Dr.-Ing. Christopher Krauß

Media & Data Science Lead at the Business Unit FAME

Application Areas

  • Interoperable & Adaptive Educational Infrastructures
  • State of the Art Media Delivery for Cross Device Applications
  • Secure Verifiability of Media Production, Modification and Usage Processes

Research Areas

  • Artificial Intelligence & Machine Learning for Media
    • Personalization & Support through Adaptive Systems & Recommender Systems
    • Data Analysis through Pattern Recognition & Predictive Data Mining
    • Supervised, Unsupervised & Reinforcement Learning Approaches based on Deep Learning
  • Sustainable Interoperable Infrastructures
    • Design of Micro-Service Architectures to support the Best-of-Breed Paradigm
    • Analysis and Support of Open Standards & Specifications
    • Translation of Business Requirements into Scientific Technical Solutions
  • Technical Innovations for Societal Challenges
    • Secure Verification of Authenticity and Provenance for Media and Physical Objects
    • Broad Acceptance through Explainable AI & Trustworthy AI
    • User Self-Sovereign Identities and Data Handling


Dr.-Ing. Christopher Krauss is Media & Data Science Lead at the business unit Future Applications and Media of the Fraunhofer Institute for Open Communication Systems (FOKUS). He specializes in the R&D of topics dealing with Machine Learning, Data Science, Technology Enhanced Learning, and Connected TVs and has been involved in multiple public founded national and international projects (e.g. Smart Learning I & II, Digi-Hand, FI-Content I & II, User Centric Networking, Global ITV) and managed many projects for different industry customers (e.g., Red Bull, Deutsche Welle, MediaBroadcast, ImmobilienScout24, Holtzbrinck Publishing Group, Cornelsen, Bertelsmann Arvato, Axel Springer, Rovi, and Deutsche Telekom).

Christopher Krauss received his Master of Science in Media Informatics (very good with distinction) at Beuth University of Applied Sciences and Fraunhofer FOKUS in 2012. From 2015 until 2017, he participated in the BMBF-funded Software Campus program which trains and professionally develops tomorrow’s senior IT executives. In June 2018, Christopher Krauss completed his PhD project (with distinction/ summa cum laude) at the TU Berlin with his dissertation “Time-Dependent Recommender Systems for the Prediction of Appropriate Learning Objects”.

