Marvin Kastner, M.Sc.
Address
Hamburg University of Technology
Institute of Maritime Logistics
Am Schwarzenberg-Campus 4 (D)
21073 Hamburg
Contact Details & Profiles
Office: building D room 5.007
Phone: +49 40 42878 4793
E-mail: marvin.kastner(at)tuhh(dot)de
ORCiD: 0000-0001-8289-2943
LinkedIn: https://www.linkedin.com/in/marvin-kastner/
ResearchGate: https://www.researchgate.net/profile/Marvin-Kastner
Google scholar: https://scholar.google.de/citations?user=lAR-oVAAAAAJ&hl=de&oi=ao
Scopus: https://www.scopus.com/authid/detail.uri?authorId=57221938031
Research Focus
- Simulation-based Design of Container Terminals
- Optimization of Yard Operations at Container Terminals
- Data-driven Improvement of Maritime Security
- Machine Learning in Maritime Logistic
- Optimization of Multivariate Black-box Functions
Presentations and workshops (Excerpt)
- 25.01.2023 a talk at the 7. Suderburger Logistics Forum: "AI-assisted planning of cargo handling facilities with the example of container terminals" (title translated)
- 15.09.2022 a talk at the MLE-Days 2022: "Synthetic data for reinforcement learning in container terminal control systems."
- 28.06.2022 a workshop at the Graduate Academy of TUHH: "Introduction to Jupyter Notebooks" (title translated) [more]
- 02.07.2021 a workshop at the MLE-Days 2021: "Machine Learning in Maritime Logistics" (title translated) [zip]
- 16.03.2021 a workshop at the Graduate Academy of TUHH: "Introduction to Jupyter Notebooks" (title translated) [more]
- 30.11.2020 in the lecture series "Train Your Engineering Network" of the MLE initiative: "How to Talk About Machine Learning with Jupyter Notebooks"
- 22.11.2019 at DISRUPT NOW! AI for Hamburg: "Artificial Intelligence in Maritime Economy" (title translated) [more]
- 29.10.2019 in the context of forschungsbörse: "Maritime Logistics - an all-round cover" (title translated) [more]
- 23.10.2019 at the Open Access Week 2019 at TUHH: "Data Analysis - Describe and Visualize Data with Jupyter Notebooks" (title translated) [more] [git]
- 16.11.2018 at the GI DevCamp Hamburg: "Mobility Research and GDPR"
- 27.09.2018 at SGKV WG regarding truck arrivals: "Forecasting and Neural Networks – What is possible?" (title translated)
2024
| [182411] |
| Title: Mit Jupyter Notebooks prüfen. |
| Written by: Kastner, Marvin and Podleschny, Nicole |
| in: <em>Beitrag zur Poster-Session des e-Prüfungs-Symposiums (ePS) in Siegen</em>. (2019). |
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| DOI: 10.15480/882.2435 |
| URL: http://hdl.handle.net/11420/3553 |
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Note: malitup
Abstract: The learning outcome of the interdisciplinary master module „machine learning in logistics“ is the ability to visualize, clean, and interpreting big data, as well as identifying connections with methods of machine learning. The media-didactical challenge is to make machine learning accessible for those students who do not possess sound programming skills. For this, we chose Jupyter Notebooks. In the exercises as well as in the final exam, students use a pre-structured Jupyter Notebook in order to write or rewrite code. They also document their answers and solutions. The poster documents the implementation of Jupyter Notebooks into the exam scenario and describes the examining process
2023
| [182411] |
| Title: Mit Jupyter Notebooks prüfen. |
| Written by: Kastner, Marvin and Podleschny, Nicole |
| in: <em>Beitrag zur Poster-Session des e-Prüfungs-Symposiums (ePS) in Siegen</em>. (2019). |
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| DOI: 10.15480/882.2435 |
| URL: http://hdl.handle.net/11420/3553 |
| ARXIVID: |
| PMID: |
Note: malitup
Abstract: The learning outcome of the interdisciplinary master module „machine learning in logistics“ is the ability to visualize, clean, and interpreting big data, as well as identifying connections with methods of machine learning. The media-didactical challenge is to make machine learning accessible for those students who do not possess sound programming skills. For this, we chose Jupyter Notebooks. In the exercises as well as in the final exam, students use a pre-structured Jupyter Notebook in order to write or rewrite code. They also document their answers and solutions. The poster documents the implementation of Jupyter Notebooks into the exam scenario and describes the examining process
2022
| [182411] |
| Title: Mit Jupyter Notebooks prüfen. |
| Written by: Kastner, Marvin and Podleschny, Nicole |
| in: <em>Beitrag zur Poster-Session des e-Prüfungs-Symposiums (ePS) in Siegen</em>. (2019). |
| Volume: Number: |
| on pages: |
