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About Us
About Us
Welcome to the Intelligent Computing Lab at Yuan Ze University. In this dynamic and innovative environment, we embark on a journey at the intersection of cutting-edge technologies, exploring the realms of autonomous vehicles, deep learning, and anomaly detection. This lab serves as a hub for exploration, experimentation, and innovation, where we delve into the transformative potential of networking technologies in shaping the future of transportation and safety.
For inquiries regarding graduate studies, postdoctoral positions, internships, or related activities, please email Assistant Professor Qazi Mazhar Ul Haq (see Contact Link)
Mission
Mission
The mission of the Intelligent Computing Lab is to conduct fundamental and applied research in computer vision, incremental and continual learning, anomaly detection, and autonomous systems. We focus on developing robust algorithms and frameworks that address real-world challenges in areas such as intelligent transportation, medical image analysis, and multi-modal perception.
Vision
Vision
The lab envisions becoming a recognized center for research in intelligent computing, advancing the scientific understanding of perception and decision-making in complex environments. By integrating theoretical models with practical implementations, we aim to contribute to reliable and scalable solutions for next-generation autonomous and intelligent systems.
Research Areas
Research Areas
Autonomous and UAVs
Autonomous and UAVs
Autonomous vehicles represent a transformative technology that aims to enable vehicles to navigate and make decisions without human intervention. This involves a fusion of technologies, including computer vision, machine learning, sensor integration, and real-time decision-making, to create safe and efficient self-driving systems.
Autonomous vehicles represent a transformative technology that aims to enable vehicles to navigate and make decisions without human intervention. This involves a fusion of technologies, including computer vision, machine learning, sensor integration, and real-time decision-making, to create safe and efficient self-driving systems.
Domain Adaptation
Domain Adaptation
Domain adaptation is a subfield of transfer learning that addresses distributional shifts between source and target domains by learning domain-invariant representations, enabling models trained on labeled source data to generalize effectively to the target domain.
Domain adaptation is a subfield of transfer learning that addresses distributional shifts between source and target domains by learning domain-invariant representations, enabling models trained on labeled source data to generalize effectively to the target domain.
Incremental Learning
Incremental Learning
Incremental learning involves developing algorithms and techniques that allow systems to adapt and learn from new data as it arrives over time. This is particularly useful when dealing with evolving environments, where models need to continuously improve their performance and adapt to changing conditions without forgetting the previously acquired knowledge.
Incremental learning involves developing algorithms and techniques that allow systems to adapt and learn from new data as it arrives over time. This is particularly useful when dealing with evolving environments, where models need to continuously improve their performance and adapt to changing conditions without forgetting the previously acquired knowledge.
Object Detection
Object Detection
Object detection is the process of identifying and locating specific objects within images or videos. In the case of 2D object detection, this involves recognizing objects in 2D images, while 3D object detection extends this to capturing spatial information for objects in a 3D scene. This technology has applications in areas such as autonomous driving, robotics, and surveillance.
Object detection is the process of identifying and locating specific objects within images or videos. In the case of 2D object detection, this involves recognizing objects in 2D images, while 3D object detection extends this to capturing spatial information for objects in a 3D scene. This technology has applications in areas such as autonomous driving, robotics, and surveillance.
Medical Image Processing
Medical Image Processing
Medical image processing focuses on the analysis, enhancement, and interpretation of images from medical imaging modalities such as X-rays, MRIs, and CT scans. This field is crucial for aiding medical professionals in diagnosing and treating various medical conditions accurately and non-invasively.
Medical image processing focuses on the analysis, enhancement, and interpretation of images from medical imaging modalities such as X-rays, MRIs, and CT scans. This field is crucial for aiding medical professionals in diagnosing and treating various medical conditions accurately and non-invasively.
Anomaly Detection System
Anomaly Detection System
Anomaly detection involves identifying patterns or instances that deviate significantly from the norm in a given dataset. This technique is valuable in various contexts, such as detecting fraud in financial transactions, identifying faults in industrial processes, and even spotting anomalies in medical images to diagnose rare conditions.
Anomaly detection involves identifying patterns or instances that deviate significantly from the norm in a given dataset. This technique is valuable in various contexts, such as detecting fraud in financial transactions, identifying faults in industrial processes, and even spotting anomalies in medical images to diagnose rare conditions.
Weekly Presentations
Weekly Presentations
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