30167 Hannover
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
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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].
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.
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)
Deng, D., & Lindauer, M. (2022). Searching in the Forest for Local Bayesian Optimization. In ECML/PKDD workshop on Meta-learning
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.
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
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.
