Vamsi Krishna Kommineni
My Research Project
Leaf traits are important and often used to understand the plant and functional diversity but the numbers of leaf trait values are still strongly limited in space and time. To overcome the leaf trait data limitations, interdisciplinary research is required, in my PhD research we mainly concentrate on automatic extraction of leaf trait related information for around 15 million Digital Herbarium Specimen (DHS) images using deep neural networks. In the second step, we focus on building an intelligent machine learning system and analyzing intra- and interspecific leaf trait variation in space and time.
Short CV
| 11/2020 - now | Doctoral researcher at the Functional Biogeography group (Max Planck Institute for Biogeochemistry, Jena) |
| 04/2019 - 09/2020 | Master thesis and research assistant: Identifying drivers of intraspecific leaf trait variation in space and time from digitized herbarium specimen using machine learning approaches. (Max Planck Institute for Biogeochemistry, Jena) |
| 04/2018 - 08/2018 | Master internship: Reconstruction of noisy signals using compressed sensing and Fourier transformation with python. (Fachhochschule Jena) |
Social Media
iDiv-Publikationen
Kommineni, V. K., J. Kattge, J. Gaikwad, P. Baddam, S. Tautenhahn
(2020): Understanding Intraspecific Trait Variability Using Digital Herbarium Specimen Images. Biodiversity Information Science and Standards
Max Planck Institute for Biogeochemistry
Hans-Knöll-Straße 10
07745 Jena
Max-Planck-Institut für Biogeochemie
