Marvin Kastner, M.Sc.
Adresse
Technische Universität Hamburg
Institut für Maritime Logistik
Am Schwarzenberg-Campus 4 (D)
21073 Hamburg
Kontaktdaten & Profile
Büro: Gebäude D Raum 5.007
Tel.: +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
Forschungsschwerpunkte
- simulationsgestütztes Planen von Container-Terminals
- Optimierung der Ablaufplanung im Yard von Container-Terminals
- technologiegestützte Verbesserung der maritimen Sicherheit
- Maschinelles Lernen in der maritimen Logistik
- Optimierung multivariater Black-box Funktionen
Vorträge und Workshops (Auszug)
- 26.09.2024 ein Vortrag auf der Hamburg International Conference of Logistics (HICL): "Hinterland rail connectivity of seaport container terminals" mit den Koautoren Owais Ahmed Shaikh, Yasser Shaikh und Anish Sundar Gowthaman
- 06.05.2024 ein Workshop an der Graduiertenakademie der TUHH: "Einführung in Jupyter Notebooks" [mehr]
- 25.01.2023 ein Vortrag auf dem 7. Suderburger Logistik-Forum: "KI-unterstützte Planung von Güterumschlaganlagen am Beispiel von Containerterminals"
- 15.09.2022 ein Vortrag bei den MLE-Days 2022: "Synthetische Daten für das Reinforcement-Learning bei Container-Terminal-Steuerungen"
- 28.06.2022 ein Workshop an der Graduiertenakademie der TUHH: "Einführung in Jupyter Notebooks" [mehr]
- 02.07.2021 ein Workshop bei den MLE-Days 2021: "Methoden des Maschinellen Lernens in der Maritimen Logistik" [zip]
- 16.03.2021 ein Workshop an der Graduiertenakademie der TUHH: "Einführung in Jupyter Notebooks" [mehr]
- 30.11.2020 im Rahmen der Vortragsreihe "Train Your Engineering Network" der MLE-Initiative: "How to Talk About Machine Learning with Jupyter Notebooks" [mehr]
- 22.11.2019 auf der DISRUPT NOW! AI for Hamburg: "Künstliche Intelligenz in der maritimen Wirtschaft" [mehr]
- 29.10.2019 im Rahmen der forschungsbörse: "Maritime Logistik - Ein Rundumschlag" [mehr]
- 23.10.2019 bei der Open Access Week 2019 an der TUHH: "Datenanalyse - Offener Workshop: Daten auswerten und visualisieren mit Jupyter Notebooks" [mehr] [git]
- 16.11.2018 beim GI DevCamp Hamburg: "Mobility Research and GDPR"
- 27.09.2018 beim SGKV AK zum Thema Lkw-Ankünfte: "Prognoseverfahren und neuronale Netze – Was ist möglich?"
Veröffentlichungen (Auszug)
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 |
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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
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). |
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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). |
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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
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: |
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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
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: |
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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