InstitutTeam
Difan Deng


Difan Deng, M. Sc.

Difan Deng, M. Sc.
Adresse
Appelstraße 9a
30167 Hannover
Gebäude
Raum
Difan Deng, M. Sc.
Adresse
Appelstraße 9a
30167 Hannover
Gebäude
Raum

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, PhD 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

    • Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer (2022): Efficient Automated Deep Learning for Time Series ForecastingProceedings of the European Conference on Machine Learning (ECML)
      arXiv: 2205.05511
    • Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, Frank Hutter (2022): SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter OptimizationJournal of Machine Learning Research (JMLR) -- MLOSS, Vol. 23, No. 54, pp. 1-9 Weitere Informationen
    • Difan Deng, Marius Lindauer (2021): Searching in the Forest for Local Bayesian Optimizatio
      arXiv: 2111.05834
    • Bernd Bischl, Martin Binder, Michel Lang, Tobias Pielok, Jakob Richter, Stefan Coors, Janek Thomas, Theresa Ullmann, Marc Becker, Anne-Laure Boulesteix, Difan Deng, Marius Lindauer (2021): Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges
      arXiv: 2107.05847
    • Sergio Izquierdo, Julia Guerrero-Viu, Sven Hauns, Guilherme Miotto, Simon Schrodi, André Biedenkapp, Thomas Elsken, Difan Deng, Marius Lindauer, Frank Hutter (2021): Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter OptimizationICML 2021 Workshop AutoML
      arXiv: 2105.01015
    • Noor Awad, Gresa Shala, Difan Deng, Neeratyoy Mallik, Matthias Feurer, Katharina Eggensperger, Andre' Biedenkapp, Diederick Vermetten, Hao Wang, Carola Doerr, Marius Lindauer, Frank Hutter (2020): Squirrel: A Switching Hyperparameter Optimize
      arXiv: 2012.08180