Publications

Jahr
Year
Titel/Autor:in
Title/Author
Publikationstyp
Publication Type
2022 Lessons learned from creating, implementing and evaluating assisted e-learning incorporating adaptivity, recommendations and learning analytics
Altun, Daniela; Krauß, Christopher; Streicher, Alexander; Müller, Christoph; Atorf, Daniel; Rerhaye, Lisa; Kunde, Dietmar
Konferenzbeitrag
Conference Paper
2021 Ansätze für eine Nutzungserfassung von Video-Streaming-Angeboten
Arbanowski, Stefan; Seeliger, Robert; Krauß, Christopher; Lasak, Martin
Bericht
Report
2021 Evaluation Methods for an AI-Supported Learning Management System: Quantifying and Qualifying Added Values for Teaching and Learning
Rerhaye, Lisa; Altun, Daniela; Krauss, Christopher; Müller, Christoph
Konferenzbeitrag
Conference Paper
2020 Interoperable education infrastructures: A middleware that brings together adaptive, social and virtual learning technologies
Krauss, Christopher; Hauswirth, Manfred
Zeitschriftenaufsatz
Journal Article
2019 Smart Learning Object Recommendations based on Time-Dependent Learning Need Models
Krauss, Christopher; Merceron, Agathe; Arbanowski, Stefan
Konferenzbeitrag
Conference Paper
2019 The timeliness deviation: A novel approach to evaluate educational recommender systems for closed-courses
Krauss, Christopher; Merceron, Agathe; Arbanowski, Stefan
Konferenzbeitrag
Conference Paper
2018 On the emergence of typical behaviours in LMS
An, Truong-Sinh; Krauss, Christopher; Merceron, Agathe
Konferenzbeitrag
Conference Paper
2018 Branched learning paths for the recommendation of personalized sequences of course items
Krauss, Christopher; Salzmann, Andreas; Merceron, Agathe
Konferenzbeitrag
Conference Paper
2018 Time-dependent recommender system for the prediction of appropriate learning objects
Krauß, Christopher
Dissertation
Doctoral Thesis
2018 Können typische Lernverhalten, wie sie in MOOCs gefunden wurden, auch in anderen Kursen auftauchen?
An, Truong-Sinh; Merceron, Agathe; Krauß, Christopher
Konferenzbeitrag
Conference Paper
2017 "Smart Learning". Ein digitaler Lernbegleiter für die berufliche Bildung
Krauß, Christopher; Merceron, Agathe; An, Truong-Sinh; Zwicklbauer, Miggi; Dinziol, Martin; Scharp, Michael
Konferenzbeitrag
Conference Paper
2017 Smart Learning - neue Ansätze für individuelles Lernen
Krauß, Christopher; Schlösser, Holger
Zeitschriftenaufsatz
Journal Article
2017 Can typical behaviors identified in MOOCs be discovered in other courses?
An, Truong-Sinh; Krauss, Christopher; Merceron, Agathe
Konferenzbeitrag
Conference Paper
2017 Teaching advanced web technologies with a mobile learning companion application
Krauss, Christopher; Merceron, Agathe; An, Truong-Sinh; Zwicklbauer, Miggi; Steglich, Stephan; Arbanowski, Stefan
Konferenzbeitrag
Conference Paper
2016 Smart learning: Time-dependent context-aware learning object recommendations
Krauss, Christopher
Konferenzbeitrag
Conference Paper
2016 Mobiles Lernen in der beruflichen Bildung mit dem digitalen Lernbegleiter
Zwicklbauer, Miggi; Krauss, Christopher; An, Truong-Sinh; Merceron, Agathe; Kania, Jost-Peter; Scharp, Michael
Vortrag
Presentation
2016 Supervised speech act classification of messages in german online discussions
Bayat, Berken; Krauss, Christopher; Merceron, Agathe; Arbanowski, Stefan
Konferenzbeitrag
Conference Paper
2016 Personalized dynamic ad insertion with MPEG DASH
Pham, Stefan; Krauss, Christopher; Silhavy, Daniel; Arbanowski, Stefan
Konferenzbeitrag
Conference Paper
2016 The smart learning approach - a mobile learning companion application
Krauss, Christopher; Merceron, Agathe; An, Truong-Sinh; Zwicklbauer, Miggi; Arbanowski, Stefan
Konferenzbeitrag
Conference Paper
2015 Towards personalized smart city guide services in future internet environments
Seeliger, Robert; Krauss, Christopher; Wilson, Annette; Zwicklbauer, Miggi; Arbanowski, Stefan
Konferenzbeitrag
Conference Paper
2015 "Smart Learning" für die handwerkliche Weiterbildung
Merceron, Agathe; Kania, Jost; Krauß, Christopher; Scharp, Michael; Zwicklbauer, Miggi
Konferenzbeitrag
Conference Paper
2015 Towards time-dependent context-sensitive user data for recommending learning objects
Krauss, Christopher; Chandru, Rakesh; Arbanowski, Stefan
Poster
2015 Towards personalized smart city guide services in future internet environments
Seeliger, Robert; Krauss, Christopher; Wilson, Annette; Zwicklbauer, Miggi; Arbanowski, Stefan
Konferenzbeitrag
Conference Paper
2015 Smart Learning: Der digitale Lernbegleiter für die berufliche Bildung
Zwicklbauer, Miggi; Krauss, Christopher; Merceron, Agathe; Kania, Jost-Peter; Scharp, Michael
Konferenzbeitrag
Conference Paper
2014 Challenges for enabling targeted multi-screen advertisement for interactive TV services
Krauss, Christopher; Bassbouss, Louay; Pham, Stefan; Kaiser, Stefan; Arbanowski, Stefan; Steglich, Stephan
Konferenzbeitrag
Conference Paper
2014 Social preference ontologies for enriching user and item data in recommendation systems
Krauss, Christopher; Arbanowski, Stefan
Konferenzbeitrag
Conference Paper
2014 Enriched personalized multi-screen content for social connected TV
Krauss, Christopher; Seeliger, Robert; Wilson, Annette; Arbanowski, Stefan
Konferenzbeitrag
Conference Paper
2014 Preference ontologies based on Social Media for compensating the Cold Start Problem
Krauss, Christopher; Braun, Sascha; Arbanowski, Stefan
Konferenzbeitrag
Conference Paper
2013 Internet-delivered television using MPEG-DASH. Opportunities and challenges
George, Lars; Pham, Stefan; Kaiser, Stefan; Krauss, Christopher; Arbanowski, Stefan
Konferenzbeitrag
Conference Paper
2013 TV predictor: Personalized program recommendations to be displayed on SmartTVs
Krauss, Christopher; George, Lars; Arbanowski, Stefan
Konferenzbeitrag
Conference Paper
Diese Liste ist ein Auszug aus der Publikationsplattform Fraunhofer-Publica

This list has been generated from the publication platform Fraunhofer-Publica