Marwan Haioun

Optimized novel protocols for frozen human brain tissue fixation and dissociation to selectively isolate single Human Brain Microvessel Endothelial Cells (HBMVECs) and whole brain microvessels.

Performed label-based protein quantification using mass-spectrometry on isolated and control samples to identify HBMVECs-specific potential new targets for drug delivery through the Blood-Brain Barrier (BBB).

Verified and incorporated new meaningful metrics from biocomputational proteomics data analysis into the existing workflow to enhance BBB target discovery.

Participated in in‑vivo studies involving tissue dissection, brain cryostat sectioning, and immunostaining for antibody validation.

Regularly presented and discussed meaningful results with team members to choose the preferred course for proteomics data analysis and target discovery.

Attended the BTP's Sponsors and Partners Meeting, promoting new brain delivery targets and omics
analysis tools to sponsor companies.

Established and optimized protocols for collecting high‑quality electroencephalography (EEG),
inertial measurement unit (IMU), electromyography (EMG), and motion capture data during walking
within a biomechanical lab (CAREN).

Programmed a virtual environment in D‑flow, ensuring fine control of experimental conditions,
real‑time visual feedback, and multimodal data acquisition during walking.

Developed a user‑friendly pipeline for automated large‑scale mobile EEG data processing and
analysis in MATLAB, leveraging EEGLAB to study cognitive load change across tasks.

Worked closely with research and clinical teams to optimize experimental procedures, significantly
enhancing data reliability and streamlining overall research workflow.

Trained fellow team members on executing personal protocols for EEG data collection and
processing.

Assisted in managing experimental setups and participant data acquisition, ensuring reliable and consistent data collection during motor imagery tasks.
Developed a Python pipeline for EEG data processing to identify statistically relevant key motor imagery patterns using time‑frequency and spatiotemporal analysis with the MNE library.
Implemented support vector machine and random forest algorithms leveraging meaningful EEG features for intent classification.