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About

Nishat Vasker is a rising researcher at the intersection of artificial intelligence, robotics, and food safety, whose work is redefining automated quality control in the agricultural and medical sectors. Her primary research areas include deep learning, computer vision, and robotic automation, with a strong emphasis on explainable AI (XAI) for real-world applications. Vasker’s most impactful contributions address critical challenges in food processing and public health. She developed an XAI-enhanced deep learning framework integrated with a robotic arm for real-time sorting of broiler chicken meat based on freshness, achieving 16 citations for its potential to reduce contamination risks. Similarly, her work on fish freshness detection using Mask R-CNN and robotic systems—cited 13 times—offers a scalable solution for Bangladesh’s fish export industry, ensuring compliance with international standards. Beyond food safety, Vasker has applied YOLOv5 for real-time ovary tumor detection in ultrasound imaging, showcasing her versatility in medical AI. With a growing citation record and a focus on deploying interpretable, automated systems, Vasker is establishing herself as a key innovator in bridging AI transparency with practical, life-saving applications.