Added abstracts to publications

Cbblr · Cbblr · commit 59755bc6b2d9 · 2025-07-15T15:07:50.000+02:00
diff --git a/_bibliography/papers.bib b/_bibliography/papers.bib
@@ -2,101 +2,193 @@
 ---
 
 @inproceedings{genath2021asim1,
+  abbr={Paper},
   author    = {Genath, Jonas and Bergmann, Sören and Spieckermann, Sven and Stauber, Stephan and Feldkamp, Niclas},
   title     = {Entwicklung einer integrierten Lösung für das Data Farming und die Wissensentdeckung in Simulationsdaten},
   booktitle = {Proceedings der ASIM Fachtagung „Simulation in Produktion und Logistik“},
   year      = {2021},
   pages     = {377--386},
   bibtex_show={true},
+  abstract = {
+    Simulation is an established methodology for planning and evaluating manufacturing and logistics systems. In contrast to classical simulation studies, the method of knowledge discovery in simulation data uses a simulation model as a data generator (data farming). Subsequently, hidden, previously unknown and potentially useful cause-effect relationships can be uncovered on the generated data using data mining and visual analytics methods. So far, however, there is a lack of integrated, easy-to-use software solutions for the application of the data farming in operational practice. This paper presents such an integrated solution, which allows for generating experiment designs, implements a method to distribute the necessary experiment runs, and provides the user with tools to analyze and visualize the result data.
+  }
 }
 
 @inproceedings{genath2021asim2,
+  abbr={Paper},
   author    = {Genath, Jonas and Bergmann, Sören and Feldkamp, Niclas and Straßburger, Steffen},
   title     = {Automatisierung im Prozess der Wissensentdeckung in Simulationsdaten - Charakterisierung der Ergebnisdaten},
   booktitle = {Proceedings der ASIM Fachtagung „Simulation in Produktion und Logistik“},
   year      = {2021},
   pages     = {367--376},
   bibtex_show={true},
+  abstract = {
+    The traditional application of simulation in production and logistics is usually aimed at changing certain parameters in order to answer clearly defined objectives or questions. In contrast to this approach, the method of knowledge discovery in simulation data (KDS) uses a simulation model as a data generator (data farming). Subsequently using data mining methods, hidden, previously unknown and potentially useful cause-effect relationships can be uncovered. So far, however, there is a lack of guidelines and automatization-tools for non-experts or novices in KDS, which leads to a more difficult use in industrial applications and prevents a broader utilization. This paper presents a concept for automating the first step of the KDS, which is the process of characterization of the result data, using meta learning and validates it on small case study.
+  }
 }
 
 @inproceedings{genath2021wsc1,
+  abbr={Paper},
   author    = {Genath, Jonas and Bergmann, Soeren and Strassburger, Steffen and Stauber, Stephan and Spieckermann, Sven},
   title     = {An Integrated Solution for Data Farming and Knowledge Discovery in Simulation Data: A Case Study of the Battery Supply of a Vehicle Manufacturer},
   booktitle = {Proceedings of the 2021 Winter Simulation Conference},
   address   = {Phoenix, AZ, USA},
   year      = {2021},
   bibtex_show={true},
+  abstract = {
+    The development of logistics concepts, here for supplying an automobile production with batteries, is a major challenge, especially when there are uncertainties. In order to mitigate this, the method of knowledge discovery in simulation data is to be applied here. In order to enable the planners to easily use the method, a tool that can be easily integrated into practical use (SimAssist-4farm) was developed. 
+  }
 }
 
