Stefan Heinrich

Stefan Heinrich

Scholar & IT Tinkerer, Associate Professor at the IT University of Copenhagen, Denmark

Overview

Hi, I’m Dr. rer. nat. Dipl.-Inform. Stefan Heinrich, a scholar and IT tinkerer from Germany, currently appointed as associate professor of the Computer Science Department at IT University of Copenhagen and affiliate researcher at Pioneer Centre for Artificial Intelligence. These are what I consider my key skills and experiences in a nutshell:

  • Extensive experience in scientific research in the fields Computer Science, Cognitive Psychology, and Computational Neuroscience with Master and PhD degrees
  • Thirteen years academic research in Computational Modeling, Natural Language Processing, and Machine Learning - current focus on Temporal Dynamic and Probabilistic Neural Networks
  • Eleven years of teaching on bachelor and master level on Artificial Intelligence, Neural Networks, Data Mining, and Human-Robot Interaction as well as more than 30 individual PhD, MSc, and BSc thesis supervisions
  • Coordination and team building of research and student teams on short- to mid-term projects
  • Resourceful, creative, goal-oriented, organised, independent as well as team-worker, and team-builder

Research Interests and Background

My research interest is located in between artificial intelligence, cognitive psychology, and computational neuroscience. Here, I aim to understand the computational principles underlying brain function but also to utilise them in developing AI systems. In particular, I study the processes and mechanisms in the brain that form representations in temporally dynamic composition and decomposition as well as in multi-modal integration. As a central approach, I develop computational cognitive models such as artificial neural networks with plausible timescale mechanisms and probabilistic learning schemes on tasks and phenomena in music and language processing, neurodiversity, and cognitive development. These models have practical applications in machine learning, natural language processing, and data science as inductive biases in AI frameworks, and implications for cognitive science as priors and constraints in models of sequence generation, sequence prediction, and compositionality.