Current Members
Jasin Machkour

Jasin Machkour

Member of the Robust Data Science Group at the Institute of Telecommunications, TU Darmstadt.


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64283 Darmstadt

Office Hours: Upon request


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 Technology 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 September 2019, he started his doctoral studies at TU Darmstadt. From September 2019 until February 2020 and from May 2023 until August 2023, he visited Prof. Daniel P. Palomar at The Hong Kong University of Science and Technology. From May 2022 until July 2022, he visited Prof. Frédéric Pascal at Université Paris-Saclay (CentraleSupélec). In 2022, he completed the Statistical Genomics Summer Course at University of Oxford.

Jasin is a founding member of the EURASIP Student Committee and represents the area Theoretical and Methodological Trends in Signal Processing (TMTSP).


Jasin’s research interests lie in the broad field that is often called statistical learning, machine learning, data science, or statistical signal processing. He is particularly interested in developing methods/algorithms for large-scale and high-dimensional learning tasks with provable statistical properties.

The following topics are of great interest to him:

  1. Sparse regression in high-dimensional settings
  2. False discovery rate (FDR) control in variable/feature selection
  3. Methods for genome-wide association studies (GWAS)
  4. Parallel computing on high-performance computing (HPC) clusters.


TRexSelector: T-Rex Selector: High-Dimensional Variable Selection & FDR Control [CRAN] [GitHub] [Paper]

tlars: The T-LARS Algorithm: Early-Terminated Forward Variable Selection [CRAN] [GitHub] [Paper]

Fabian Scheidt Robust and Computationally Efficient Statistical Learning in High-Dimensional Data: The T-Rex Selector Master's Thesis 03/2023
Marie Flohr Reproducibility Analysis of Discoveries in Genome-Wide Association Studies Using Variable Selection Methods for High-Dimensional Data Bachelor's Thesis 12/2022
Simon Tien
(co-supervision with Michael Muma)
Development of High-Dimensional Learning Methods for Genome-Wide Association Studies Master's Thesis 06/2022
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
Group of three Master's students Python vs. R: Benchmarking the computational performance of the Terminating Random Experiments (T-Rex) Method RSP Seminar 07/2022
Group of four Master's students HIV Dataset Case-Study C – Same Features but Different Responses RSP Seminar 02/2022
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


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