Tim Ruhkopf

Tim Ruhkopf, M. Sc.

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. My distinct objects of study are Knowledge Graphs and Graph Neural Networks. Recently, I am interested reinforcement learning for algorithm selection.

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

  • Working Experience

    2021 (6 Months) Research Assistant, Georg-August Universität Göttingen

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

    2017-2018 (18 Months) Research Assistant, Georg-August Universität Göttingen

  • Education

    Since 2021 PhD candidate in AutoML, Leibniz University Hannover

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

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


  • 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.
    Led by: 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
    Year: 2021
    Funding: Innovationswettbewerb Künstliche Intelligenz (BMWK)
    Duration: 2021-2024