 @unpublished{genath2021wsc2,
+  abbr={Vortrag},
   author    = {Genath, Jonas},
   title     = {Automation in the Process of Knowledge Discovery in Simulation Data},
   howpublished = {Proceedings 2021 Winter Simulation Conference, Vortrag und Poster},
   address   = {Phoenix, AZ, USA},
   year      = {2021},
-  additional_info = {Winter Simulation Conference, Phoenix, AZ, USA, Vortrag und Poster}
+  additional_info = {Winter Simulation Conference, Phoenix, AZ, USA, Vortrag und Poster},
+  abstract = {
+    In contrast to classical simulation studies, the method of knowledge discovery in simulation data uses a simulation model as a data generator (data farming). Subsequently using data mining methods, hidden, previously unknown and potentially useful cause-effect relationships can be uncovered. So far, however, there is a lack of support and automatization tools for non-experts or novices in knowledge discovery in simulation data, which leads to a more difficult use in industrial applications and prevents a broader utilization. In this work, we propose a concept which provides an approach for automating and supporting knowledge discovery in simulation data.
+  }
 }
 
 @article{genath2022zfwf,
+  abbr  ={Article},
   author  = {Genath, Jonas and Bergmann, Soeren and Straßburger, Steffen and Spieckermann, Sven and Stauber, Stephan},
   title   = {Data Farming und Wissensentdeckung in Simulationsdaten - Entwicklung einer integrierten Lösung},
   journal = {Zeitschrift für wirtschaftlichen Fabrikbetrieb},
   number  = {3},
   year    = {2022},
   bibtex_show={true},
+  abstract = {
+    Simulation als Methode der Digitalen Fabrik ist seit langem etabliert
+    zur Unterstützung der Planung von Produktions- und Logistiksystemen.
+    In Ergänzung zu bisher vorherrschenden Simulationsstudien
+    wird bei der hier vorgestellten Methode der Wissensentdeckung in
+    Simulationsdaten ein Simulationsmodell als Datengenerator verwendet.
+    Dadurch können mittels Data-Mining- und Visual-Analytics-Methoden
+    versteckte und potenziell nützliche Ursache-Wirkungs-Beziehungen
+    in den generierten Daten aufgedeckt werden. Bislang fehlte es
+    jedoch an integrierten Softwarelösungen für die Praxis.
+  }
 }
 
 @article{genath2022sne,
+  abbr={Article},
   author  = {Genath, Jonas and Bergmann, Sören and Spieckermann, Sven and Stauber, Stephan and Feldkamp, Niclas},
   title   = {Development of an Integrated Solution for Data Farming and Knowledge Discovery in Simulation Data},
   journal = {Simulation Notes Europe},
   volume  = {32},
   number  = {2},
   year    = {2022},
   bibtex_show={true},
+  abstract = {
+    Simulation is an established methodology for
+    planning and evaluating manufacturing and logistics systems.
+    In contrast to classical simulation studies, the
+    method of knowledge discovery in simulation data uses a
+    simulation model as a data generator (data farming). Subsequently,
+    hidden, previously unknown and potentially
+    useful cause-effect relationships can be uncovered on the
+    generated data using data mining and visual analytics
+    methods. So far, however, there was a lack of integrated,
+    easy-to-use software solutions for the application of the
+    data farming in operational practice. This paper presents
+    such an integrated solution, which allows generating experiment
+    designs, implements a method to distribute the
+    necessary experiment runs, and provides the user with
+    tools to analyze and visualize the result data.
+  }
 }
 
 @inproceedings{feldkamp2022wsc,
+  abbr={Paper},
   author    = {Feldkamp, Niclas and Genath, Jonas and Strassburger, Steffen},
   title     = {Explainable AI for Data Farming Output Analysis: A Use Case for Knowledge Generation through Black-Box Classifiers},
   booktitle = {Proceedings of the 2022 Winter Simulation Conference},
   address   = {Singapur, SGP},
   year      = {2022},
   bibtex_show={true},
+  abstract = {
+    Data farming combines large-scale simulation experiments with high performance computing and
+    sophisticated big data analysis methods. The portfolio of analysis methods for those large amounts of
+    simulation data still yields potential to further development, and new methods emerge frequently. Among
+    the most interesting are methods of explainable artificial intelligence (XAI). Those methods enable the use
+    of black-box-classifiers for data farming output analysis, which has been shown in a previous paper. In this
+    paper, we apply the concept for XAI-based data farming analysis on a complex, real world case study to
+    investigate the suitability of such concept in a real world application, and we also elaborate on which blackbox
+    classifiers are actually the most suitable for large-scale simulation data that accumulates in a data
+    farming project.
+  }
 }
 
