Dr. Raazia Sosan

Designation

Chairperson

Department

Specialization

Computer Graphics and Visualization, High Performance Computing

Qualifications

Research Fellowship INTI University, Malaysia, 2025-Inprocess Ph.D (Computer Science), DHA SUFFA University, Karachi

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    Teaching Experience

    Salim Habib University

    Chairperson
    March 2025 – Present

    Assistant Professor
    December 2024 – Present

    DHA Suffa University

    Assistant Professor
    March 2024 – December 2024

    Senior Lecturer
    July 2016 – March 2024

    Hamdard University

    Assistant Professor
    Dec 2015 – August 2016

    Lecturer
    December 2014 – Dec 2015

    Lab Instructor
    October 2012 – December 2014

    Courses Taught

    1. Parallel & Distributed Computing
    2. Object Oriented Programming
    3. High Performance Computing using CUDA
    4. Compiler Construction
    5. Data Structures
    6. Algorithms
    7. Seminar I and Seminar II.
    8. Advanced Operating System
    9. Advanced Theory of Computing
    10. Advanced Digital Image Processing

    Research Interest

    1. Computer Graphics and Visualization
    2. High Performance Computing

    Selected Publications

    19/12/2022

    Perceptual Analysis of Tone Mapping Operators:

    Tone mapping is a crucial step in rendering, as it maps the High Dynamic Range (HDR) images to a Lower Dynamic Range (LDR) for display on standard devices. In this research, we address the challenge of selecting the most appropriate Tone Mapping Operator (TMO) based on perceptual differences, which are essential for achieving visually aesthetic and physically plausible images. In this article, we evaluate several existing TMOs using two novel perceptual parameters: Structural Similarity Index (SSIM) and Color Difference Formula (CIEDE2000).

    28/06/2022

    Perceptual analysis of distance sampling and transmittance estimation techniques in biomedical volume visualization:

    In volumetric path tracer, distance sampling and transmittance estimation techniques play a vital role in producing high-quality final rendered images. Previously, these techniques were implemented for production volume rendering, and were analyzed for faster convergence. In this article, we have augmented additional transmittance estimators including ratio tracking, residual ratio tracking and unbiased ray marcher in a GPU-based volumetric path tracer (Exposure Render) for biomedical datasets. We have also analyzed distance sampling methods and transmittance estimators perceptually using CIEDE2000 and Structural Similarity Index (SSIM). It was found that ratio and residual ratio tracking estimators performed close to each other and were better than unbiased ray marching perceptually. In addition, ray marching was observed to be better than delta tracking for distance sampling.

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