About

Maximilian Li joined as a PhD researcher at the Intuitive Robots Lab (IRL) headed by Prof. Rudolf Lioutikov in April 2022, after he graduated as M.Sc. in Computer Science at the Karlsruhe Institute of Technology (KIT). Maximilian's research interest include Explainable AI (XAI) and Computer Vision for Human-Robot-Interaction (HRI).

Publications

2024

Information Maximizing Curriculum: A Curriculum-Based Approach for Learning Versatile Skills
Blessing, D.; Celik, O.; Jia, X.; Reuss, M.; Li, M.; Lioutikov, R.; Neumann, G.
2024. Advances in Neural Information Processing Systems. Ed.: A. Oh, MIT-Press

2023

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

Goal-Conditioned Imitation Learning using Score-based Diffusion Policies
Reuss, M.; Li, M.; Jia, X.; Lioutikov, R.
2023. arxiv. doi:10.48550/arXiv.2304.02532

Information Maximizing Curriculum: A Curriculum-Based Approach for Training Mixtures of Experts
Blessing, D.; Celik, O.; Jia, X.; Reuss, M.; Li, M. X.; Lioutikov, R.; Neumann, G.
2023. arxiv. doi:10.48550/arXiv.2303.15349

Curriculum-Based Imitation of Versatile Skills
Li, M. X.; Celik, O.; Becker, P.; Blessing, D.; Lioutikov, R.; Neumann, G.
2023. arxiv. doi:10.48550/arXiv.2304.05171

2022

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

2020

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

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

2019

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)

2018

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