Max Planck Gesellschaft

Vamsi Krishna Kommineni

PhD Student at the FUSION Group (Faculty of Mathematics and Computer Sciences) in collaboration with Functional Biogeography Research Group and Graduate school(yDiv) of German Centre for Integrative Biodiversity Research(iDiv)

Address: Leutragraben 1, JenTower, 07743 Jena
Room: 21N03
Phone: +49 (0) 3641-9-46448
Email: vkommin@bgc-jena.mpg.de
Other contact details: vamsi.krishna.kommineni@uni-jena.de, vamsi_krishna.kommineni@idiv.de


Ph.D. Thesis Focus:

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.

Master Thesis Focus:

  • Evaluating global patterns of leaf size using machine learning approaches.
  • Extraction of leaf morphological traits from digital images using image processing tool (Trait Ex).
  • Creation of workflows to extract data from various environmental data bases
  • The work was performed in Python using the following libraries: Scikitlearn, Keras, Pdpbox, Matplotlib, Pandas, Numpy, Matplotlib, Pandas, Csv, PIL, time etc.

Research Interests:

  • Exploring different types of machine learning, deep learning and computer vision techniques.
  • Data management and biodiversity informatics.
  • Intrested to learn web development and game design using AI.
  • Implementation of new technology in the agriculture field.

CV:

2016-2020

Master Studies in the Scientific Instrumentation at Ernst-Abbe-Hochschule Jena, Jena, Germany

2019

Master thesis "Identifying drivers of intraspecific leaf trait variation in space and time from digitized herbarium specimens using machine learning approaches." at the Max Planck Institute for Biogeochemistry, Jena, Germany

2016

Bachelor thesis "Design Analysis and fabrication of Multidimensional trolley" in the "Innovation Centre, Guru Nanak Institutions, Hyd" at the Guru Nanak Institute of Technology, Hyderabad, India

2012 - 2016

Bachelor Studies in the "Mechanical Engineering" at the Jawaharlal Nehru Technological University Hyderabad, Telangana, India


Softwares:

  • Python, MATLAB, Latex, Trait Ex
  • AutoCAD, Pro/E, Ansys, Autodesk Inventor

Online Courses:

  • Programming for everybody: Getting started with python (Coursera)
  • Programming for everybody: Python data structures (Coursera)
  • Essential Math for Machine Learning: python edition (edX)
  • Machine Learning with Python: A Practical Introduction (edX)

Transferable Skills:

  • Create your personal competency profile (yDiv leipzig) by Dr Iris Köhle
  • Conflict Management (yDiv leipzig) by Dr Alexander Egeling
  • Horse-assisted leadership training (Grand Tamino Ranch, Leipzig) by Sandra Mader
  • Moderation of Online and Offline Meetings (yDiv leipzig) by Dr. Markus Gyger
  • Voice Matters! Speech and Vocal Training (Graduate Academy, FSU Jena) by Hilde Weeg
  • Leadership Skills in Academia and Industry (Graduate Academy, FSU Jena) by Peter Wagner

Interests:

  • Playing chess
  • Table Tennis
  • Listening music
  • International politics

Social media:

LinkedIn: https://www.linkedin.com/in/vamsi-krishna-kommineni
XING: https://www.xing.com/profile/Vamsikrishna_kommineni


Publications:

  1. Kommineni VK, Tautenhahn S, Baddam P, Gaikwad J, Wieczorek B, Triki A, Kattge J (2021) Comprehensive leaf size traits dataset for seven plant species from digitised herbarium specimen images covering more than two centuries . Biodiversity Data Journal 9: e69806. https://doi.org/10.3897/BDJ.9.e69806
  2. ---

Conferences:

  1. Kommineni VK, Kattge J, Gaikwad J, Baddam P, Tautenhahn S (2020) Understanding Intraspecific Trait Variability Using Digital Herbarium Specimen Images. Biodiversity Information Science and Standards 4: e59061. https://doi.org/10.3897/biss.4.59061
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