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@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.