Ali Doosthosseini

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Ali Doosthosseini obtained his B.Sc. degree in Computer Science at Sharif University of Technology and his M.Sc. in Applied Computer Science at University of Göttingen. During his masters studies, he tutored for Prof. Dr. Alexander Ecker’s “Machine Learning” course, and worked as a student research assistant for the project “ForestCare” at GWDG. His master’s thesis, supervised by Prof. Dr. Julian Kunkel, involved the development of SynForest, a tool for generating synthetic forest point clouds. SynForest utilized the High-Performance Computing (HPC) cluster at GWDG and was applied in deep learning, which was presented at the NHR’23 conference. Since November 2023, he is employed as a scientific researcher for the KISSKI project.
ORCID: 0000-0002-0654-1268

  • Machine Learning
  • Deep Learning and AI
  • Data Science and Big Data
  • High-Performance Computing
  • Enhancing Tree Segmentation in Large Forest Point Clouds with Synthetic Data, Ali Doosthosseini (Master's Thesis), Advisors: Prof. Dr. Julian Kunkel, Prof. Dr. Alexander Ecker, 2023-09, BibTeX
  • Developing machine learning models for wheat yield prediction using ground-based data, satellite-based actual evapotranspiration and vegetation indices (Mojtaba Naghdyzadegan Jahromi, Shahrokh Zand-Parsa, Fatemeh Razzaghi, Sajad Jamshidi, Shohreh Didari, Ali Doosthosseini, Hamid Reza Pourghasemi), In European Journal of Agronomy (146), pp. 126820, 2023 BibTeX DOI
  • Improve the Deep Learning Models in Forestry Based on Explanations and Expertise (Ximeng Cheng, Ali Doosthosseini, Julian Kunkel), In Frontiers in Plant Science, Schloss Dagstuhl – Leibniz-Zentrum für Informatik, ISSN: 1664-462X, 2022-05-19 BibTeX URL DOI PDF
  • Independent domination in subcubic graphs (Amirreza Akbari, Saieed Akbari, Ali Doosthosseini, Zahra Hadizadeh, Michael A Henning, Aria Naraghi), In Journal of Combinatorial Optimization (43), pp. 28–41, 2022 BibTeX DOI
  • Enhancing vegetation indices from sentinel-2 using multispectral UAV data, Google Earth engine and Machine Learning (Mojtaba Naghdyzadegan Jahromi, Shahrokh Zand-Parsa, Ali Doosthosseini, Fatemeh Razzaghi, Sajad Jamshidi), In Computational Intelligence for Water and Environmental Sciences (Array), pp. 507–523, Springer, 2022 BibTeX URL

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