Vamsi Krishna Kommineni

Doctoral Researcher

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

LinkedIn: https://www.linkedin.com/in/vamsi-krishna-kommineni

Xing: https://www.xing.com/profile/Vamsikrishna_kommineni

iDiv publications

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

DOI

Max Planck Institute for Biogeochemistry
Hans-Knoell-Strasse 10
07745 Jena
Germany

vamsi_krishna.kommineni@idiv.de

Affiliation

Max Planck Institute for Biogeochemistry