 @inproceedings{genath2023wsc,
+  abbr={Paper},
   author    = {Genath, Jonas and Strassburger, Steffen},
   title     = {How Not to Visualize your Simulation Output Data},
   booktitle = {Proceedings of the 2023 Winter Simulation Conference},
   address   = {San Antonio, TX, USA},
   year      = {2023},
   bibtex_show={true},
+  abstract ={
+    Hybrid modeling and simulation studies combine well-defined methods from other disciplines with a
+    simulation technique. Especially in the area of output data analysis of simulation studies, there is great
+    potential for hybrid approaches that incorporate methods from machine learning and AI. For their successful
+    application, the analytical capabilities of machine learning and AI must be combined with the interpretive
+    capabilities of humans. In most cases, this connection is achieved through visualizations. As methods
+    become more complicated, the demands on visualizations are increasing. In this paper, we conduct a data
+    farming study and delve into the analysis of the output data. In doing so, we uncover typical errors in
+    visualizations making the interpretation and evaluation of the data difficult or misleading. We then apply
+    concepts of visual analytics to these visualizations and derive general guidelines to help simulation users
+    to analyze their simulation studies and present results unambiguously and clearly.
+  }
 }
 
 @inproceedings{amthor2023codeocean,
+  abbr = {Paper},
   author    = {Genath, Jonas and Amthor, Peter and Döring, Ulf and Fischer, Daniel and  Kreuzberger, Gunther},
   title     = {Erfahrungen bei der Integration des Autograding-Systems CodeOcean in die universitäre Programmierausbildung},
   booktitle = {Proceedings of the sixth workshop "Automatische Bewertung von Programmieraufgaben"},
   publisher = {Gesellschaft für Informatik e. V.},
   year      = {2023},
   bibtex_show={true},
+  abstract = {
+    Effective and efficient university programming education increasingly requires
+    the use of automated assessment systems. As part of the examING2 project, the
+    AutoPING subproject is testing the use of the open-source autograding system CodeOcean for comprehensive
+    courses and exams at the Technical University of Ilmenau with the aim of enabling and promoting self-directed and
+    competence-oriented learning. This article provides an overview of
+    initial project experiences in adapting didactic scenarios in programming education to
+    test-driven software development and the generation of feedback. It discusses key
+    findings from the perspective of students and teachers, challenges and approaches to
+    integrating and expanding CodeOcean for new fields of application, and
+    opens up future perspectives.
+  }
 }
 
 @unpublished{kreuzberger2024turn,
+  abbr={Vortrag},
   author    = {Kreuzberger, Gunther and Genath, Jonas and Fischer, Daniel},
   title     = {ChatGPT meets CodeOcean: Integeration KI-basierten Feedbacks in Autograder-Systeme},
   howpublished = {TURN Conference, Vortrag und Poster},
   address   = {Berlin},
   year      = {2024},
-  additional_info = {TURN Conference, Berlin, Vortrag und Poster}
+  additional_info = {TURN Conference, Berlin, Vortrag und Poster},
+  abstract = {
+    Im Projekt examING – Digitalisierung des kompetenzorientierten Prüfens für ingenieurwissenschaftliche Bachelorstudiengänge wird untersucht, wie Feedback zu Programmieraufgaben durch generative KI verbessert werden kann. Ziel ist es, Rückmeldungen individueller, differenzierter, konstruktiver und sprachlich variabler zu gestalten. Der entwickelte Ansatz motiviert Lernende durch praxisnahe Aufgaben, umfangreiche Übungsmöglichkeiten und integrierte Programmierwerkzeuge. Zur Umsetzung wurde ChatGPT über eine API in das webbasierte Autograder-System CodeOcean eingebunden. Mithilfe strukturierter Prompts wird sachorientiertes Feedback generiert, das gezielt auf die eingereichten Lösungen eingeht. Erste Ergebnisse zeigen, dass sich durch die KI-Integration qualitativ hochwertiges, anpassbares Feedback entlang definierter Dimensionen erzeugen lässt. Die nächsten Schritte umfassen die nutzerfreundliche Darstellung des Feedbacks, eine Evaluation der Akzeptanz sowie die Erweiterung auf weitere Anwendungsfälle wie Kommentaranfragen, Hinweiserstellungen und Aufgabenklärung. Das Projekt wird von der Stiftung Innovation in der Hochschullehre im Rahmen des Bund-Länder-Programms „Hochschule durch Digitalisierung stärken“ gefördert.
+  }
 }
 
