Hi, My name is Armin, I'm a Machine Learning Engineer, my passion for the field has been a driving force throughout my academic journey and over seven years of professional experience. This commitment has fueled my ambition to contribute significantly to the advancement of state-of-the-art algorithms. With a robust foundation in algorithm design and problem-solving, I have consistently delivered reliable results. My approach to learning is anchored in both research and the practical implementation of cutting-edge applications. Creativity and productivity are the cornerstones of my work ethos, empowering me to develop innovative algorithms with minimal oversight. Deep learning is more than a professional interest; it's a passion. My inherent talent for crafting new algorithms has established me as an independent researcher, adept at working autonomously. My extensive project management experience ensures that I can set up and adhere to precise timelines, guaranteeing the delivery of expected outcomes. I am dedicated to sharing my knowledge and insights, presenting my experiences, ideas, and implementation details to peers and aspiring students alike, fostering a collaborative and forward-thinking environment.
One of My Projects.
Object tracking in video analysis is a sophisticated computer vision technique that monitors the movement of objects across frames by analyzing their spatial and temporal characteristics. The process involves detection and localization of objects using advanced algorithms like YOLOv7, YOLOR, and YOLOv8, followed by motion prediction that estimates future movements based on historical trajectory. This technology is vital in continuous monitoring scenarios, such as traffic management for monitoring flow and detecting violations, sports analytics for identifying actions and tracking scores, and multi-camera surveillance systems requiring ReIdentification capabilities. Object trackers come in two varieties: Single Object Trackers, which track one object through a video sequence, and Multiple Object Trackers, which use algorithms like SORT, combining Kalman filters and the Hungarian algorithm, and DeepSORT, which incorporates complex association metrics based on motion and appearance, to track multiple objects simultaneously.
Project Highlights
Generative AI
Enhanced the stability and performance of Generative Adversarial Networks (GANs) and Transformer models, streamlining the synthesis of high-fidelity images and the integration of descriptive text. Deployed state-of-the-art Generative Diffusion Models to forge novel data samples from noise, utilizing advanced image segmentation techniques in tandem with GPT architectures to innovate a potent visualization function.
Stable Diffusion
Stable Diffusion is a generative architecture designed to create realistic images through a structured, denoising process. Unlike traditional diffusion models that execute a single denoising cycle, this framework adopts a hierarchical generation pipeline, where each stage progressively enhances spatial structure, texture detail, and overall visual fidelity. This Multi-Stage Latent Generation approach is ideal for creative AI applications.
Autonomous Robots and Self-Driving Vehicles
Focusing on optimizing the autonomous driving behaviors of self-driving vehicles to reduce their environmental impact. By employing efficient algorithms, these vehicles require less battery power, which is associated with decreased air pollution. This innovation also results in fewer cars needed per household, directly contributing to reduced emissions.
Facial Expression Analysis and Interpretation
Crafted advanced models for nuanced facial expression recognition and engineered sophisticated optimization algorithms, culminating in benchmark-setting performance accuracies.
Enhancing Models with Advanced Optimization and Tuning Techniques
Refined the optimization framework by introducing a soft-margin loss and a customized Adam optimizer. This approach enhances feature separability, stabilizes convergence, and achieves higher precision across complex learning tasks.
Fraud and Anomaly Detection Systems
Engineered robust anomaly detection frameworks for Fraud Risk Analytics, enhancing preventative measures and risk mitigation strategies.
Contact Me
"If you have any questions or require further information, please do not hesitate to contact me."