021

  • M. Böhm, T. Schmid
    An Algorithmic Approach to Establish a Lower Bound for the Size of Semiring Neural Networks | LINK

    Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), p. 311-316, 2021

  • T. Schmid, W. Hildesheim, T. Holoyad, K. Schumacher
    The AI Methods, Capabilities and Criticality Grid – A Three-Dimensional Classification Scheme for Artificial Intelligence Applications | LINK

    Künstliche Intelligenz (2021). https://doi.org/10.1007/s13218-021-00736-4

  • T. Schmid
    Batch-like Online Learning for More Robust Hybrid Artificial Intelligence: Deconstruction as a Machine Learning Process | LINK

    Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021). Stanford University, Palo Alto, California, USA, March 22-24, 2021.

  • T. Schmid, W. Hildesheim, T. Holoyad, K. Schumacher
    Managing and Understanding Artificial Intelligence Solutions – The AI-Methods, Capabilities and Criticality Grid and its Value for Decision Makers, Developers and Regulators | LINK

    1. Auflage, Beuth-Verlag, Berlin, 2020. ISBN 978-3-410-30407-4

  • M. Simões, D. Borra, E. Santamaría-Vázquez, GBT-UPM, M. Bittencourt-Villalpando, D. Krzemiñski, A. Miladinović, Neural_Engineering_Group, T. Schmid, H. Zhao, C. Amaral, B. Direito, J. Henriques, P. Carvalho, M. Castelo-Branco
    BCIAUT-P300: A Multi-Session and Multi-Subject Benchmark Dataset on Autism for P300-Based Brain-Computer-Interfaces | LINK

    Frontiers in Neuroscience 14:568104, 2020

  • T. Schmid
    Using Learning Algorithms to Create, Exploit and Maintain Knowledge Bases: Principles of Constructivist Machine Learning | LINK

    Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020). Stanford University, Palo Alto, California, USA, March 23-25, 2020.

  • V. S. Adama, B. Schindler, T. Schmid
    Using Time Domain and Pearson’s Correlation to Predict Attention Focus in Autistic Spectrum Disorder from EEG P300 Components | LINK

    Proceedings of the XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019. IFMBE Proceedings Vol. 76. Springer, S. 1890-1893, 2019

  • N. Bilski, T. Schmid
    Verantwortungsfindung beim Einsatz maschinell lernender Systeme | LINK

    Neue Juristische Online-Zeitschrift (NJOZ) 2019(20), S. 657-688

  • T. Schmid
    Deconstructing the Final Frontier of Artificial Intelligence: Five Theses for a Constructivist Machine Learning | LINK

    Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019), Stanford University, Palo Alto, California, USA, 2019

  • T. Schmid
    Automatisierte Analyse von Impedanzspektren mittels konstruktivistischen maschinellen Lernens | LINK

    Dissertation, Leipzig, 2018

  • T. Schmid, D. Günzel, M. Bogdan
    Automated Quantification of the Resistance of Epithelial Cell Layers from an Impedance Spectrum | LINK

    Proceedings of the Tenth International Conference on Bioinformatics, Biocomputational Systems and Biotechnologies, pp. 8-13, 2018

  • R. M. Fancy, L. Wang, T. Schmid, Q. Zeng, H. Wang, T. Zhou, D. J. Buchsbaum, Y. Song
    Characterization of the Interactions between Calmodulin and Death Receptor 5 in Triple-negative and Estrogen Receptor-positive Breast Cancer Cells: An Integrated Experimental and Computational Study | LINK

    The Journal of Biological Chemistry 291(24):12862-70, 2016

  • T. Schmid
    Forschungsorientierte Reflexion von Primärliteratur für Master-Studierende der Informatik und Bioinformatik | LINK

    HDS.Journal – Werkstattberichte : Lehr-Lern-Projekte, 2015/2, S. 46-52

  • T. Schmid, D. Günzel, M. Bogdan
    Automated Quantification of the Capacitance of Epithelial Cell Layers from an Impedance Spectrum | LINK

    Proceedings of the Seventh International Conference on Bioinformatics, Biocomputational Systems and Biotechnologies, pp. 27-32, 2015

  • T. Schmid
    Macht „Big Data“ synthetische Datensätze überflüssig? | LINK

    Lecture Notes in Informatics S-13, S. 61-64, 2014

  • T. Schmid, D. Günzel, M. Bogdan
    Automated Quantification of the Relation between Resistor-Capacitor Subcircuits from an Impedance Spectrum | LINK

    Proceedings of the International Conference on Bio-inspired Systems and Signal Processing, pp. 141-148, 2014

  • T. Schmid, M. Bogdan, D. Günzel
    Discerning Apical and Basolateral Properties of HT-29/B6 and IPEC-J2 Cell Layers by Impedance Spectroscopy, Mathematical Modeling and Machine Learning | LINK

    PLoS ONE 8(7): e62913, 2013

  • T. Schmid
    Wie man zwischen den Zahlen liest. Data-Mining und computergestützte Vorhersagen am Beispiel Bioinformatik | LINK

    Arbeitstitel – Forum für Leipziger Promovierende 5(1), S. 13-29, 2013

  • T. Schmid, D. Günzel, M. Bogdan
    Efficient prediction of x-axis intercepts of discrete impedance spectra | LINK

    Proceedings of the 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), p. 185-190, 2013

  • D. Günzel, S. S. Zakrzewski, T. Schmid, M. Pangalos, J. Wiedenhoeft, C. Blasse, C. Ozboda, S. M. Krug
    From TER to trans- and paracellular resistance: lessons from impedance spectroscopy | LINK

    Annals of the New York Academy of Sciences 1257, S. 142-151, 2012

  • T. Schmid, D. Günzel, M. Bogdan
    Using an artificial neural network to determine electrical properties of epithelia

    ICANN, Thessaloniki, LNCS vol. 6352, pp. 211-216, 2010