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)
- 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
| [182401] |
| Title: Teaching Machine Learning and Data Literacy to Students of Logistics using Jupyter Notebooks [DELFI Poster Award Winner]. <em>DELFI 2020</em> |
| Written by: Kastner, Marvin and Franzkeit, Janna and Lainé, Anna |
| in: <em>DELFI 2020</em>. (2020). |
| Volume: Number: |
| on pages: 365-366 |
| Chapter: |
| Editor: In Zender, Raphael and Ifenthaler, Dirk and Leonhardt, Thiemo and Schumacher, Clara (Eds.) |
| Publisher: Gesellschaft für Informatik e.V.: |
| Series: Lecture Notes in Informatics (LNI) - Proceedings |
| Address: Bonn |
| Edition: |
| ISBN: 978-3-88579-702-9 |
| how published: |
| Organization: |
| School: |
| Institution: |
| Type: |
| DOI: |
| URL: https://api.ltb.io/show/BMRWS |
| ARXIVID: |
| PMID: |
Note: malitup
Abstract: Teaching machine learning in fields outside of computer sciences can be challenging when the students do not have a solid code knowledge. In this work, the requirements for teaching data literacy and code literacy to students of logistics are explored. Specifically, the use of Jupyter Notebooks in a machine learning course for students in logistics is evaluated, using “Teaching and Learning with Jupyter” written by Barba et al. in 2019 that lists several teaching patterns for Jupyter Notebooks
2023
| [182401] |
| Title: Teaching Machine Learning and Data Literacy to Students of Logistics using Jupyter Notebooks [DELFI Poster Award Winner]. <em>DELFI 2020</em> |
| Written by: Kastner, Marvin and Franzkeit, Janna and Lainé, Anna |
| in: <em>DELFI 2020</em>. (2020). |
| Volume: Number: |
| on pages: 365-366 |
| Chapter: |
| Editor: In Zender, Raphael and Ifenthaler, Dirk and Leonhardt, Thiemo and Schumacher, Clara (Eds.) |
| Publisher: Gesellschaft für Informatik e.V.: |
| Series: Lecture Notes in Informatics (LNI) - Proceedings |
| Address: Bonn |
| Edition: |
| ISBN: 978-3-88579-702-9 |
| how published: |
| Organization: |
| School: |
| Institution: |
| Type: |
| DOI: |
| URL: https://api.ltb.io/show/BMRWS |
| ARXIVID: |
| PMID: |
Note: malitup
Abstract: Teaching machine learning in fields outside of computer sciences can be challenging when the students do not have a solid code knowledge. In this work, the requirements for teaching data literacy and code literacy to students of logistics are explored. Specifically, the use of Jupyter Notebooks in a machine learning course for students in logistics is evaluated, using “Teaching and Learning with Jupyter” written by Barba et al. in 2019 that lists several teaching patterns for Jupyter Notebooks
2022
| [182401] |
| Title: Teaching Machine Learning and Data Literacy to Students of Logistics using Jupyter Notebooks [DELFI Poster Award Winner]. <em>DELFI 2020</em> |
| Written by: Kastner, Marvin and Franzkeit, Janna and Lainé, Anna |
| in: <em>DELFI 2020</em>. (2020). |
| Volume: Number: |
| on pages: 365-366 |
| Chapter: |
| Editor: In Zender, Raphael and Ifenthaler, Dirk and Leonhardt, Thiemo and Schumacher, Clara (Eds.) |
| Publisher: Gesellschaft für Informatik e.V.: |
| Series: Lecture Notes in Informatics (LNI) - Proceedings |
| Address: Bonn |
| Edition: |
| ISBN: 978-3-88579-702-9 |
| how published: |
| Organization: |
| School: |
| Institution: |
| Type: |
| DOI: |
| URL: https://api.ltb.io/show/BMRWS |
| ARXIVID: |
| PMID: |
Note: malitup
Abstract: Teaching machine learning in fields outside of computer sciences can be challenging when the students do not have a solid code knowledge. In this work, the requirements for teaching data literacy and code literacy to students of logistics are explored. Specifically, the use of Jupyter Notebooks in a machine learning course for students in logistics is evaluated, using “Teaching and Learning with Jupyter” written by Barba et al. in 2019 that lists several teaching patterns for Jupyter Notebooks
