Philipp Dumbach, M. Sc.
- Job title: Research Associate
- Organization: Department of Computer Science
- Working group: Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)
- Phone number: +49 9131 85-26836
- Mobile phone: +49 162 242 075 81
- Email: philipp.dumbach@fau.de
- Website: https://www.mad.tf.fau.de/person/dumbach-philipp-m-sc/
- Address: Carl-Thiersch-Str. 2b
91052 Erlangen
GermanyRoom 01.010
| Since 11/2017 | Research Associate at the Machine Learning and Data Analytics Lab Friedrich-Alexander University Erlangen-Nürnberg |
| Since 11/2017 | Research Associate at the Central Institute of Medical Engineering (ZiMT) Friedrich-Alexander University Erlangen-Nürnberg |
| 10/2014 – 05/2017 | Master’s Degree (M. Sc.) “Industrial Engineering and Management“ Friedrich-Alexander University Erlangen-Nürnberg |
| 07/2015 – 12/2015 | Semester Abroad in Australia Royal Melbourne Institute of Technology (RMIT) |
| 10/2011 – 01/2015 | Bachelor’s Degree (B. Sc.) “Industrial Engineering and Management” Friedrich-Alexander University Erlangen-Nürnberg |
Courses
| Innovation Lab for Wearable and Ubiquitous Computing |
| Seminar Medical Devices of the Future |
| Seminar Operating Room of the Future |
| MedTech Entrepreneurship Lab |
Thesis supervision
| Year | Type | Name | Title |
| 2019 | Bachelor Thesis | Ilija Borodin | Modeling regional relationships – an automated network based website analysis |
[B] = Bachelor’s Thesis, [M] = Master’s Thesis, [I] = Implementation Task (for Computer Science “Master Project” or Medical Engineering “Research Internship” (Forschungspraktikum))
| Thesis ID | Scope | Topic | Area |
| 1910 | B / M | Design of accelerator programs in MedTech and Digital Health – Perspective of industry experts and investors in the framework of a qualitative-empirical study | Startup Development,
Innovation Management |
| 1911 | B / M | Incubation programs within the European ecosystem – A qualitative-empirical study for analysing the perspective of entrepreneurs and startups | Startup Development,
Innovation Management |
- Green Belt ML@Operations
This project develops a seminar on applied machine learning for engineers in production and quality.