High Performance EM for Gaussian Mixture
- Research topic/area
- Artificial Intelligence
- Type of thesis
- Bachelor / Master
- Start time
- -
- Application deadline
- 30.06.2028
- Duration of the thesis
- 4 months(BSc) - 6 months(MSc)
Description
High performance Expectation Maximization (EM) for Gaussian Mixture is compelling because it unlocks scalable, accurate density estimation for modern datasets. Faster E and M steps enable real time clustering, anomaly detection, and soft classificationin streaming and interactive applications. Optimized linear algebra, vectorization, and GPU acceleration reduce runtime and energy, broadening feasibility on edge and cloud. Careful numerical stability, batching, and memory layout improve convergence
and robustness on large datasets. Parallelized responsibilities, batched covariance updates, and efficient mixture normalization increase throughput without sacrificing precision. Such implementations empower rapid model selection, online updates, and hyperparameter sweeps, driving better decisions in diverse range of applicatioons.
Requirement
- Requirements for students
-
- There are no hard constraints but the more programming and math you know the more you can have fun while doing the project.
- Faculty departments
-
- Engineering sciences
Informatics
- Engineering sciences
Supervision
- Title, first name, last name
- Ali Darijani
- Organizational unit
- Computer Science(IAR/IES)
- Email address
- ali.darijani@iosb.fraunhofer.de
- Link to personal homepage/personal page
- Website
Application via email
- Application documents
-
E-Mail Address for application
Senden Sie die oben genannten Bewerbungsunterlagen bitte per Mail an ali.darijani@iosb.fraunhofer.de
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