Maximilian Li

B.Sc. Maximilian Li

  • Karlsruhe Institute of Technology (KIT)
    Institute of Information Systems and Marketing (IISM)
    Kaiserstraße 89
    76133 Karlsruhe

Research Interests

  • Artificial Intelligence & Machine Learning
  • Physiological Computing & Physiolytics

Short CV

  • Since 2020: Associated Researcher at the Institute of Information Systems and Marketing, Karlsruhe Institute of Technology.
  • Since 2018: Master of Science Student in Computer Science at the Karlsruhe Institute of Technology
  • 2018-2020: Junior Researcher at the Institute of Information Systems and Marketing, Karlsruhe Institute of Technology.
  • 2017-2018: International Study Year at Tongj University, Shanghai (China)
  • 2013-2018: Bachelor of Science in Computer Science at the Technical University Darmstadt

Publications


Conference Papers
  1. Flow in knowledge work groups - Autonomy as a driver or digitally mediated communication as a limiting factor?
    Knierim, M. T.; Nadj, M.; Li, M. X.; Weinhardt, C.
    2020. 40th International Conference on Information Systems (ICIS 2019), München, 15.-18. Dezember 2019, AIS eLibrary (AISeL)
  2. Flow in Knowledge Work Groups – Autonomy as a Driver or Digitally Mediated Communication as a Limiting Factor?
    Knierim, M. T.; Nadj, M.; Li, M. X.; Weinhardt, C.
    2019. ICIS 2019, Munich, Germany, December 15-18, 2019, 1–17, AIS eLibrary (AISeL)
  3. Got Flow? Using Machine Learning on Physiological Data to Classify Flow
    Rissler, R.; Nadj, M.; Li, M. X.; Knierim, M. T.; Maedche, A.
    2018. Proceedings of the Conference on Human Factors in Computing Systems (CHI), Montréal, Canada, 21st - 26th April 2018, Art.Nr. LBW612, Association for Computing Machinery (ACM). doi:10.1145/3170427.3188480
Journal Articles
  1. What Disrupts Flow in Office Work? The Impact of Frequency and Relevance of IT-Mediated Interruptions
    Nadj, M.; Rissler, R.; Adam, M. T. P.; Knierim, M. T.; Li, M. X.; Maedche, A.; Riedl, R.
    2023. MIS quarterly, 47 (4), 1615–1646
  2. Towards a Physiological Computing Infrastructure for Researching Students’ Flow in Remote Learning – Preliminary Results from a Field Study
    Li, M. X.; Nadj, M.; Maedche, A.; Ifenthaler, D.; Wöhler, J.
    2022. Technology, knowledge and learning, 27, 365–384. doi:10.1007/s10758-021-09569-4
  3. To Be or Not to Be in Flow at Work: Physiological Classification of Flow using Machine Learning
    Rissler, R.; Nadj, M.; Li, M. X.; Loewe, N.; Knierim, M. T.; Maedche, A.
    2020. IEEE transactions on affective computing, 14 (1), 463–474. doi:10.1109/TAFFC.2020.3045269
  4. Power to the Oracle? Design Principles for Interactive Labeling Systems in Machine Learning
    Nadj, M.; Knaeble, M.; Li, M. X.; Maedche, A.
    2020. Künstliche Intelligenz, 34, 131–142. doi:10.1007/s13218-020-00634-1