Cognitive Vision and Robotics Lab (CVR Lab)
Research Field
Dr. Naeem Ul Islam is a distinguished scholar in the field of computer engineering, with a rich academic and research background that spans multiple areas. He earned his doctoral degree in Computer Engineering from the Intelligent Systems Research Institute at Sungkyunkwan University, South Korea, in 2019. His journey into the world of cutting-edge technology and artificial intelligence began at this prestigious institution.
Following the successful completion of his Ph.D., Dr. Islam started a postdoctoral fellowship at Jeonbuk National University, South Korea, from 2019 to 2021. During this time, his research focus centered on the development of AI-based autonomous agricultural robots with a particular emphasis on terrain analysis for autonomous navigation. He made significant contributions to the project during his tenure.
He also worked as a visiting faculty at Gachon University, South Korea. In 2021, he took up the role of Assistant Professor at the esteemed National University of Sciences and Technology (NUST), Pakistan. At NUST, he became actively engaged in both teaching and diverse research endeavors. His research portfolio included areas such as path planning, AI-based smart infrastructure management, fault detection in photovoltaic cells using advanced AI approaches, medical imaging, and obstacle detection.
Currently, he is serving as an Assistant Professor at Yuan Ze University in Taiwan, where his focus is on computer vision and deploying AI techniques in different robotics applications, in addition to teaching computer science courses.
AI-Vision Robotics Innovations Lab is dedicated to exploring and advancing various research areas at the intersection of computer engineering and artificial intelligence. Here, we take pride in our commitment to pushing the boundaries of knowledge and creating practical applications that can make a meaningful impact on society. Our research spans a wide spectrum of domains, including
Path Planning: Navigating through complex environments is a fundamental challenge in robotics and autonomous systems. Our lab focuses on developing novel path-planning algorithms that enable efficient and safe navigation for autonomous vehicles and robotic platforms.
AI-Based Smart Infrastructure Management: The integration of artificial intelligence in infrastructure management is crucial for optimizing resource utilization, enhancing operational efficiency, and ensuring the sustainability of urban environments. Our research explores AI-driven solutions for the intelligent management of infrastructure systems.
Fault Detection in Photovoltaic Cells Using Advanced AI Approaches: As the demand for renewable energy sources grows, it becomes imperative to ensure the reliability and performance of photovoltaic systems. We employ advanced AI techniques to detect and diagnose faults in photovoltaic cells, improving the efficiency and lifespan of solar power installations.
Medical Imaging: Our lab is actively involved in developing AI-enhanced tools and techniques for medical imaging. These innovations are aimed at improving diagnostic accuracy, streamlining healthcare processes, and ultimately saving lives through early detection and diagnosis.
Terrain Analysis & Obstacle Detection: Terrain analysis and obstacle detection and avoidance are crucial components of autonomous navigation systems. We investigate state-of-the-art methods for analyzing the terrain, detecting and responding to obstacles in real-time, enhancing the safety and reliability of autonomous vehicles.
Deepfake detection and prevention.
Bioinformatics: Our lab is also working on the analysis and interpretation of biological data, aiding in critical research related to genomics, proteomics, and beyond.
In our laboratory, we foster a collaborative and multidisciplinary research environment, bringing together experts from diverse backgrounds to tackle complex challenges. We welcome brilliant students from all over the world to join our lab.
Path Planning
AI-Based Smart Infrastructure Management
Fault Detection in Photovoltaic Cells Using Advanced AI Approaches
Medical Imaging
Deepfake detection and prevention
Terrain Analysis & Obstacle Detection
Bioinformatics
2D to 3D transformation using deep learning
Interpretation of deep neural networks
Ph.D. fellowship grant
Research travel grants
Awarded the Outstanding Teaching Award (2023-2024)
Awarded Innovative Teaching Award (2023-2024)
Awarded the Innovative Teaching Award for the second time (2024-2025)
Ph.D. in Computer Engineering
Job Description
Implement RL controllers for navigation and manipulation tasks.
Integrate trained policies with robotic simulation platforms.
Evaluate system performance under varying environmental conditions.
Assist in real-to-simulation or simulation-to-real experiments.
Contribute to research papers.
Preferred Intern Educational Level
Applicants must be senior-level Bachelor’s students (final year) or currently enrolled in a Master’s or Ph.D. program in Computer Science, Electrical Engineering, Biomedical Engineering, Artificial Intelligence, Robotics, or a closely related field.
Skill sets or Qualities
Experience with robotics simulation tools.
Programming proficiency in Python.
Strong background in machine learning and optimization.
Experience with PyTorch and RL frameworks.
Job Description
Design and develop transformer-based architectures for medical image segmentation and lesion detection.
Explore vision transformers and hybrid CNN–Transformer models for high-resolution medical images.
Develop models for automated medical report generation by integrating visual and textual representations.
Investigate multimodal learning combining medical images and clinical text.
Evaluate models using clinically relevant metrics and conduct ablation studies.
Contribute to peer-reviewed journal and conference publications.
Preferred Intern Educational Level
Senior Bachelor’s (final-year), Master’s, or Ph.D. candidate in Biomedical Engineering, Computer Science, AI, or a related field.
Strong background in deep learning and medical image analysis.
Experience with transformers, attention mechanisms, or multimodal models.
Proficiency in Python and deep learning frameworks (e.g., PyTorch).
Skill sets or Qualities
Experience with Vision Transformers (ViT), Swin Transformer, or medical transformer models.
Familiarity with medical report generation, vision–language models, or large language models.
Prior research experience or publications in medical imaging or medical AI.