I use this blog as a medium for refining my own thoughts on, and understanding of, various topics that I encounter in the course of my research. This primarily consists of research notes, pedagogical reviews, and personal thoughts on:
- Physics: a wide range of topics from quantum theory to black holes and back, typically with a eye towards deeper conceptual issues.
- Minds & Machines: mostly deep learning and a bit of computational neuroscience, through the lens of physics and information theory.
- Philosophy: meta musings on philosophy of physics, ontology, etc.
The best way to learn is to teach: I’m delighted when students or colleagues find something stimulating here, but insofar as I primarily use this as a research tool, my posting frequency varies depending on how readily whatever I’m currently engrossed in lends itself to a blog article. Much of the research process never makes it into papers, so consider this my humble attempt to sharpen and share my thoughts along the way.
I’m an assistant professor at Utrecht University, where I’m fortunate to hold a dual appointment between the Department of Physics and the Department of Information and Computing Sciences that allows me tremendous research freedom. My publications in high-energy physics can be found on INSPIRE-HEP, or by following the links on my Google Scholar profile. You can also find me on GitHub.
I’m a theoretical physicist by training, broadly interested in quantum gravity, particularly in the context of holography. AdS/CFT provides a concrete paradigm in which to investigate such questions as the emergence of spacetime from quantum entanglement, the reconstruction of black hole interiors, and the localization of information in gravitational theories. I’ve an unrequited love affair with black holes, and am interested in the application of von Neumann algebras (in particular modular theory) to these and other foundational issues.
More recently, I’ve branched into the surprisingly rich mathematical intersection of physics, information theory, and machine learning. Ideas from statistical thermodynamics and quantum field theory, for example, have recently been applied to the study of deep neural networks. I’m interested in such questions as how information propagates in neural networks (both biological and artificial), whether insights from physics can shed light on hierarchical models or the relevance of criticality, and the precise conditions under which complex phenomena like intelligence may emerge. I’m delighted that the aforementioned dual appointment with Computer Science enables me to explore this emerging interdisciplinary field in earnest, and am eager to collaborate with experts from complementary backgrounds.
Lastly, on a personal level: I’m non-binary, use they/them pronouns, and actively support a more inclusive scientific culture for women and trans/non-binary researchers at all levels. I mention this in part to build solidarity with other gender minorities in STEM fields, as well as to spare my colleagues the uncertainty of how to refer to me in third person. 😉