Julian Vexler, M.Sc.

Johannes Gutenberg University Mainz
Institut für Informatik
Staudingerweg 9
55128 Mainz, Germany

E-Mail:
Room: 03-627
Office phone: +49-6131-39-23336

 

Research Interests

  • Time series data mining
  • Online data streams
  • 3D Object classification
  • Causal inference
  • Deep neural networks

 

Activities

(Co-)Reviewing:

  • IEEE International Conference on Data Mining (ICDM 2023)
  • IEEE International Conference on Data Mining (ICDM 2021)
  • ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2020)
  • ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2018)
  • International Joint Conferences on Artificial Intelligence (IJCAI 2018)
  • European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2018)
  • IEEE International Conference on Big Knowledge (ICBK 2018)

 

Short Scientific CV

since 01/2018        Research associate at Johannes Gutenberg University in Mainz
08/2015 - 01/2016 Semester abroad at Dalarna University in Borlänge, Sweden
04/2015 - 10/2017 Computer Science (M.Sc.) at Johannes Gutenberg University in Mainz
10/2011 - 03/2015 Mathematics (B.Sc.) at Johannes Gutenberg University in Mainz

 

Teaching

  • Data Mining Seminar at Johannes Gutenberg University in Mainz
  • Machine Learning Seminar at Johannes Gutenberg University in Mainz

 

Publications

Vexler, Julian, and Stefan Kramer. "Classifying Aircraft Categories from Magnetometry Data Using a Hypotheses-based Multi-Task Framework." In the 12th International Conference on Prestigious Applications of Intelligent Systems, PAIS 2023, October 3rd, Krakow, Poland. TBA

Vexler, Julian, and Stefan Kramer, "Identifying Aircraft Motions and Patterns from Magnetometry Data Using a Knowledge-Based Multi-Fusion Approach," 2023 26th International Conference on Information Fusion (FUSION), Charleston, SC, USA, 2023, pp. 1-8, doi: 10.23919/FUSION52260.2023.10224226.

Garcon, Antoine, Julian Vexler, Dmitry Budker, and Stefan Kramer. "Deep neural networks to recover unknown physical parameters from oscillating time series." Plos one 17, no. 5 (2022): e0268439.

Vexler, Julian, and Stefan Kramer. "Integrating LSTMs with online density estimation for the probabilistic forecast of energy consumption." In Discovery Science: 22nd International Conference, DS 2019, Split, Croatia, October 28–30, 2019, Proceedings 22, pp. 533-543. Springer International Publishing, 2019.