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| DOI: 10.15480/882.2435 |
| URL: http://hdl.handle.net/11420/3553 |
| ARXIVID: |
| PMID: |
Note: malitup
Abstract: The learning outcome of the interdisciplinary master module „machine learning in logistics“ is the ability to visualize, clean, and interpreting big data, as well as identifying connections with methods of machine learning. The media-didactical challenge is to make machine learning accessible for those students who do not possess sound programming skills. For this, we chose Jupyter Notebooks. In the exercises as well as in the final exam, students use a pre-structured Jupyter Notebook in order to write or rewrite code. They also document their answers and solutions. The poster documents the implementation of Jupyter Notebooks into the exam scenario and describes the examining process
2021
| [182411] |
| Title: Mit Jupyter Notebooks prüfen. |
| Written by: Kastner, Marvin and Podleschny, Nicole |
| in: <em>Beitrag zur Poster-Session des e-Prüfungs-Symposiums (ePS) in Siegen</em>. (2019). |
| Volume: Number: |
| on pages: |
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| Editor: |
| Publisher: |
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| Address: |
| Edition: |
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| how published: |
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| Type: |
| DOI: 10.15480/882.2435 |
| URL: http://hdl.handle.net/11420/3553 |
| ARXIVID: |
| PMID: |
Note: malitup
Abstract: The learning outcome of the interdisciplinary master module „machine learning in logistics“ is the ability to visualize, clean, and interpreting big data, as well as identifying connections with methods of machine learning. The media-didactical challenge is to make machine learning accessible for those students who do not possess sound programming skills. For this, we chose Jupyter Notebooks. In the exercises as well as in the final exam, students use a pre-structured Jupyter Notebook in order to write or rewrite code. They also document their answers and solutions. The poster documents the implementation of Jupyter Notebooks into the exam scenario and describes the examining process
2020
| [182411] |
| Title: Mit Jupyter Notebooks prüfen. |
| Written by: Kastner, Marvin and Podleschny, Nicole |
| in: <em>Beitrag zur Poster-Session des e-Prüfungs-Symposiums (ePS) in Siegen</em>. (2019). |
| Volume: Number: |
| on pages: |
| Chapter: |
| Editor: |
| Publisher: |
| Series: |
| Address: |
| Edition: |
| ISBN: |
| how published: |
| Organization: |
| School: |
| Institution: |
| Type: |
| DOI: 10.15480/882.2435 |
| URL: http://hdl.handle.net/11420/3553 |
| ARXIVID: |
| PMID: |
Note: malitup
Abstract: The learning outcome of the interdisciplinary master module „machine learning in logistics“ is the ability to visualize, clean, and interpreting big data, as well as identifying connections with methods of machine learning. The media-didactical challenge is to make machine learning accessible for those students who do not possess sound programming skills. For this, we chose Jupyter Notebooks. In the exercises as well as in the final exam, students use a pre-structured Jupyter Notebook in order to write or rewrite code. They also document their answers and solutions. The poster documents the implementation of Jupyter Notebooks into the exam scenario and describes the examining process
2019
| [182411] |
| Title: Mit Jupyter Notebooks prüfen. |
| Written by: Kastner, Marvin and Podleschny, Nicole |
| in: <em>Beitrag zur Poster-Session des e-Prüfungs-Symposiums (ePS) in Siegen</em>. (2019). |
| Volume: Number: |
| on pages: |
| Chapter: |
| Editor: |
| Publisher: |
| Series: |
| Address: |
| Edition: |
| ISBN: |
| how published: |
| Organization: |
| School: |
| Institution: |
| Type: |
| DOI: 10.15480/882.2435 |
| URL: http://hdl.handle.net/11420/3553 |
| ARXIVID: |
| PMID: |
Note: malitup
Abstract: The learning outcome of the interdisciplinary master module „machine learning in logistics“ is the ability to visualize, clean, and interpreting big data, as well as identifying connections with methods of machine learning. The media-didactical challenge is to make machine learning accessible for those students who do not possess sound programming skills. For this, we chose Jupyter Notebooks. In the exercises as well as in the final exam, students use a pre-structured Jupyter Notebook in order to write or rewrite code. They also document their answers and solutions. The poster documents the implementation of Jupyter Notebooks into the exam scenario and describes the examining process