Dominik Grimm

Affiliated Professor

Germany

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About

Dominik Grimm is an Affiliated Professor at Technische Universität München Fakultät für Informatik in Germany. His research interests encompass deep reinforcement learning, computer-aided molecular design, horticultural production planning, and the application of machine learning in biological contexts. Recent publications include studies on process design for azeotropic mixtures and improved weed segmentation in UAV imagery.

Articles (20)

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2025

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2021

Collaborators (11)

Thomas Haaf

Julius-Maximilians-Universität

GERMANY

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Arthur Korte

JProf

University of Wuerzburg

GERMANY

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Klaus Menrad

Professor

University of Applied Sciences Weihenstephan-Triesdorf

GERMANY

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Rupashree Dass

Aarhus University

DENMARK

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Bastian Blombach

Professor for Microbial Biotechnology

Technical University of Munich

GERMANY

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Gregory Owens

Assistant Professor

University of Victoria

CANADA

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Florian Haselbeck

Professor for Smart Farming

University of Applied Sciences Weihenstephan-Triesdorf

GERMANY

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Olga Zolotareva

Universität Hamburg

GERMANY

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Markus List

Technical University of Munich

GERMANY

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Nico Pfeifer

Professor

Eberhard Karls Universität Tübingen Mathematisch-Naturwissenschaftliche Fakultät

GERMANY

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Tim Kacprowski

GERMANY

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Dominik Grimm

Research Interests

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Articles (20)

Deep reinforcement learning enables conceptual design of processes for separating azeotropic mixtures without prior knowledge

GraphXForm: graph transformer for computer-aided molecular design

Procurement and production planning in horticulture considering short-term re-order opportunities

The combined effect of decreased stomatal density and aperture increases water use efficiency in maize

The benefits of permutation-based genome-wide association studies

Forecasting seasonally fluctuating sales of perishable products in the horticultural industry

Guiding questions to avoid data leakage in biological machine learning applications

Population-aware permutation-based significance thresholds for genome-wide association studies

Manually annotated and curated Dataset of diverse Weed Species in Maize and Sorghum for Computer Vision

Improved weed segmentation in UAV imagery of sorghum fields with a combined deblurring segmentation model.

HeliantHOME, a public and centralized database of phenotypic sunflower data

A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species

Deep learning-based early weed segmentation using motion blurred UAV images of sorghum fields

Efficient permutation-based genome-wide association studies for normal and skewed phenotypic distributions

Rat Hepatic Stellate Cell Line CFSC-2G: Genetic Markers and Short Tandem Repeat Profile Useful for Cell Line Authentication

Systematic analysis of the underlying genomic architecture for transcriptional–translational coupling in prokaryotes

Genetic Characterization of Rat Hepatic Stellate Cell Line HSC-T6 for In Vitro Cell Line Authentication

Machine Learning Outperforms Classical Forecasting on Horticultural Sales Predictions

Computational identification of protein complexes from network interactions: Present state, challenges, and the way forward

The AIMe registry for artificial intelligence in biomedical research

Collaborators (11)

Thomas Haaf

Arthur Korte

Klaus Menrad

Rupashree Dass

Bastian Blombach

Gregory Owens

Florian Haselbeck

Olga Zolotareva

Markus List

Nico Pfeifer

Tim Kacprowski

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