Tim Ruhkopf
Tim Ruhkopf
Appelstraße 9a
30167 Hannover
Tim Ruhkopf
Appelstraße 9a
30167 Hannover

I received my M.Sc. in Applied Statistics and B.Sc. in Economics from the University of Göttingen. In my studies I focused on Machine & Deep Learning, (Bayesian) Generalized Linear Regression methods and Econometrics respectively. My thesis concerned itself with extracting main effects from Bayesian Neural Networks using grouped shrinkage priors and splines; inferring its parameters using Stochastic Gradient Markov Chain Monte Carlo methods. 

Since Sep. 2021, I am pursuing my PhD as a member of Prof. Lindauer’s group. My current research interests are Bayesian- & multi-fidelity optimization and meta-learning, aiming at boosting the performance of machine learning algorithms by choosing appropriate hyperparameters in a data driven, principled and efficient manner. Most recently we investigated, how to combine multi-fidelity and meta-learning for algorithm selection using a transformer architecture. Currently I am involved in finding a way of training reinforcement learning agents more robustly as well as training graph-based models more efficiently using a novel fidelity type.

Research Interests

  • Bayesian Optimization
  • Multi-Fidelity for Hyperparameter Optimization
  • Multi-Fidelity for Graph Neural Networks
  • Meta-Learning for Hyperparameter Optimization
  • Reinforcement Learning for Algorithm Selection on partial learning curves 
  • Hyperparameter Optimization for Reinforcement learning

Curriculum Vitae

  • Work Experience

    2021-today Doctoral Researcher Leibniz University Hannover

    2021 (6 Monate) Student Assistant, Georg-August University Göttingen

    2019 (6 Monate) Internship: Data Architecture and Smart Analytics, Deutsche Bank AG

    2017-2018 (18 Monate) Student Assistant, Georg-August University Göttingen

  • Education

    Since 2021 Ph.D. Student in AutoML, Leibniz University Hannover

    2017-2020 M.Sc Applied Statistics, Georg-August University Göttingen

    2014-2017 B.Sc. Economics, Georg-August University Göttingen



Mohan, A., Ruhkopf, T., & Lindauer, M. (2022). Towards Meta-learned Algorithm Selection using Implicit Fidelity Information. in ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML)

Ruhkopf, T., Mohan, A., Deng, D., Tornede, A., Hutter, F., & Lindauer, M. (2022). MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information.


  • CoyPu: Cognitive Economy Intelligence Platform for the Resillience of Economic Ecosystems
    Natural disasters, pandemics, financial- & political crises, supply shortages or demand shocks propagate through hidden and intermediate linkages across the global economic system. This is a consequence of the continuous international division of business and labor which is at the heart of globalisation. The aim of the project is to provide a platform that expounds the complex supply chains and reveal the linkages, compounded risks and provide companies with predictions regarding their exposure in various granularities.
    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