Muhammad Rashid

Muhammad Rashid

Machine Learning Researcher

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Muhammad Rashid

Machine Learning Researcher specializing in Explainable AI (XAI),
Computer Vision, and Visual Anomaly Detection for real-world and safety-critical systems.

🔬 Research Focus

  • Explainable AI for trustworthy decision-making
  • Visual anomaly detection in industrial and robotic environments
  • Pixel-level feature attribution using Shapley-based methods
  • Robust and efficient explanation methods for computer vision

📌 AAAI 2026: ShapBPT — Image Feature Attribution using Data-Aware Binary Partition Trees
📌 ICPE 2026 / QualITA Workshop: ShapBPT in Perspective: A Consolidated Review and an eXplainable Anomaly Detection Case Study

⚙️ Systems & Engineering

  • Real-time anomaly detection for robotics safety
  • ROS2-based live inference pipeline
  • VAE / VAE-GAN models for industrial anomaly detection
  • Multi-camera monitoring and safety-area segmentation
  • End-to-end workflow: training → threshold calibration → live deployment

🚀 Achieved:

  • ~12 FPS real-time inference
  • ~99.6% detection accuracy in industrial scenarios
  • Explainable anomaly maps for safety-critical decision support

🧠 Open Source & Tools

  • ShapBPT — AAAI 2026 explainability framework
  • XAD — Explainable anomaly detection with ShapBPT
  • LIME Stratified Sampling — Improved explanation stability
  • AI on Edge Devices — Lightweight deployment pipelines
  • XAI evaluation tools for saliency, attribution, and benchmarking

📄 Selected Publications

🚀 Featured Projects

➡️ View all projects

🔧 Real-Time Anomaly Detection for Robotics

  • Industrial safety monitoring system
  • Multi-area detection: RoboArm, Conveyor Belt, Pallet Left, Pallet Right
  • ROS2 integration with live inference and anomaly scoring
  • Visual anomaly maps for decision interpretation

Code

🧠 ShapBPT

  • AAAI 2026 explainability method for images
  • Uses data-aware Binary Partition Trees
  • Provides hierarchical Shapley-based feature attributions
  • More image-structure-aware than standard SHAP partition explainer

🔗 Links:

PDF arXiv Code Tests PyPI User Study Poster

🧪 XAD: Explainable Anomaly Detection

  • Applies ShapBPT to anomaly detection systems
  • Explains why an image or region is considered anomalous
  • Supports interpretation of black-box anomaly detection models
  • Code: github.com/rashidrao-pk/XAD

📊 LIME Stratified Sampling

  • Improves stability of LIME image explanations
  • Reduces variance in perturbation-based sampling
  • Provides more reliable local explanations

🌍 Experience

  • 🎓 PhD Researcher — University of Turin, Italy
  • 🏭 Industrial Research — RuleX Innovation Labs
  • 🇪🇺 EU Project — DistriMuSe: Robotics Safety & AI
  • 🌐 Visiting Researcher — University of Granada, Spain

🤝 Collaboration

I’m interested in collaborations on:

  • Explainable AI and trustworthy machine learning
  • Visual anomaly detection
  • Industrial AI and robotics safety
  • Computer vision for real-world systems
  • Vision-language models and explainability

📫 Contact