Kontakt:

Telefon:	05121 / 883-40398
E-Mail:

Teaching:

• Computer Vision, Summer Term 2019
• Planning and Optimal Control, Winter Term 2018/2019
• Bayesian Networks, Summer Term 2018
• Machine Learning, Winter Term 2017/2018

• Shayan Jawed, Kiran Madhusudhanan, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme (2023):
  Forecasting Early with Meta Learning, in Proceedings of the 2023 IEEE International Joint Conference on Neural Networks (IEEE IJCNN 2023).
• Shayan Jawed, Lars Schmidt-Thieme (2022):
  GQFormer: A Multi-Quantile Generative Transformer for Time Series Forecasting, in Proceedings of the 2022 IEEE International Conference on Big Data (IEEE BigData 2022).
• Shayan Jawed, Lars Schmidt-Thieme (2022):
  FQFormer: A Fully Quantile Transformer for Time Series Forecasting, in 8th SIGKDD International Workshop on Mining and Learning from Time Series--Deep Forecasting: Models, Interpretability, and Applications.
• Shayan Jawed, Hadi S. Jomaa, Lars Schmidt-Thieme, Josif Grabocka (2021):
  Multi-task Learning Curve Forecasting Across Hyperparameter Configurations and Datasets, in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021).
• Shayan Jawed, Mofassir ul Islam Arif, Ahmed Rashed, Kiran Madhusudhanan, Shereen Elsayed, Mohsan Jameel, Alexei Volk, Andre Hintsches, Marlies Kornfeld, Katrin Lange, Lars Schmidt-Thieme (2022):
  AI and Data-Driven Mobility at Volkswagen Financial Services AG, in arXiv.
• Ahmed Rashed, Shayan Jawed, Lars Schmidt-Thieme (2020):
  MultiRec: A Multi-Relational Approach for Unique Item Recommendation in Auction Systems, in 14th ACM Recommender Systems Conference (RecSys 2020).
• Shayan Jawed, Josif Grabocka, Lars Schmidt-Thieme (2020):
  Self-Supervised Learning for Semi-Supervised Time Series Classification, in Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2020).
• Mohsan Jameel, Shayan Jawed, Lars Schmidt-Thieme (2020):
  Optimal Topology Search for Fast Model Averaging in Decentralized Parallel SGD, in Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2020).
• Shayan Jawed, Ahmed Rashed, Lars Schmidt-Thieme (2019):
  Multi-step Forecasting via Multi-task Learning, in Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData 2019) In The IEEE Big Data Conference 2019.
• Ahmed Rashed, Shayan Jawed, Jens Rehberg, Josif Grabocka, Lars Schmidt-Thieme, Andre Hintsches (2019):
  A Deep Multi-Task Approach for Residual Value Forecasting, in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2019).
• Shayan Jawed, Eya Boumaiza, Josif Grabocka, Lars Schmidt-Thieme (2018):
  Data-Driven Vehicle Trajectory Forecasting, in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Workshop on (KNOWMe@ECML PKDD 2018).