Biography

I am currently working as a visiting scholar at the Department of Statistics at Stanford University, advised by Wing Hung Wong. Also, I am a postdoctoral researcher in the group of Harald Binder where I am a Hans A. Krebs Medical Scientist Programme Fellow. My research interests lie at the intersection of statistics, computation, and modeling, focusing on biological applications. A significant part of my research is driven by building deep learning tools for analyzing (multi-omics) single-cell data.

I recently obtained my PhD degree, titled “Deep Generative Models for Designing Single-Cell RNA-Sequencing Experiments,” from the Institute of Medical Biometry and Statistics at the University of Freiburg. In addition, I currently serve as the head of the “Working Group for Single-Cell Data” of the German Association of Medical Informatics, Biometry, and Epidemiology (GMDS) and co-founded the Freiburg Young Scientist AI Network.

Previously, I did internships at the Max Planck Institute for Evolutionary Anthropology (Leipzig), the Federal Ministry of Finance Germany (Berlin), and the Korea Economic Research Institute (Seoul). I was born and grew up in Berlin, Germany.

Interests
  • Machine Learning
  • Explainable AI
  • Computational Biology
  • Biostatistics
Education

  • Visiting Scholar, April-September 2023

    Stanford University

  • PhD in Machine Learning (Summa cum laude), 2022

    Institute of Medical Biometry and Statistics, University of Freiburg

  • M.Sc. in Statistics, 2017

    Otto-von-Guericke-University, Magdeburg

  • Exchange Semester, 2016

    Middle East Technical University, Ankara

  • B.Sc. in Economics, 2015

    European-University Viadrina, Frankfurt (Oder)

  • Exchange Semester, 2013

    Sookmyung Women´s University, Seoul

Featured Publications

Eva Brombacher, Maren Hackenberg, Clemens Kreutz, Harald Binder, Martin Treppner (2022). The performance of deep generative models for learning joint embeddings of single-cell multi-omics data. Frontiers in Molecular Biosciences.

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Martin Treppner, Stefan Haug, Anna Köttgen, Harald Binder (2022). Designing Single Cell RNA-Sequencing Experiments for Learning Latent Representations. bioRxiv.

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Martin Treppner, Harald Binder, Moritz Hess (2022). Interpretable generative deep learning: an illustration with single cell gene expression data. Human Genetics.

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Martin Treppner, Adrián Salas-Bastos, Moritz Hess, Stefan Lenz, Tanja Vogel, Harald Binder (2021). Synthetic single cell RNA sequencing data from small pilot studies using deep generative models. Scientific Reports.

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