Algorithmic Information Theory for Machine Intelligence

Algorithmic Information Theory for Machine Intelligence

Internship Description

We recently developed numerical and computational techniques to use algorithmic information theory (AIT) to the analysis of networks. For publications see google scholar (https://scholar.google.com/citations?hl=sv&user=_DUppAgAAAAJ&view_op=list_works&so rtby=pubdate).

Subprojects include to

-          Develop python packages for AIT analysis of large-scale networks

-          Develop new AIT network embedding algorithms

-          Analyze Convolutional Networks a representational learning using AIT

-          Quantify and benchmark AIT network analysis with other techniques

-          Large-scale computation of AIT networks using a supercomputer (Shaheen)

New and improved numerical approximation of algorithmic complexity using massive computations of Turing Machines on Shaheen (supercomputer)

Deliverables/Expectations

Individual projects will be tailored and narrowly designed from the above palette according to interest of the student, technical proficiency, and level of study. We expect you (a) to bring enthusiasm, creativity, and hard work, (b) give lab seminars on your work, and (c) produce a final written report. In return this facilitates your critical thinking, presentations skills, and scientific writing. Your research, in collaboration and with support of team members, may lead to scientific publications. You will also get a good hands-on perspective at the frontier of machine intelligence and its applications in an interdisciplinary research group and environment.​

Faculty Name

Jesper Tegner

Field of Study

Computer Science, Applied Mathematics