Geometric Gaussian Mixture Learning of Space Curves

Research topic/area

Artificial Intelligence, Machine Learning, Computational Mathematic

Type of thesis

Bachelor / Master

Start time

-

Application deadline

30.06.2028

Duration of the thesis

4 months(BSc) - 6 months(MSc)

Description

Preface

Sometimes you need a probabilistic representation of a deterministic space curve and for many reasons having them in Gaussian Mixtures is useful. Gaussian mixtures are fitted to data using expectation maximization. The algorithm is not guaranteed to converge to the optimum, is data hungry and quite slow.

Problem

Is it possible to have a Gaussian Mixture representation for a space curve without sampling and coupling it with an expectation maximization step?

Requirement

Requirements for students

• * Firm grasp over Linear/Multilinear Algebra and Analysis
• * PyTorch or an equivalent NumPy-like framework
• * Some exposition to Functional Analysis and Differential Geometry is advantageous

Faculty departments

• Engineering sciences
  Geodesy & geoinformatics
  Informatics
  Mechanical Engineering
  Remote Sensing and Geoinformatics
  Information System Engineering and Management
  Electrical Engineering and Information Technology
  Mechatronics and Information Technology
• Natural sciences and Technology
  Geophysics
  Mathematics
  Computational and Data Sience
  Physics
  Techno-Mathe­matics

Supervision

Title, first name, last name

Ali Darijani

Organizational unit

* Computer Science(IAR/IES) * Fraunhofer IOSB

Email address

ali.darijani@iosb.fraunhofer.de

Link to personal homepage/personal page

Website

Application via email

Application documents

• Curriculum vitae
• Grade transcript
• Any document of your choosing

E-Mail Address for application
Senden Sie die oben genannten Bewerbungsunterlagen bitte per Mail an ali.darijani@iosb.fraunhofer.de


Back