Physics and AI Workshop at McGill
Date:
At the two-day workshop I presented my poster Defining conformational states of proteins using dimensionality reduction and clustering algorithms - the same one from BPS 2019.
Workshop topics
- How AI can help physics-leaning engineers, physicists, and biologists to improve their science by, e.g., achieving state-of-the-art performance on existing hard inference problems, providing solutions to previously intractable inference problems at unprecedented scale or complexity, and enriching the set of concepts used to understand natural and engineered inferential systems.
- How theoretical and computational physics can support the research effort in artificial intelligence to gain deeper understanding of their algorithms. For example, the physics of condensed matter and in particular the statistical physics of the dynamics of many-body systems provide powerful and established theoretical tools and concepts designed for the analysis and understanding of complex systems like the deep and recurrent network models used in contemporary machine learning.