Berens Lab

Data Science for Vision Research

Home » Labs » Berens Lab » Members » Sarah Müller

Sarah Müller

Surname Müller
First name Sarah
Present position and title PhD Student

Business address

Werner Reichardt Centre for Integrative Neuroscience (CIN)
Institute for Ophthalmic Research
University of Tübingen
Otfried-Müller-Str. 25
D-72076 Tübingen,
Germany

E-mail: sar.mueller[at]uni-tuebingen.de

Academic Education

Year Degree University Field of study
2021 M.Sc. University of Stuttgart Engineering Cybernetics
2018 B.Sc. University of Tübingen and University of Stuttgart Medical Engineering

Professional Experience

Period Institution Position Discipline
Apr. 2018 – Nov. 2020 University Hospital Tübingen, Department of Diagnostic and Interventional Radiology Research Assistant Medical Image Analysis
May 2020 – Jul. 2020 University of Stuttgart, Institute for System Dynamics (ISYS) Teaching Assistant Signal Processing, Dynamic Filtering
Oct. 2019 – Mar. 2020 University of Stuttgart, Institute of System Theory and Automatic Control (IST) Teaching Assistant Control Engineering
Apr. 2019 – Aug. 2019 Daimler AG, R&D, Perception and Digital Testing Department, Image Understanding Group Internship Deep Learning, Software Development
Oct. 2018 – Feb. 2019 University of Stuttgart, Institute of System Theory and Automatic Control (IST) Teaching Assistant Control Engineering
Nov. 2017 – Mar. 2018 University of Stuttgart,
Institute of Geometry and Topology (IGT)
Teaching Assistant Advanced Mathematics III

Research Interests

My research is about developing machine learning and computer vision methodologies suitable to clinical problems. To enable safe and efficient methods in clinical practice, I am particularly interested in the estimation and utilization of uncertainty.

The focus of my PhD project is on medical image analysis for diagnosis, prognosis, or personalized treatment. Currently, I am involved in a project where we analyze ophthalmic disorders with retinal fundus images and generative models.

Specific research interests include: computer vision, deep learning, generative models, medical image analysis, Bayesian statistics, uncertainty, active learning, data fusion, reinforcement learning.