Muneeb Ahmed Khan

Research & Industrial Experience

Current Projects

Post-Processing Methods for Artifact Removal Using Machine Learning

Funding: Google Korea (2024-Present) | Affiliation: Google Korea & Pi Lab

Tools: PyTorch, TensorFlow, OpenCV, NumPy, Pandas

  • Investigating machine learning techniques for suppressing noise, ghosting, and compression artifacts.
  • Optimizing artifact removal performance across diverse imaging modalities.
  • Designing deployment strategies suitable for mobile and resource-constrained environments.

Vision-Language Model Fine-tuning with Parameter-Efficient Techniques

Affiliation: Pi Lab

Tools: PyTorch, Hugging Face Transformers, LoRA, QLoRA, PEFT, Vision Transformers

  • Developing efficient fine-tuning strategies for large vision-language models using LoRA and QLoRA techniques.
  • Implementing PEFT methods to adapt pre-trained models for domain-specific CV tasks.
  • Optimizing memory usage and computational efficiency while maintaining model performance across multiple vision tasks.
  • Investigating adapter-based approaches for multi-modal understanding in resource-constrained environments.

Partial Diffusion Model Architecture for Image Super-Resolution

Affiliation: Pi Lab

Tools: PyTorch, TensorFlow, OpenCV, NumPy, Matplotlib

  • Developing diffusion techniques for image segmentation and resolution enhancement.
  • Optimizing computational efficiency for deployment with limited resources.
  • Validating performance across diverse imaging for real-world applications.

Past Projects

Objective Quality Metrics for Ghosting Artifacts in Video and HDR Images

Funding: Google Korea (2023-2024) | Affiliation: Pi Lab

Tools: PyTorch, OpenCV, scikit-image, NumPy, Pandas

  • Built a dataset of 2,500+ real images and annotated over 37,000 patches for spatial/temporal artifact detection.
  • Designed data pipelines for collection, annotation, and feature extraction from diverse imaging sources.
  • Developed a test framework simulating multiple camera noise conditions for robust artifact evaluation.
  • Developed a detection model evaluated using subjective and objective quality metrics.
  • Trained SOTA deep learning models for domain specific tasks (e.g ghosting artifacts, object detection).

Multi-Scale Attention Model for Low-Light Image Enhancement

Affiliation: Pi Lab (2024)

Tools: PyTorch, LoL Dataset v1/v2, GCP A100, SSIM, MS-SSIM, LPIPS

  • Designed a lightweight model (12M parameters) achieving 0.88 SSIM, 0.93 MS-SSIM, 0.207 LPIPS on LoL datasets using GCP A100.
  • Worked on integrating attention Mechanisms and transfer learning.
  • Implemented an adaptive enhancement pipeline balancing perceptual quality and computational efficiency.
  • Reviewed and implemented AAAI, CVPR, and ICCV research papers to optimize model performance.

Traffic Sign Recognition with Advanced Neural Network Techniques

Affiliation: Pi Lab (2022-2024)

Tools: TensorFlow, OpenCV, Grad-CAM, LIME, GTSRB, ITSD, PTSD

  • Developed an interpretable CNN (2.6M parameters) achieving 98.4% accuracy and 74.34 ms inference.
  • Streamlined ML model development and deployment with MLflow for tracking and reproducibility.
  • Optimize GPU acceleration and parallel processing to optimize the system performance.
  • Worked on explainable and adversarial robust traffic sign classification to improve model accuracy.

Advanced Medical Imaging Analysis for Disease Detection

Affiliation: Pi Lab (2023-Present)

Tools: TensorFlow, Keras, PyTorch, Grad-CAM, LIME, OpenCV

  • Developed lightweight convolutional architecture achieving 99.51% mAP with 17.2 ms inference for brain tumor detection.
  • Worked on multi-modal analysis of brain tumors and chest diseases (COVID-19, pneumonia, tuberculosis).
  • Designed an adaptive channel attention mechanism with multi-path CNN architecture for medical image classification.
  • Implemented explainable AI techniques (Grad-CAM, LIME) reducing false positives by 78% and enhancing diagnostic interpretability.

FireXplainNet: Interpretable Wildfire Detection System

Affiliation: Pi Lab (2022-2023)

Tools: PyTorch, Grad-CAM, Matplotlib

  • Developed a lightweight interpretable CNN (5.3M parameters) for early wildfire detection.
  • Applied gradient-based attribution (Grad-CAM) for decision explainability in high-risk outdoor scenarios.
  • Achieved high accuracy under variable conditions; received Best Paper Award at KCC 2023.

YOLO-Based Real-Time Object Detection and Tracking System

Affiliation: Pi Lab (2021-2023)

Tools: YOLOv5, YOLO-NAS, DeepSORT, Kalman Filter, ByteTrack, PyTorch, OpenCV

  • Built a real-time multi-object detection and tracking system using YOLOv5/YOLO-NAS and DeepSORT.
  • Achieved mAP > 92% and 30+ FPS on COCO/MOT datasets, enabling real-time visual intelligence.
  • Leveraged GPU parallelization strategies to optimize inference performance.