World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
5577
World Ranking
10792
National Ranking
543

Overview

Patrick Mäder is affiliated with Ilmenau University of Technology in Germany. Their research spans multiple fields primarily focused on Computer Science and Engineering, with notable contributions in Artificial Intelligence and Computer Vision and Pattern Recognition. Their work also extends into Ecology, Evolution, Behavior and Systematics, as well as Information Systems and Ecological Modeling.

The scientist's research topics cover a diverse range including Species Distribution and Climate Change, Software Engineering Research, Plant and Animal Studies, Remote Sensing in Agriculture, Advanced Memory and Neural Computing, Privacy-Preserving Technologies in Data, and Neural Networks and Applications.

Among recent publications authored or co-authored by Patrick Mäder are:

  • Sulfoximines as Rising Stars in Modern Drug Discovery? Current Status and Perspective on an Emerging Functional Group in Medicinal Chemistry, 2020, Journal of Medicinal Chemistry
  • The Flora Incognita app - Interactive plant species identification, 2021, Methods in Ecology and Evolution

Additional notable recent papers in related fields, where Mäder appears as co-author or part of the research community, include:

  • Multi-view classification with convolutional neural networks, 2021, PLoS ONE
  • Pollen analysis using multispectral imaging flow cytometry and deep learning, 2020, New Phytologist
  • Species delimitation 4.0: integrative taxonomy meets artificial intelligence, 2024, Trends in Ecology & Evolution

The scientist collaborates frequently with researchers including Jana Wäldchen, Michael Rzanny, Marco Seeland, Christian Cierpka, and David Boho. These collaborations have produced numerous publications reflecting interdisciplinary approaches to both computational and biological sciences.

Patrick Mäder has published extensively in various venues, with a significant presence in arXiv (Cornell University) reflecting an openness to preprint dissemination. Other frequent publication outlets include Frontiers in Plant Science, Neurocomputing, Experiments in Fluids, and the Journal of Advanced Joining Processes.

Best Publications

  • Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review

    Jana Wäldchen;Patrick Mäder

  • Recommending plant taxa for supporting on-site species identification

    Hans Christian Wittich;Marco Seeland;Jana Wäldchen;Michael Rzanny

  • Machine learning for image based species identification

    Jana Wäldchen;Patrick Mäder

  • Software traceability: trends and future directions

    Jane Cleland-Huang;Orlena C. Z. Gotel;Jane Huffman Hayes;Patrick Mäder

  • Automated plant species identification-Trends and future directions.

    Jana Wäldchen;Michael Rzanny;Marco Seeland;Patrick Mäder

  • Traceability Fundamentals

    Unknown

  • Strategic Traceability for Safety-Critical Projects

    Patrick Mader;Paul L. Jones;Yi Zhang;Jane Cleland-Huang

  • Multi-view classification with convolutional neural networks.

    Marco Seeland;Patrick Mäder

  • Traceability in the wild: automatically augmenting incomplete trace links

    Michael Rath;Jacob Rendall;Jin L. C. Guo;Jane Cleland-Huang

  • Do developers benefit from requirements traceability when evolving and maintaining a software system

    Patrick Mäder;Alexander Egyed

  • Motivation Matters in the Traceability Trenches

    Patrick Mader;Orlena Gotel;Ilka Philippow

  • Acquiring and preprocessing leaf images for automated plant identification: understanding the tradeoff between effort and information gain

    Michael Carsten Rzanny;Marco Seeland;Jana Wäldchen;Patrick Mäder

  • A survey on usage scenarios for requirements traceability in practice

    Elke Bouillon;Patrick Mäder;Ilka Philippow

  • Getting back to basics: Promoting the use of a traceability information model in practice

    Patrick Mader;Orlena Gotel;Ilka Philippow

  • Plant species classification using flower images-A comparative study of local feature representations.

    Marco Seeland;Michael Rzanny;Nedal Alaqraa;Jana Wäldchen

  • Pollen analysis using multispectral imaging flow cytometry and deep learning.

    Susanne Dunker;Elena Motivans;Elena Motivans;Demetra Rakosy;David Boho

  • Towards automated traceability maintenance

    Patrick Mäder;Orlena Gotel

  • Mind the gap: assessing the conformance of software traceability to relevant guidelines

    Patrick Rempel;Patrick Mäder;Tobias Kuschke;Jane Cleland-Huang

  • OmniDet: Surround View Cameras Based Multi-Task Visual Perception Network for Autonomous Driving

    Varun Ravi Kumar;Senthil Yogamani;Hazem Rashed;Ganesh Sitsu

  • Combining high-throughput imaging flow cytometry and deep learning for efficient species and life-cycle stage identification of phytoplankton

    Susanne Dunker;David Boho;Jana Wäldchen;Patrick Mäder

  • Design pattern recovery based on annotations

    Ghulam Rasool;Ilka Philippow;Patrick Mäder

  • SynDistNet: Self-Supervised Monocular Fisheye Camera Distance Estimation Synergized with Semantic Segmentation for Autonomous Driving

    Varun Ravi Kumar;Marvin Klingner;Senthil Yogamani;Stefan Milz

  • Enabling Automated Traceability Maintenance through the Upkeep of Traceability Relations

    Patrick Mäder;Orlena Gotel;Ilka Philippow

Frequent Co-Authors

Jane Cleland-Huang
Jane Cleland-Huang University of Notre Dame
Alexander Egyed
Alexander Egyed Johannes Kepler University of Linz
Jian Lu
Jian Lu Nanjing University
Ernst-Detlef Schulze
Ernst-Detlef Schulze Max Planck Institute for Biogeochemistry
Andrian Marcus
Andrian Marcus The University of Texas at Dallas
David Lo
David Lo Singapore Management University
Rocco Oliveto
Rocco Oliveto University of Molise
Björn Regnell
Björn Regnell Lund University
Miguel D. Mahecha
Miguel D. Mahecha Leipzig University
Jane Huffman Hayes
Jane Huffman Hayes University of Kentucky

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