Dr. Thomas Schmid « Arbeitsgruppe Maschinelles Lernen

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

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