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
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