
Dr. Raazia Sosan
Designation
Chairperson
Department
- Faculty of Information Technology, Department of Computer Science
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
- Parallel & Distributed Computing
- Object Oriented Programming
- High Performance Computing using CUDA
- Compiler Construction
- Data Structures
- Algorithms
- Seminar I and Seminar II.
- Advanced Operating System
- Advanced Theory of Computing
- Advanced Digital Image Processing
Research Interest
- Computer Graphics and Visualization
- 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.