Dipl.-Phys. Thomas Jochmann


Last update: August 18th, 2022

Specialized in data science, deep learning/machine learning/AI, and neurophysics. Physicist by heart, and training. 

Ph.D. student

E-mail

List of publications

Google Scholar profile

Research Interest

  • methods development for analyzing the brain's electrical activity with MEG, EEG
  • methods development for mapping the brain's microstructural tissue composition with quantitative MRI
  • data analysis with machine learning / deep learning for analytical chemistry and biomedical engineering
       

Short Bio

  • Ph.D. student in biomedical engineering with Prof. Jens Haueisen, co-supervised by Prof. Ferdinand Schweser, University at Buffalo
  • Past startup founder and CEO for 9 years at Invigon GmbH, Jena, Germany
  • Research and development on advanced driver-assistance systems for SMR Automotive, Stuttgart, Germany
  • Diploma (master's equivalent) in physics from Friedrich Schiller University Jena, Germany (thesis supervisor: Prof. Jürgen R. Reichenbach, Medical Physics Group)
Past stays as visiting researcher at:
  • Harvard Medical School (2 months, Prof. Matti Hämäläinen, Martinos Center for Biomedical Imaging)
  • University at Buffalo (1 month on-site, 24+ months close remote collaboration, Prof. Ferdinand Schweser, Buffalo Neuroimaging Analysis Center)
  • University of California in San Francisco (12 months, Prof. Srikantan S. Nagarajan, Biomagnetic Imaging Lab)
  • Trinity College Dublin (2 months, summer student, Prof. J. Michael D. Coey, Magnetism and Spin Electronics Group)

Downloads

Poster “How to train a Deep Convolutional Neural Network for Quantitative Susceptibility Mapping (QSM)”
(awarded 1st prize in the ISMRM Electro-Magnetic Tissue Properties Study Group Poster Competition, 2020)

Poster "Quantitative magnetic resonance imaging and quantitative susceptibility mapping"
(awarded 3rd prize in the ISMRM Quantitative MRI Study Group Public Engagement Competition, 2020)

Poster "DEEPOLE QUASAR–A Physics-Informed Deep Convolutional Neural Network to Disentangle MRI Phase Contrast Mechanisms"
(awarded 1st place for the best poster at the ISMRM Workshop on Machine Learning (Pt. II) in Washington, D.C., USA, 2018)