Leibniz Universität Hannover Faculty Faculty of Electrical Engineering and Computer Science
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Institute of Artificial Intelligence
 
InstituteStaff
Difan Deng
Difan Deng
Phone
+49 511 762 19633
Email
d.deng@ai.uni-hannover.de
Address
Welfengarten 1
30167 Hannover
Building
1101
Room
a446
Difan Deng
Phone
+49 511 762 19633
Email
d.deng@ai.uni-hannover.de
Address
Welfengarten 1
30167 Hannover
Building
1101
Room
a446

I am working towards a Ph.D. at Leibniz University Hannover. Previously I obtained my Master's degree in electrical engineering and information technology at TU Darmstadt and my bachelor's degree in electronics and information engineering at Huazhong University of Science in 2019 and 2015 respectively. 

My research interest is AutoML, including Hyperparameter Optimization and Neural Architecture Search. The goal is to provide easy-to-use AutoML systems that allow non-expert Machine learning users to work with machine learning problems at hand.

Research Interests

  • Hyperparameter Optimization
  • Neural Architecture
  • Time Series forecasting

    Curriculum Vitae

    • Education

      Since 2020, Ph.D. Candidate, University Hannover, Germany

      2016-2019, Master of Science, Electrical Engineering and Information Technology , TU Darmstadt, Germany

      2011-2015, Bachelor of Engineering, Electronic and Information Engineering, Huazhong University of Science and Technology

    Publications

    2023

    Bischl, B., Binder, M., Lang, M., Pielok, T., Richter, J., Coors, S., Thomas, J., Ullmann, T., Becker, M., Boulesteix, A-L., Deng, D., & Lindauer, M. (2023). Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13(2), [e1484]. https://doi.org/10.1002/widm.1484
    More...
    Ruhkopf, T., Mohan, A., Deng, D., Tornede, A., Hutter, F., & Lindauer, M. (2023). MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information. Transactions on Machine Learning Research. https://openreview.net/forum?id=5aYGXxByI6
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    Tornede, A., Deng, D., Eimer, T., Giovanelli, J., Mohan, A., Ruhkopf, T., Segel, S., Theodorakopoulos, D., Tornede, T., Wachsmuth, H., & Lindauer, M. (2023). AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks. https://doi.org/10.48550/arXiv.2306.08107
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    2022

    Deng, D., Karl, F., Hutter, F., Bischl, B., & Lindauer, M. (2022). Efficient Automated Deep Learning for Time Series Forecasting. In Proceedings of the European Conference on Machine Learning (ECML) https://doi.org/10.48550/arXiv.2205.05511
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    Deng, D., & Lindauer, M. (2022). Searching in the Forest for Local Bayesian Optimization. In ECML/PKDD workshop on Meta-learning https://arxiv.org/abs/2111.05834
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    Lindauer, M., Eggensperger, K., Feurer, M., Biedenkapp, A., Deng, D., Benjamins, C., Sass, R., & Hutter, F. (2022). SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. Journal of Machine Learning Research, 2022(23). https://arxiv.org/abs/2109.09831
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    2021

    Guerrero-Viu, J., Hauns, S., Izquierdo, S., Miotto, G., Schrodi, S., Biedenkapp, A., Elsken, T., Deng, D., Lindauer, M., & Hutter, F. (2021). Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization. In ICML 2021 Workshop AutoML https://arxiv.org/abs/2105.01015
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    2020

    Awad, N., Shala, G., Deng, D., Mallik, N., Feurer, M., Eggensperger, K., Biedenkapp, A., Vermetten, D., Wang, H., Doerr, C., Lindauer, M., & Hutter, F. (2020). Squirrel: A Switching Hyperparameter Optimizer. https://arxiv.org/abs/2012.08180
    More...

    Last Change: 19.09.23 Print

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