Bostan Khan, Doctoral student

E-mail:
bostan.khan@mdu.se
Room:
U2-097
Phone:
0736620514
Phone (alt):
0729274571
Division:
Division of Intelligent Future Technologies
Research group:
Heterogeneous systems - hardware software co-design
Web:
Linkedin page


I am a dedicated researcher with a strong background in deep learning, computer vision, and remote sensing. I completed both my Bachelor's degree in Electrical Engineering and my Master's degree in Computer Science from the National University of Sciences and Technology (NUST) in Islamabad, Pakistan. Throughout my academic journey, I developed a passion for solving complex problems using advanced machine learning techniques, particularly in the areas of image processing and computer vision.

Currently, I am pursuing my PhD at Mälardalens University in Västerås, Sweden, where my research focuses on enhancing remote sensing imagery through super-resolution techniques. I am involved in the AutoFL project in which I am particularly interested in leveraging deep learning models within a federated learning setup, enabling multiple clients or satellites to jointly train these models while maintaining data privacy and robustness. Before embarking on my PhD, I led the AI team at the Machine Vision and Intelligent Systems (MachVIS) Lab at NUST, where I guided numerous projects in AI and deep learning. My experience also includes working as a Machine Learning Engineer at the National Engineering and Scientific Commission (NESCOM), where I developed and deployed deep learning models for image matching and object detection tasks. I am proficient in Python, C, C++, and SQL, and have extensive experience with leading frameworks such as PyTorch, Keras, and OpenCV, which has equipped me with the versatility and skills needed to excel in the fields of machine learning and computer vision.

My research interests lie at the intersection of Machine Learning and Computer Vision, with a particular focus on applying deep learning techniques to enhance remote sensing imagery. I have been working extensively on developing super-resolution models that significantly improve the quality and detail of satellite images. This work not only advances the field of remote sensing but also has practical implications for various applications, including environmental monitoring, urban planning, and disaster management.

Currently, I am involved in the AutoFL project exploring the challenges and opportunities of implementing super-resolution in a federated learning setup, where multiple clients or satellites collaborate to jointly train models while preserving data privacy. This cutting-edge approach leverages the strengths of distributed learning to enhance the performance of super-resolution systems in diverse and heterogeneous environments. My goal is to push the boundaries of what is possible with remote sensing imagery, contributing to more accurate and actionable insights from satellite data.