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Lars Kotthoff

Lars Kotthoff

Assistant Professor


EERB 422b
Department of Computer Science
University of Wyoming
Dept 3315, 1000 E University Ave
Laramie, WY 82071-2000

My research combines artificial intelligence and machine learning to build robust systems with state-of-the-art performance. I develop techniques to induce models of how algorithms for solving computationally difficult problems behave in practice. Such models allow to select the best algorithm and choose the best parameter configuration for solving a given problem. I lead the Meta-Algorithmics, Learning and Large-scale Empirical Testing (MALLET) lab and direct the Artificially Intelligent Manufacturing center (AIM) at the University of Wyoming.

More broadly, I am interested in innovative ways of modelling and solving challenging problems and applying such approaches to the real world. Part of this is making cutting edge research available to and usable by non-experts. Machine learning often plays a crucial role in this, and I am also working on making machine learning more accessible and easier to use.

Interested in coming to beautiful Wyoming and joining MALLET? Please drop me an email or, if you are already here, come by my office. I also have Master's projects and projects for undergraduates seeking research experience available.



For citation numbers, please see my Google Scholar page.


  • Kashgarani, Haniye, and Lars Kotthoff. “Is Algorithm Selection Worth It? Comparing Selecting Single Algorithms and Parallel Execution.” In AAAI Workshop on Meta-Learning and MetaDL Challenge, 140:58–64. Proceedings of Machine Learning Research. PMLR, 2021. preprint PDF bibTeX abstract

    For many practical problems, there is more than one algorithm or approach to solve them. Such algorithms often have complementary performance – where one fails, another performs well, and vice versa. Per-instance algorithm selection leverages this by employing portfolios of complementary algorithms to solve sets of difficult problems, choosing the most appropriate algorithm for each problem instance. However, this requires complex models to effect this selection and introduces overhead to compute the data needed for those models. On the other hand, even basic hardware is more than capable of running several algorithms in parallel. We investigate the tradeoff between selecting a single algorithm and running multiple in parallel and incurring a slowdown because of contention for shared resources. Our results indicate that algorithm selection is worth it, especially for large portfolios.
  • Bistarelli, Stefano, Lars Kotthoff, Francesco Santini, and Carlo Taticchi. “Summary Report for the Third International Competition on Computational Models of Argumentation.” AI Magazine In press. (2021). bibTeX

  • Binder, Martin, Florian Pfisterer, Michel Lang, Lennart Schneider, Lars Kotthoff, and Bernd Bischl. “mlr3pipelines - Flexible Machine Learning Pipelines in R.” Journal of Machine Learning Research 22, no. 184 (2021): 1–7. preprint PDF bibTeX abstract

    Recent years have seen a proliferation of ML frameworks. Such systems make ML accessible to non-experts, especially when combined with powerful parameter tuning and AutoML techniques. Modern, applied ML extends beyond direct learning on clean data, however, and needs an expressive language for the construction of complex ML workflows beyond simple pre- and post-processing. We present mlr3pipelines, an R framework which can be used to define linear and complex non-linear ML workflows as directed acyclic graphs. The framework is part of the mlr3 ecosystem, leveraging convenient resampling, benchmarking, and tuning components.
  • Kotthoff, Lars, Sourin Dey, Vivek Jain, Alexander Tyrrell, Hud Wahab, and Patrick Johnson. “Modeling and Optimizing Laser-Induced Graphene,” 2021. preprint PDF bibTeX abstract

    A lot of technological advances depend on next-generation materials, such as graphene, which enables a raft of new applications, for example better electronics. Manufacturing such materials is often difficult; in particular, producing graphene at scale is an open problem. We provide a series of datasets that describe the optimization of the production of laser-induced graphene, an established manufacturing method that has shown great promise. We pose three challenges based on the datasets we provide -- modeling the behavior of laser-induced graphene production with respect to parameters of the production process, transferring models and knowledge between different precursor materials, and optimizing the outcome of the transformation over the space of possible production parameters. We present illustrative results, along with the code used to generate them, as a starting point for interested users. The data we provide represents an important real-world application of machine learning; to the best of our knowledge, no similar datasets are available.
  • Kotthoff, Lars, Hud Wahab, and Patrick Johnson. “Bayesian Optimization in Materials Science: A Survey,” 2021. preprint PDF bibTeX abstract

    Bayesian optimization is used in many areas of AI for the optimization of black-box processes and has achieved impressive improvements of the state of the art for a lot of applications. It intelligently explores large and complex design spaces while minimizing the number of evaluations of the expensive underlying process to be optimized. Materials science considers the problem of optimizing materials' properties given a large design space that defines how to synthesize or process them, with evaluations requiring expensive experiments or simulations -- a very similar setting. While Bayesian optimization is also a popular approach to tackle such problems, there is almost no overlap between the two communities that are investigating the same concepts. We present a survey of Bayesian optimization approaches in materials science to increase cross-fertilization and avoid duplication of work. We highlight common challenges and opportunities for joint research efforts.
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  • Maintainer of the FSelector R package.

  • Author and maintainer of LLAMA, an R package to simplify common algorithm selection tasks such as training a classifier as portfolio selector.

  • Core contributor to the mlr R package (Github) for all things machine learning in R.

  • Leading the Auto-WEKA project, which brings automated machine learning to WEKA.


  • I am teaching COSC 3020 (Algorithms and Data Structures) this semester. Lecture materials, assignments, announcements, etc. are available on WyoCourses.
  • I am teaching a practical machine learning course using mlr. The slides are available here.
  • If you are interested in the AI reading group, check out the list of proposed papers here.



Apart from my main affiliation, I am a research associate with the Maya Research Program. If I'm not in the office, it's possible that you can find me in the jungle of Belize excavating and/or mapping Maya ruins. Check out the interactive map.

I am also involved with the OpenML project project and a core contributor to ASlib, the benchmark library for algorithm selection.

While you're here, have a look at my overview of the Algorithm Selection literature. For something more visual, have a look at my pictures on Flickr.