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Jennifer Landes

My research focuses on the design, enhancement, and evaluation of a preprocessing pipeline for eye-tracking data. The pipeline consists of multiple stages, including the detection and treatment of missing values and outliers, data normalization, as well as optional steps such as, smoothing and filtering. The pipeline's evaluation involves applying various methods at each stage and analyzing their impact on classification performance, specifically using a Random Forest classifier.


Research Interests

  • Mathematical models of Maschine Learning
  • Data Science
  • Artificial Intelligence
  • Data Preprocessing


Publications


Teaching

Lectures and Seminars:

ONGOING

  • Softwareprojekt Data Engineering (SS25)

PAST

  • IT-Grundlagen 1 und Programmierung at HNU (SS23-WS24)
  • Grundlagen der Informatik at EAH Jena (WS24)

Theses:

2024

  • SS 24: Vorhersage von Betrugsversuchen nach einer Prüfung duch Auswertung von Eye-Tracking Daten mithilfe von Machine Learning in Python (Bachelor Th