Jasin Machkour
Member of the Signal Processing Group at the Institute of Telecommunications, TU Darmstadt.
Contact
j.machkour@spg.tu-...
work +49 6151 16-21353
fax +49 6151 16-21342
Work
S3|06 252
Merckstr. 25
64283
Darmstadt
Office Hours: Fridays, 2-5 p.m.
News
- Invited Lecture at 3rd Statistical Learning for Signal and Image Processing (SLSIP) Workshop in Rüdesheim, Germany (October 6-8, 2021).
Title: “The Terminating-Knockoff Filter: Fast High-Dimensional Variable Selection with False Discovery Rate Control.”
Bio
Jasin received the B.Sc. degree in Industrial Engineering and the M.Sc. degree in Electrical Engineering and Information Technology with a major in Communication and Sensor Networks from Technische Universität Darmstadt (TU Darmstadt) in 2016 and 2019, respectively.
He spent one year of his master’s studies (2016/2017) at the Department of Electrical and Computer Engineering of the University of Illinois at Urbana-Champaign (USA).
His bachelor’s and master’s theses deal with robust and adaptive regression for linear and sparse models and robust and adaptive statistical learning for high-dimensional data, respectively.
In 2019, he joined the Signal Processing Group at TU Darmstadt and started his doctoral studies. From September 2019 until February 2020, he visited Prof. Daniel P. Palomar at the Hong Kong University of Science and Technology.
Research
Jasin’s research interests lie in the broad field that is often called statistical learning, machine learning, data science or statistical signal processing. Within this field he focusses on developing methods/algorithms for large-scale and high-dimensional learning tasks with provable statistical properties.
The following topics are of great interest to him:
- Sparse regression in high-dimensional settings
- False discovery rate (FDR) control in variable/feature selection
- Methods for genome-wide association studies (GWAS)
- Parallel computing on high-performance computing (HPC) clusters.
Current Student Projects
|
Simon Tien (co-supervision with Michael Muma) |
Development of High-Dimensional Learning Methods for Genome-Wide Association Studies | Master's Thesis |
| Group of four Master's students | HIV Dataset Case-Study C – Same Features but Different Responses | RSP Seminar |
|
Lisa Dawel (co-supervision with Michael Muma) |
Development and Analysis of Sparse High-Dimensional Ensemble Learning Methods | Master's Thesis | 12/2021 |
| Pertami Kunz | Robust Variable Selection and False Discovery Rate Control for High-Dimensional Regression Models Using Knockoffs | Master's Thesis | 02/2021 |
| Qiang Zhao | Robust Error Control for High-Dimensional Variable Selection based on the Stability Selection Method | Master's Thesis | 01/2021 |
| Individual project (Master's student) | Integrating Robust Loss Functions Into Deep Learning | RSP Seminar | 02/2021 |
| Individual project (Master's student) | Robust Reconstruction of Biomedical Images in the Presence of Outliers | RSP Seminar | 02/2021 |
| Individual project (Master's student) | HIV Dataset Case-Study B – What Types of Outliers are More Severe? | RSP Seminar | 02/2021 |
| Group of two Master's students | Dependent Data Bootstrap | ATISSP Seminar | 02/2021 |
| Group of two Master's students | Hypotheses Testing With The Bootstrap and Signal Detection | ATISSP Seminar | 02/2021 |
| Group of two Master's students | Applications of The Bootstrap | ATISSP Seminar | 02/2021 |
Teaching
Robust and Biomedical Signal Processing (RSP Seminar) – WS 2020/21 & WS 2021/22
Medizintechnisches Praktikum – WS 2021/22
Praktikum Kommunikationstechnik und Sensorsysteme – SS 2021 & WS 2021/22
Advanced Topics in Statistical Signal Processing (ATISSP) – WS 2020/21
Publications
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