About me
News
- 2025-09-03: Runner-up Best Student Paper Award for our paper "BEST: Bilaterally Expanding Subtrace Tree for Event Sequence Prediction" at International Conference on Business Process Management 2025
- 2025-08-18: Paper accept "SHAining on Process Mining: Explaining Event Log Characteristics Impact on Algorithms" at ICPM 2025
- 2025-05-08: Paper accept "BEST: Bilaterally Expanding Subtrace Tree for Event Sequence Prediction" at BPM 2025
- 2025-04-01: Teaching assistant for "Deep Learning (Basic Module)" - S25
- 2025-02-14: New arXiv paper: "Is Deep Learning finally better than Decision Trees on Tabular Data?" [Link]
- 2025-10-01: Teaching assistant for "Large Language Models (Advanced Module) - W24/25"
- 2024-09-18: Paper accept "iGEDI: interactive Generating Event Data with Intentional Features (Extended Abstract)" at ICPM Demos Track 2024
- 2024-09-01: New PostDoc Position: Joined Machine Learning Lab at University of Technology Nuremberg (UTN)
Short Bio
Dr. Christian M. M. Frey studied (Media) Informatics at the Ludwig-Maximilian University. Afterward, he worked as a research assistant in the field of network analysis and graph theory at the Ludwig-Maximilian University of Munich and Christian-Albrecht University of Kiel, Germany. His dissertation is titled ‘Learning from Complex Networks’ and he received his Ph.D. degree (Dr.rer.nat.) in Informatics in June 2023. He then worked in the Department Analytics in the group of Supply Chain Management at the Fraunhofer IIS, Nuremberg, as Senior Scientist, where he led the research projects from a strategic and conceptional view, and supervised a PhD student in collaboration with the LMU. In winter term 2023/2024, he also worked as a lecturer for his “Data Science in Supply Chain Management” lecture at the Otto-Friedrich University of Bamberg.
Currently, he works as an academic advisor (dt., Akademischer Rat) in the Machine Learning Lab at the University of Technology of Nuremberg (UTN), Germany. In his current position, his research focuses on foundation models, tabular data, reinforcement learning and optimization techniques.
Research
My primary interests in research are [updated Aug-2025]:
- Foundation Models
- Large Language Models
- Learning on Tabular Data
- Neural Combinatorial Optimization
- Data-efficient Learning
International research stays and cooperations
- 04/24/2017 - 05/13/2017: Research stay at the George Mason University, Fairfax at the Department of Computational and Data Sciences (CDS)
- 09/22/2016 - 12/17/2016: Research stay at the University of California, Berkeley at the Berkeley Institute for Data Science (BIDS)
- 07/29/2016 - 08/10/2016: Research stay at the Hong Kong University of Science and Technology and University of Hong Kong
- 09/10/2015 - 09/25/2015: Research stay at the University of California, Berkeley at the Berkeley Institute for Data Science (BIDS)
Misc.
Peer reviewed for conferences in …
- 2015: CIKM'15, SIGSPATIAL'15
- 2016: SIGSPATIAL'16, KDD'16, SSDBM'16
- 2017: ICDE'17, DASFAA'17, KDD'17, VLDB'17
- 2018: ICDE'18, DASFAA'18, CIKM'18
- 2019: VLDB'19
- 2020: SDM'20, DAMI'20
- 2021: ICDM'21, ECMLPKDD'21
- 2023: SIGMOD'23, EJOR'23
- 2024: ECML PKDD'24 [PC Member], EJOR'24
- 2025: ECML PKDD'25 [PC Member], LOG'25
Supervised Theses:
- MT - "Attention-driven Learning of Temporal Abstractions in deep Reinforcement Learning"
- MT - "Relation Disambiguation in Knowledge Graphs"
- MT - "Application of Energy Consumption Forecasts using Deep Learning"
- MT - "Analysis Framework for spatio-temporal soccer data"
[wip: work in progress]