Applied Machine Learning

Members of the AML Group: Zarah Khosravi, Shammi More, Ji Chen, Kaustubh Patil, Xiaojin Liu, Daouia Larabi, Niels Reuter


Technological progress allows data collection at an ever-increasing rate. This is true for many scientific fields including neuroscience where structural and functional brain-imaging data is now available for tens of thousands of subjects. This raises the question of how these large amounts of data can be efficiently and effectively transformed into insights to improve our understanding of the brain as well as foresights that can influence clinical decisions. Machine-learning based analytics provides a plethora of tools to tackle such questions. Importantly, machine-learning can provide predictions at the individual level which can help understand individual differences, as opposed to the traditionally used group analyses. Machine-learning is a vast toolbox which offers a multitude of options while setting up a prediction pipeline, including choices for data representation and learning algorithms. The success of a machine-learning pipeline critically depends on making proper choices.

Research Topics

The group “Applied Machine Learning”, led by Dr. Kaustubh Patil, focuses on designing and evaluating machine-learning algorithms and pipelines. The application areas include mental health and psychiatric disorders as well as basic understanding of brain structure, function and development. The group relies on neuroimaging and behavioral data, either publicly available or acquired in-house. Example projects include:

  • Brain-age prediction: the chronological age might differ from the biological age of the brain in patients suffering from neurodegenerative diseases. This difference can serve as a biomarker.
  • Disorder subtyping: identification of disorder (e.g. Schizophrenia and Parkinson’s) subtypes from behavioral and neuroimaging data with applications in precision medicine.
  • Parcellation: identification of regional subdivisions within brain regions that exhibit differential connectivity, with a focus on clinical applications.

The group also works on machine learning problems with the aim of devising general methods for data representation as well as problem-specific algorithms that can generalize well. In addition, the group provides important support to the entire INM-7 in data analysis and modeling.


Dr Kaustubh Patil


Building 15.2 / Room 312

+49 2461/61-1521


Group members

Dr Kaustubh PatilNoneBuilding 15.2 / Room 312+49 2461/61-1521
Dr. Daouia LarabiBuilding 14.6y / Room 2035+49 2461/61-85859
Dr. Federico RaimondoBuilding 14.6y / Room 2049+49 2461/61-96083
Dr. Amir OmidvarniaBuilding 15.2v / Room 234+49 2461/61-8785
Nicolas NietoNoneBuilding 14.6y / Room 2049+49 2461/61-96083
Vera KomeyerBuilding 15.2 / Room 312+49 2461/61-1521
Leonard SasseBuilding 15.2v / Room 234+49 2461/61-8785
Sami HamdanBuilding 15.2 / Room 419+49 2461/61-8785
Shammi MoreBuilding 14.6y / Room 3049+49 2461/61-96116
Kimia NazarzadehNoneBuilding 15.2v / Room 234+49 2461/61-8785
Jinyang YuNoneBuilding 14.6y / Room 2038+49 2461/61-1521
Synchon MandalNoneBuilding 14.6y / Room 2049+49 2461/61-96083
Xinyi WangNoneBuilding 15.2 / Room 208+49 2461/61-8785
Tobias MugangaBuilding 14.6y / Room 2038+49 2461/61-1521
Lucy WiedermannNoneBuilding 14.6y / Room 2035+49 2461/61-85859
Georgios AntonopoulosBuilding 15.2 / Room 318+49 2461/61-8785


Xiaojin LiuBuilding 15.2 / Room 417+49 2461/61-85333
Dr. Niels ReuterNoneBuilding 14.6y / Room 3040+49 2461/61-96095
Last Modified: 17.05.2023