 @unpublished{genath2025digitell,
+  abbr={Vortrag},
   author    = {Genath, Jonas and Fischer, Daniel},
   title     = {Einsatz eines Autograders in der universitären Programmierausbildung zur Verbesserung des digital gestützten Lernens und Prüfens für Ingenieure},
   howpublished = {DigiTeLL – Digital Teaching and Learning Lab, Vortrag und Poster},
   address   = {Frankfurt},
   year      = {2025},
-  additional_info = {DigiTeLL – Digital Teaching and Learning Lab, Frankfurt, Vortrag und Poster}
+  additional_info = {DigiTeLL – Digital Teaching and Learning Lab, Frankfurt, Vortrag und Poster},
+  abstract = {
+    As part of the redesign of a course on operational digitization, the desire was expressed to also digitize teaching itself to a greater extent. The aim is to give students a basic understanding of programming so that they can better understand digital possibilities. The autograder CodeOcean was used as a suitable tool—a web-based open-source platform with a development environment, collaboration functions, and LMS integration. In the “examING” project, funded by the Foundation for Innovation in Higher Education, new digital teaching and examination formats for Python training were developed and tested with CodeOcean. So far, around 270 students have participated in the courses, and around 120 have taken digital exams. Feedback and observations accompanied the implementation. Automated assessment by CodeOcean facilitates individual learning and promotes targeted skills development. The article reflects on the experiences, identifies challenges, and outlines further developments, in particular the planned integration of generative AI such as ChatGPT to further improve feedback for students.
+  }
 }
diff --git a/assets/json/resume.json b/assets/json/resume.json
@@ -1,7 +1,7 @@
 {
   "basics": {
     "name": "Jonas Genath",
-    "label": "AI Developer and Data Scientist",
+    "label": "AI Developer, Data Scientist, and Data Visualization Expert",
     "image": "",
     "email": "genath.jonas@gmail.com",
     "url": "https://cbblr.github.io/",
@@ -235,25 +235,25 @@
   ],
   "projects": [
     {
-      "name": "DaWiS",
+      "name": "AutoPING",
       "summary": "Quantum computing is the use of quantum-mechanical phenomena such as superposition and entanglement to perform computation. Computers that perform quantum computations are known as quantum computers.",
       "highlights": [
         "Quantum Teleportation",
         "Quantum Cryptography"
       ],
-      "startDate": "2020-01-01",
-      "endDate": "2021-12-31",
+      "startDate": "2022-01-01",
+      "endDate": "2025-09-30",
       "url": "https://example.com"
     },
     {
-      "name": "AutoPING",
+      "name": "DaWiS",
       "summary": "Quantum computing is the use of quantum-mechanical phenomena such as superposition and entanglement to perform computation. Computers that perform quantum computations are known as quantum computers.",
       "highlights": [
         "Quantum Teleportation",
         "Quantum Cryptography"
       ],
-      "startDate": "2022-01-01",
-      "endDate": "2025-09-30",
+      "startDate": "2020-01-01",
+      "endDate": "2021-12-31",
       "url": "https://example.com"
     }
   ]