2021
| [182401] |
| Title: Teaching Machine Learning and Data Literacy to Students of Logistics using Jupyter Notebooks [DELFI Poster Award Winner]. <em>DELFI 2020</em> |
| Written by: Kastner, Marvin and Franzkeit, Janna and Lainé, Anna |
| in: <em>DELFI 2020</em>. (2020). |
| Volume: Number: |
| on pages: 365-366 |
| Chapter: |
| Editor: In Zender, Raphael and Ifenthaler, Dirk and Leonhardt, Thiemo and Schumacher, Clara (Eds.) |
| Publisher: Gesellschaft für Informatik e.V.: |
| Series: Lecture Notes in Informatics (LNI) - Proceedings |
| Address: Bonn |
| Edition: |
| ISBN: 978-3-88579-702-9 |
| how published: |
| Organization: |
| School: |
| Institution: |
| Type: |
| DOI: |
| URL: https://api.ltb.io/show/BMRWS |
| ARXIVID: |
| PMID: |
Note: malitup
Abstract: Teaching machine learning in fields outside of computer sciences can be challenging when the students do not have a solid code knowledge. In this work, the requirements for teaching data literacy and code literacy to students of logistics are explored. Specifically, the use of Jupyter Notebooks in a machine learning course for students in logistics is evaluated, using “Teaching and Learning with Jupyter” written by Barba et al. in 2019 that lists several teaching patterns for Jupyter Notebooks
2020
| [182401] |
| Title: Teaching Machine Learning and Data Literacy to Students of Logistics using Jupyter Notebooks [DELFI Poster Award Winner]. <em>DELFI 2020</em> |
| Written by: Kastner, Marvin and Franzkeit, Janna and Lainé, Anna |
| in: <em>DELFI 2020</em>. (2020). |
| Volume: Number: |
| on pages: 365-366 |
| Chapter: |
| Editor: In Zender, Raphael and Ifenthaler, Dirk and Leonhardt, Thiemo and Schumacher, Clara (Eds.) |
| Publisher: Gesellschaft für Informatik e.V.: |
| Series: Lecture Notes in Informatics (LNI) - Proceedings |
| Address: Bonn |
| Edition: |
| ISBN: 978-3-88579-702-9 |
| how published: |
| Organization: |
| School: |
| Institution: |
| Type: |
| DOI: |
| URL: https://api.ltb.io/show/BMRWS |
| ARXIVID: |
| PMID: |
Note: malitup
Abstract: Teaching machine learning in fields outside of computer sciences can be challenging when the students do not have a solid code knowledge. In this work, the requirements for teaching data literacy and code literacy to students of logistics are explored. Specifically, the use of Jupyter Notebooks in a machine learning course for students in logistics is evaluated, using “Teaching and Learning with Jupyter” written by Barba et al. in 2019 that lists several teaching patterns for Jupyter Notebooks
2019
| [182401] |
| Title: Teaching Machine Learning and Data Literacy to Students of Logistics using Jupyter Notebooks [DELFI Poster Award Winner]. <em>DELFI 2020</em> |
| Written by: Kastner, Marvin and Franzkeit, Janna and Lainé, Anna |
| in: <em>DELFI 2020</em>. (2020). |
| Volume: Number: |
| on pages: 365-366 |
| Chapter: |
| Editor: In Zender, Raphael and Ifenthaler, Dirk and Leonhardt, Thiemo and Schumacher, Clara (Eds.) |
| Publisher: Gesellschaft für Informatik e.V.: |
| Series: Lecture Notes in Informatics (LNI) - Proceedings |
| Address: Bonn |
| Edition: |
| ISBN: 978-3-88579-702-9 |
| how published: |
| Organization: |
| School: |
| Institution: |
| Type: |
| DOI: |
| URL: https://api.ltb.io/show/BMRWS |
| ARXIVID: |
| PMID: |
Note: malitup
Abstract: Teaching machine learning in fields outside of computer sciences can be challenging when the students do not have a solid code knowledge. In this work, the requirements for teaching data literacy and code literacy to students of logistics are explored. Specifically, the use of Jupyter Notebooks in a machine learning course for students in logistics is evaluated, using “Teaching and Learning with Jupyter” written by Barba et al. in 2019 that lists several teaching patterns for Jupyter Notebooks