I am an assistant professor at the Department of Computer Science of the University of Helsinki, where I lead the Machine and Human Intelligence research group. I am also a member of the International Brain Laboratory and an off-site visiting scholar at New York University. My research spans probabilistic machine learning and computational and cognitive neuroscience.

Previously, I was a postdoc in Alex Pouget‘s lab in Geneva, Switzerland, and with Wei Ji Ma at NYU. I obtained my PhD at the Doctoral Training Centre for computational neuroscience based in Edinburgh, UK, working with Sethu Vijayakumar and Daniel Wolpert. I spent several months of my PhD at the Computational and Biological Learning Lab in Cambridge, UK.


Email: luigi.acerbi AT helsinki.fi
Twitter: @AcerbiLuigi
CrossValidated: lacerbi
GitHub: lacerbi

About the group

The Machine and Human Intelligence research group explores the mechanisms and processes at the basis of intelligent behavior in artificial and biological systems. We study brains and computers alike as statistical inference engines which are probabilistic, approximate, active, robust, and resource-constrained. We apply machine learning techniques to model human behavior, and take insights from problems in computational and cognitive neuroscience to develop new algorithms.

Our work in probabilistic machine learning has focused on developing robust methods to perform approximate Bayesian inference and Bayesian optimization with limited resources (e.g., only a few hundred likelihood evaluations). Check out our package Bayesian adaptive direct search for model fitting (via optimization), and Variational Bayesian Monte Carlo for Bayesian posterior and model inference. We also developed a technique to efficiently fit simulator-based models, inverse binomial sampling.

Our research in computational neuroscience focuses on investigating how we can model human and animal decision making by accounting for resource limitations and different sources of suboptimality. We are particularly interested in how the brain combines different sources of perceptual information when there are multiple possible underlying explanations (causal inference), and in how the brain represents probabilistic beliefs (priors), and how these beliefs are formed, updated, and used for decision making under uncertainty. We explore these questions with mathematical modelling and computational analysis of behavioral experiments.

See our Publications page for more information about our research.


Student collaborators

  • Nisheet Patel (Department of Neuroscience, University of Geneva)
  • Berk Gerçek (Department of Neuroscience, University of Geneva)
  • Yanli Zhou (Center for Data Science, NYU)
  • Xiang Li (Department of Psychology, NYU)