    Projects

    • Dynamic Algorithm Configuration
      Da die Konfigurationen während der Laufzeit in Abhängigkeit vom aktuellen Zustand des Algorithmus ausgewählt werden sollten, kann es als ein Problem des Reinforcement Learning (RL) betrachtet werden, bei dem ein Agent in jedem Zeitschritt die zu verwendende Konfiguration auf der Grundlage der Leistung im letzten Schritt und des aktuellen Zustands des Algorithmus auswählt. Dies ermöglicht uns einerseits den Einsatz leistungsfähiger RL-Methoden, andererseits bringt RL auch eine Reihe von Herausforderungen mit sich, wie Instabilität, Rauschen und Ineffizienz bei der Abtastung, die bei Anwendungen wie DAC angegangen werden müssen. Daher umfasst die Forschung zu DAC auch die Forschung zu zuverlässigem, interpretierbarem, allgemeinem und schnellem Reinforcement Learning.
      Leitung: Prof. Dr. Marius Lindauer
      Jahr: 2019
      Förderung: DFG
      Laufzeit: 2019-2023
    • Dynamic Algorithm Configuration
      As configurations should be chosen during runtime depending on the current algorithm state, it can be viewed as a reinforcement learning (RL) problem where at each timestep an agent selects the configuration to use based on the performance in the last step and the current state of the algorithm. This enables us to use powerful RL methods on one hand; on the other, RL also brings a set of challenges like instability, noise and sample inefficiency that need to be addressed in applications such as DAC. Therefore research on DAC also includes research on reliable, interpretable, general and fast reinforcement learning.
      Leitung: Prof. Dr. Marius Lindauer
      Jahr: 2019
      Förderung: DFG
      Laufzeit: 2019-2023
    • CoyPu: Cognitive Economy Intelligence Plattform für die Resilienz wirtschaftlicher Ökosysteme
      Naturkatastrophen, Pandemien, Finanzkrisen, politische Krisen und Angebotsknappeheiten oder Nachfrageschocks propagieren sich durch offensichtliche und latente Handelsbeziehungen durch das globale ökonomische System. Dies ist eine Konsequenz der kontinuierlichen Globalisierung mit der einhergehenden Arbeitsteilung. Ziel dieses Projektes ist es diese Verbindungen offenzulegen und kaskadierende Risiken vorherzusagen um damit Unternehmen die Möglichkeit einzuräumen vorausschauend agieren zu können.
      Leitung: Prof. Marius Lindauer and Prof. Maria Esther-Vidal (L3S/LUH)
      Team: InfAI, DATEV eg., eccenca GmbH, Implisense GmbH, Deutsches Institut für Wirtschaftsforschung, Leibniz Informationszentrum Technik und Naturwissenschaften, Hamburger Informatik Technologie-Center e.V., Selbstregulierung Informationswirtschaft e.V., Infineo
      Jahr: 2021
      Förderung: Innovationswettbewerb Künstliche Intelligenz (BMWK)
      Laufzeit: 2021-2024
    • Leibniz AI Academy
      The Leibniz AI Academy aims to develop and establish a trans-curricular and interdisciplinary micro-degree program at the Leibniz Universität Hannover (LUH), in which students from different courses of study acquire competencies in the field of Artificial Intelligence
      Leitung: Prof. Dr. Marius Lindauer, Prof. Dr. Ralph Ewert, Prof. Dr. Johannes Krugel
      Jahr: 2021
      Förderung: Bundesministerium für Bildung und Forschung (BMBF)
      Laufzeit: 2021 - 2024
      Logo of Leibniz AI academy Logo of Leibniz AI academy
    • ERC Starting Grant: Interactive and Explainable Human-Centered AutoML
      Trust and interactivity are key factors in the future development and use of automated machine learning (AutoML), supporting developers and researchers in determining powerful task-specific machine learning pipelines, including pre-processing, predictive algorithm, their hyperparameters and--if applicable--the architecture design of deep neural networks. Although AutoML is ready for its prime time after it achieved impressive results in several machine learning (ML) applications and its efficiency improved by several orders of magnitudes in recent years, democratization of machine learning via AutoML is still not achieved. In contrast to previously purely automation-centered approaches, ixAutoML is designed with human users at its heart in several stages. First of all, the foundation of trustful use of AutoML will be based on explanations of its results and processes. Therefore, we aim for: (i) Explaining static effects of design decisions in ML pipelines optimized by state-of-the-art AutoML systems. (ii) Explaining dynamic AutoML policies for temporal aspects of dynamically adapted hyperparameters while ML models are trained. These explanations will be the base for allowing interactions, bringing the best of two worlds together: human intuition and generalization capabilities for complex systems, and efficiency of systematic optimization approaches for AutoML. Concretely, we aim for: (iii) Enabling interactions between humans and AutoML by taking human's latent knowledge into account and learning when to interact. (iv) Building first ixAutoML prototypes and showing its efficiency in the context of Industry 4.0. Perfectly aligned with the EU's AI strategy and recent efforts on interpretability in the ML community, we strongly believe that this timely human-centered ixAutoML will have a substantial impact on the democratization of machine learning.
      Leitung: Prof. Dr. Marius Lindauer
      Team: AutoML
      Jahr: 2022
      Förderung: EU
      Laufzeit: 2022-2027
    • KISSKI: AI Service Center
      The central approach for the KISSKI project is the research on AI methods and their provision with the goal of enabling a highly available AI service center for critical and sensitive infrastructures with a focus on the fields of medicine and energy. Due to their relevance to society as a whole, medicine and the energy industry are among the future fields of application-oriented AI research in Germany. Beyond the technological developments, artificial intelligence (AI) has the potential to make a significant contribution to social progress. This is particularly true in areas where digitization processes are increasingly gaining ground and complexity is high. For both medicine and the energy industry, the pressure to innovate, but also the potential, is immense due to the availability of more and more distributed information based on a multitude of new sensors and actuators. The increasing complexity of the tasks as well as the availability of very large data sets offer a high potential for the application of AI methods in both topics.
      Leitung: Prof. Dr. Marius Lindauer
      Team: AutoML
      Jahr: 2022
      Förderung: BMBF
      Laufzeit: 2022-2025