I am a PhD student in Data Science at the University of North Texas (UNT) working at the intersection of machine learning and biomedical data analysis.

My research focuses on developing predictive and interpretable AI methods for EEG, medical imaging, and multimodal clinical data. Before starting my PhD, I completed a Master's in Biometrics & Intelligent Vision at Université Paris-Est Créteil (UPEC), France, and conducted medical AI research at Aix-Marseille University / CRMBM-CEMEREM.

My academic path began in Telecommunication Engineering, giving me a strong foundation in signal processing, wireless systems, and engineering before transitioning into computer vision and biomedical AI.

My PhD research investigates whether pre-treatment resting-state EEG can help predict patient response to repetitive transcranial magnetic stimulation (rTMS).

I am exploring:

• Functional connectivity: PLV, PLI, and wPLI
• Time-frequency representations: Fourier-Bessel Series Expansion, CQT, and WVD
• Deep learning: learning predictive representations from EEG-derived features
• Multimodal fusion: combining complementary EEG representations
• Validation: developing reliable patient-level evaluation strategies for biomedical datasets

The broader goal is to develop clinically meaningful and interpretable machine-learning methods for personalized treatment prediction.

At Aix-Marseille University / CRMBM-CEMEREM, I worked on deep-learning pipelines for multimodal medical imaging.

My work included:

• 3D CNNs for medical image analysis
• MRI and clinical data integration
• Medical image preprocessing
• Class imbalance handling and threshold calibration
• Grad-CAM explainability
• Reproducible HPC/SLURM training pipelines

You can find these projects in my repo.

Computer-vision pipeline for analyzing gaze behavior in real-world interview videos.

YOLOv5 Gaze Estimation Computer Vision

Object-detection system for identifying drones using Mask R-CNN.

PyTorch Mask R-CNN Object Detection

Unsupervised physiological time-series anomaly detection using an LSTM autoencoder.

LSTM Autoencoder ECG

Medical image segmentation using thresholding and morphological image processing.

Medical Imaging Segmentation Image Processing

Real-time face verification and identification application.

Biometrics Face Recognition Computer Vision

Languages & Data Python NumPy Pandas Scikit-learn

Deep Learning & Vision PyTorch OpenCV CNNs 3D CNNs LSTMs Autoencoders Object Detection

Biomedical Data EEG MRI ECG Signal Processing Time-Frequency Analysis

Research & Computing Linux Git GitHub SLURM HPC LaTeX Jupyter

PhD in Data Science University of North Texas, USA 2026 – Present

Master's in Biometrics & Intelligent Vision Université Paris-Est Créteil, France 2023 – 2025

Bachelor's in Telecommunication Engineering Pakistan

Telecommunications & Signal Processing 🇵🇰 ↓ Computer Vision & Biometrics 🇫🇷 ↓ Medical Imaging & Deep Learning 🇫🇷 ↓ EEG & Medical AI 🇺🇸

I am interested in research and collaboration involving Medical AI, NeuroAI, EEG, Computer Vision, Biomedical Signal Processing, Medical Imaging, and Multimodal Learning.

I am especially interested in work that connects machine-learning methodology with real biomedical and clinical problems.

📍 Denton, Texas, USA 🎓 PhD Researcher — University of North Texas 📧 Email: muhammad.bilal@unt.edu

Developing machine-learning methods that turn complex biomedical data into meaningful and interpretable predictions.