Farnaz Rahimi

• Since January 2025:
  Postdoctoral researcher at the Biomedical Network Science Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.
• December 2022 – December 2024:
  Research assistant at the Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.
• March 2022 – November 2022:
  Visiting researcher at the N-squared Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.
• September 2018 – May 2024:
  Ph.D. in Electrical Control Engineering at University of Tabriz, Tabriz, Iran. Thesis entitled “EMG-Based Hand Movement Classification and Force Estimation Using CNN-LSTM Models”
• September 2016 – August 2018:
  Master in Electrical Control Engineering at University of Tabriz, Tabriz, Iran.
• September 2012 – July 2016:
  Bachelor in Electrical Engineering at University of Tabriz, Tabriz, Iran.

2024

• Federated network medicine for laboratory data in paediatric oncology

  (Third Party Funds Group – Overall project)

  Term: 01/11/2024 - 31/10/2026
  Funding source: BMBF / Verbundprojekt
  Abstract

  In FLabNet, we will harness the potential of algorithmic network biology and distributed machine learning to address two exemplary unmet needs in paediatric oncology: prediction ofchemotherapy side effects like neutropenic fever and early-stage detection of rare malignantdiseases such as myeloproliferative neoplasms. Based on >54 million laboratory test resultsfrom >500,000 patients from the Core Dataset of the German Medical Informatics Initiative (MII),we will create personalised networks, where nodes represent individual laboratory measurementsand edges encode patient-specific relationships. We hypothesise the emerging personal graph representations to capture the unique spectra and dependencies of the individual patients’ health anddisease characteristics. The networks will be used as signatures for label-efficient graph-based pre-dictors such as graph kernels; and we will provide privacy-preserving federated implementationsof our predictors that are fully interoperable with MII standards. To achieve its objectives, ourconsortium combines expertise in algorithmic systems biology (FAU), paediatric oncology (UKER),quantitative analysis of laboratory data (UKER), federated learning for biomedicine (Bitspark GmbH& FAU), and professional software development (Bitspark GmbH). These synergistic skill sets willenable us to combine laboratory diagnostics, computational systems medicine, and privacy-preserving machine learning, advancing the state of the art in quantitative analysis of laboratory data for precision medicine in paediatric oncology and beyond. 

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2021

• Empathokinästhetische Sensorik für Biofeedback bei depressiven Patienten

  (Third Party Funds Group – Sub project)

  Overall project: Empathokinästhetische Sensorik
  Term: 01/07/2021 - 30/06/2025
  Funding source: DFG / Sonderforschungsbereich (SFB)
  URL: https://www.empkins.de/
  Abstract

  The aim of the D02 project is the establishment of empathokinesthetic sensor technology and methods of machine learning as a means for the automatic detection and modification of depression-associated facial expressions, posture, and movement. The aim is to clarify to what extent, with the help of kinesthetic-related modifications influence depressogenic information processing and/or depressive symptoms. First, we will record facial expressions, body posture, and movement relevant to depression with the help of currently available technologies (e.g., RGB and depth cameras, wired EMG, established emotion recognition software) and use them as input parameters for new machine learning models to automatically detect depression-associated affect expressions. Secondly, a fully automated biofeedback paradigm is to be implemented and validated using the project results available up to that point. More ways of real-time feedback of depression-relevant kinaesthesia are investigated. Thirdly, we will research possibilities of mobile use of the biofeedback approach developed up to then.