Philipp Heilmann
■ Postdoctoral Researcher
■ Research areas
Development of machine learning and deep learning models for genomic selection.
Feature engineering using genomic, environmental, and other data sources.
Development of statistical tools and software to simulate plant breeding trials and selection.
■ CV
| 2025–2027 | Postdoctoral Researcher |
| 2019–2025 | PhD Student |
| 2016–2019 | M.Sc. Environmental Science |
| 2012–2016 | B.Sc. Environmental Management |
■ Awards
21. Kurt von Rümker Prize (GPZ) - awarded to an early-career researcher for the best PhD presentation
■ Publications
Heilmann PG, Grosch E, Frisch M, Herrmann M, Beuch S, Kurra V, Mascher M, Avni R, Oldach K, Röhrs I, Hanemann A, Mehta RR, Reinbrecht C, Serfling A, Stahl A, Stucke M, Abbadi A, Kox T, Zenke-Philippi C (2025) Haplotype-based autoencoders can reduce the dataset dimension and estimate haplotype block effects in different crop species. BMC Bioinformatics 26:289
Heilmann PG, Difabachew YF, Frisch M, Moritz AL, Stahl A, Wittkop B, Snowdon RJ, Koch M, Kirchhoff M, Cselényi L, Wolf M, Förster J, Zenke-Philippi C (2024) Machine learning for prediction of resistance scores in wheat (Triticum aestivum L.). Plant Breeding 144:192–205
Heilmann PG, Frisch M, Abbadi A, Kox T, Herzog E (2023) Stacked ensembles on basis of parentage information can predict hybrid performance with an accuracy comparable to marker-based GBLUP. Frontiers in Plant Science 14:1178902
■ Contact
E-mail: philipp.g.heilmann[at]agrar.uni-giessen.de
Phone: (0641) 99 37543
IFZ Room B209