Muhammad Talha Zubair

As an image recognition, computer vision, and deep learning researcher, I significantly contributed to infrastructure development, notably developing algorithms to detect harmful cells in microscopic chicken imagery. I retrained the StarDist segmentation model with Python and TensorFlow for multispecies detection and built a custom Keras data generator for efficient dataset loading.

I utilized OpenCV, k-means, PCA, and ResNet-50 for noise removal and PostgreSQL and Pandas for data analysis. I also implemented an MLOps pipeline with wandB to facilitate seamless dataset and model tracking, with GitHub pre-commit hooks ensuring code formatting.

Prioritizing scalability, I deployed code using Docker, leveraged Azure DevOps for version control and management, and optimized Linux pipelines with bash scripts. I adhered to best practices in code optimization and version control using Git and Azure DevOps and implemented CI/CD and cron jobs. Finally, I managed Docker containers with Kubernetes and tested solutions via Flask APIs.

I successfully trained the YOLOv5 computer vision algorithm using PyTorch. This training utilized custom datasets across various GPU environments, including Amazon EC2 instances. Subsequently, I converted the Torch YOLOv5 model to the Core ML format, enabling efficient inference on iOS platforms. DeepLab was instrumental in segmenting different road parts, with image up-sampling techniques deployed via cv2 superRes.

I developed Bash scripts and Docker pipelines, which automated significant parts of the training and testing processes. I collected data using PostgreSQL, with comprehensive data analysis conducted through Pandas. I worked extensively with Amazon EC2 and S3, ensuring efficient deployment and storage, and implemented unit tests to ensure seamless integration with GitHub Actions. Code optimization was a priority, and I maintained version control using Git, ensuring high quality and collaboration standards. Finally, I rigorously tested the Flask API for back-end solutions to ensure reliable performance and functionality.

I spearheaded the design and development of an object detection and tracking solution for thermal imagery, emphasizing occlusion handling and deployment on the NVIDIA Jetson TK1. The project involved training PyTorch's YOLOv3-tiny model on a custom dataset and creating a Python-based feature-tracking pipeline using normalized cross-correlation (NCC), SIFT feature selection, and the Kalman Filter for optimal estimation. Analyzing our custom detector and tracker results offered valuable system performance insights.

As a team leader, I promoted collaboration between hardware and software teams, managed a four-person team, and ensured accurate object annotation guidance for various videos.

The Python solution was effectively deployed on NVIDIA Jetson TK1. We used OpenCV for pipeline development, Numpy for efficient array operations, and improved performance with Cython and Numba for real-time capabilities. Azure DevOps was our choice for code versioning, reporting, and requirement management, and we employed Linux frameworks and bash scripts for efficient pipelines.

I developed a deep reinforcement learning-based recommendation algorithm for custom users on the augmented reality game Mad Hunt using Python. I designed and deployed reward functions in reinforcement learning using TensorFlow within the existing game structure, and handled queries from Firebase and AWS platforms in Python using Firebase SDK and the Boto library.

I implemented the YOLO image recognition algorithm to facilitate real-time object detection during gameplay while researching and developing cutting-edge recommendation systems based on reinforcement learning. Exploring existing recommendation systems led to a focus on machine learning algorithms, such as collaborative filtering systems.

I also designed and implemented facial recognition solutions using algorithms like Haar cascades, MTCNN, and FaceNet in TensorFlow, deploying these on web platforms. I used Flask and NGINX for efficient web hosting and server-side functionality and Azure DevOps for code versioning, reporting, and requirements management.

To ensure the highest quality standards, I prioritized code optimization and version control using Git. Finally, I handled CRUD operations on a MySQL database to manage daily user interactions with the application.

In this project, I focused on researching and developing existing deep-learning solutions for gaze estimation. This process involved training a computer vision gaze detection solution using a custom dataset developed in PyTorch. To keep track of our experiments, I used the MLOps tool WandB, which allowed us to manage and monitor our machine learning operations effectively. All model training and testing were conducted using Google Cloud Platform (GCP) instances, which provided us with scalable compute resources to ensure efficient performance and reliable results.

In this project, I developed and implemented natural language processing (NLP) algorithms and models to enhance optical character recognition (OCR) systems, resulting in an impressive 20% improvement in text extraction accuracy. This success was achieved through a fruitful collaboration with a cross-functional team of software engineers and data scientists. Together, we optimized OCR workflows, achieving a 30% reduction in processing time.

I conducted a comprehensive analysis of text data and bolstered OCR accuracy by implementing data preprocessing techniques like tokenization, stemming, and lemmatization. Further enriching our OCR systems, I integrated advanced NLP techniques, such as named entity recognition (NER) and sentiment analysis, allowing us to extract more valuable information from documents.

To continually improve OCR performance, particularly on challenging document types, I trained and evaluated deep learning models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs).

I developed an advanced evaluation framework for assessing the quality of stable diffusion-generated images. This involved leveraging Detectron2 and a supervised graph approach, specifically incorporating a relative size graph to accurately determine the realism of objects within the images.

RESPONSIBILITIES
• Researching and implementing an LSTM-based speaker diarization algorithm for the call center analytics application.
• Preprocessing the audio data, including feature extraction and normalization.
• Testing and tuning the algorithm to optimize its performance and accuracy.
• Creating a user-friendly interface for the output of the algorithm.

I successfully implemented an LSTM-based speaker diarization algorithm that achieved 85% accuracy on a large customer call center recordings dataset. This algorithm was integrated into the call center analytics application, enabling our clients to understand their customers' needs better and improve their overall performance.