Current Staff Members
Martin Gölz

Martin Gölz

Member of the Signal Processing Group at the Institute of Telecommunications, TU Darmstadt.

Contact

work +49 6151 16-21351
fax +49 6151 16-21342

Work S3|06 251
Merckstr. 25
64283 Darmstadt

Office Hours: upon request

Martin received his B.Sc. in Electrical Engineering and Information Technology with major in Communication Engineering and Sensor Systems (CES) from Technische Universität Darmstadt in September 2016. In his bachelor's thesis, he dealt with bootstrapping sequential probability ratio tests. He received the “Rohde & Schwarz-Preis” for the best graduate of the year in CES and Computer Engineering in his faculty. Afterwards, he spent half a year as a research assistant at Aalto University, Finland, to work on nonparametric detection methods in the group of Prof. D.Sc. (EE) Visa Koivunen.

At the beginning of his master's in Electrical Engineering and Information Technology at Technische Universität Darmstadt, he was accepted into the Future Minds Student Program by Siemens AG and consequently started working part-time in the project management department of Siemens Mobility Division in Mannheim. For his master's thesis on spatial inference in large-scale sensor networks, he moved back to Finland to continue the collaboration with Aalto University. He received his M.Sc. with honors from Technische Universität Darmstadt in March 2019.

In April 2019, he commenced working towards his PhD with the Signal Processing Group. His research interest is in the field of (robust) statistical signal processing, in particular in multiple hypothesis testing for large-scale sensor networks. If you are interested in Martin's research work, you may watch the video of his presentation at the 2022 International Conference on Acoustics, Speech, and Signal Processing here, or watch his EUSIPCO 2023 3MT video below.

3MT video from EUSIPCO 2023

With this video about his PhD Thesis, Martin qualified for the final of EUSIPCO 2023's Three Minute Thesis competition. At the final in Helsinki, he won the second place.

Open Student Projects

I am offering student projects (pro and project seminars, bachelor's and master's theses) related to my core research interests. Those are in signal processing application of multiple hypothesis testing (MHT), with a particular focus on spatial anomaly detection via large-scale sensor networks for the Internet of Things (IoT). But I am also interested in supervising projects that deal with any other problem solvable via MHT. This includes a large variety of biomedical signal processing applications.

If you have a different, specific topic in mind, I am happy to discuss the idea with you and see if we can shape it into a suitable student project together.

To get in touch, shoot me an . We will then arrange an informal first meeting.

Ongoing Student Projects

Large-scale spatial inference via a temperature/humidity wireless sensor network bachelor's thesis
Robust bootstrap for large-scale data project seminar

Completed Student Projects

False discovery rate control under dependence proseminar
Parametric target velocity estimation in heterodyne interferometry master's thesis
Spatial inference in large-scale sensor networks: Practical challenges and applications master's thesis
Spatial multiple hypothesis testing with hidden Markov random fields bachelor's thesis
Realization of a large-scale wireless sensor network for anomaly detection with multiple hypothesis testing bachelor's thesis
Modeling spatial dependencies in test statistics using copulas bachelor's thesis
Robust bootstrap for large-scale data proseminar
Spatio-temporal inference using multiple testing and reinforcement learning master's thesis

Teaching

Adaptive Filters (SS 19)

Digital Signal Processing (WS 19/20, WS 20/21, WS 22/23)

Fundamentals of Signal Processing (SS 19, SS 20)

Publications

Loading...
Loading data from TUbiblio…

Error on loading data

An error has occured when loading publications data from TUbiblio. Please try again later.

  • {{ year }}

    • ({{ publication.date.toString().substring(0,4) }}):
      {{ publication.title }}.
      {{ labels[publication.type]?labels[publication.type]:publication.type }}
    • […]