Ms. Sumera Aslam

Lecturer (CS)

Introduction

Ms. Sumera Aslam, a dedicated Lecturer in the Department of Computer Science at the National University of Technology, Islamabad. With a rich background in Artificial Intelligence (AI), she brings a wealth of experience from her work on cutting-edge research projects that explore the transformative applications of AI across diverse domains. Her primary research interests include AI, machine learning, and deep learning, with a focus on solving complex, real-world challenges. As an educator, she is deeply committed to fostering a profound understanding of computer science principles while equipping her students with the practical skills necessary to thrive in the dynamic tech landscape. Ms. Sumera Aslam’s passion for innovation and education inspires her students to explore new frontiers in technology and make meaningful contributions to the field.

Experience

Research Assistant, National University of Technology Islamabad (January 1, 2024 – November 28, 2024)

  •  
  • Worked on the NRPU project under HEC titled "Artificial Intelligence (AI) based Smart Precision Agriculture for Rural Areas of Pakistan."
  • Developed and implemented AI algorithms to enhance agricultural practices, focusing on challenges like weed and crop identification, disease detection, and crop recommendation.
  • Contributed to improving agricultural efficiency and addressing key issues in rural farming through AI-driven solutions.

Qualification

  • Academia:
    • MS in Computer Science, COMSATS University Islamabad, (2024)
    • BS in Information Technology, Islamia University Bahawalpur, (2020)
  • Certifications:
    • C# Programming Language, Aptech Bahawalpur (2018)
    • Python Programming Language, CAS Academy Bahawalpur (2020)

Research

Ms. Sumera Aslam’s research focuses on the intersection of medical imaging, natural imaging, and agriculture, with a strong emphasis on explainable AI. She is currently working on projects that apply AI techniques to medical imaging, such as developing models for automated diagnosis and interpretation of brain tumor. Additionally, she has contributed to agricultural AI project where she developed AI algorithms for weed and crop identification. Her work aims to make AI models more interpretable and actionable across these critical domains.

    • Ongoing Research Projects:
      • Precision Agriculture using AI to enhance agricultural practices.
      • Explainable AI in Medical Imaging to interpret deep learning models' decisions.
    • Publications:
      •  “Segmentation of Agricultural Fields in Aerial Imagery Using Enhanced Deep Learning Models” IEEE Conference (2024).
      • “Weed & Crop Segmentation in Aerial Imagery: A Weighted Hybrid Loss Function Approach."(Under Review)