mk07811@st.habib.edu.pk
I am excited by the domain of numerical optimization, high-performance computing, and understanding the mathematics of machine learning. I intend to pursue Data Science and Robotics.<\/p>\n
mk07811@st.habib.edu.pk
musab.kas@gmail.com
https:\/\/www.linkedin.com\/in\/musab-kasbati\/
Portfolio
https:\/\/github.com\/musabkas
+923368277877<\/p>\n
Dhanani School of Science and Engineering
BS Computer Science<\/p>\n
2026
C++|
\nJax|
\nNextjs|
\nPython|
\nPytorch|
Drive for Results; Takes Initiative
Machine Learning; Robotics; Software Engineering;
Dean’s List 2023, 2024, 2025|
\nHigh Achievement Scholarship 2023, 2024, 2025|
\nPresident’s List 2023, 2025|
President,Brain.hack() – CSEC (Computer Science and Engineering Club)|Vice President,Brain.hack() – CSEC (Computer Science and Engineering Club)|Active Member,Math Club|
Data Science Intern,
\n Securiti.ai
\n (June \u2013 August 2025)
\n|<\/p>\n
Summer Tehqiq Research Program – Student Researcher,
\n Habib University – GSCP
\n (May \u2013 August 2025)
\n|<\/p>\n
Ml Intern,
\n Toyota IMC
\n (November 2024 \u2013 June 2025)
\n|<\/p>\n
Algorithms Researcher,
\n Cispa Helmholtz Center for Information Security
\n (June \u2013 July 2024)
\n|
Internship \u2013 Research Abroad internship at CISPA Helmholtz Center for Information Security in Germany|
\nCompetition \u2013 Meta Hacker Cup Quarter Finalist – 1st in Pakistan and 128th in the World|
\nAward \u2013 ICPC International Collegiate Programming Contest Regional Gold Medalist|
TurboDiff: A 2D Differentiable Fluid Simulator for Wind Turbine Airfoil Optimization
To maximize wind turbine efficiency, traditional design relies on computationally expensive solvers or low-fidelity tools lacking gradient capabilities. We developed TurboDiff, a lightweight, 2D differentiable CFD (Computational Fluid Dynamics) simulator specifically for Aerodynamic Shape Optimization (ASO).<\/p>\n
By leveraging Automatic Differentiation (AD) and Numerical Optimization, TurboDiff solves incompressible Navier-Stokes equations using Cartesian grids and Signed Distance Functions. This high-performance approach bypasses complex adjoint derivations, instead utilizing automatic gradients to iteratively refine airfoil geometries parameterized by Class-Shape Transformation.<\/p>\n
The research produced three high-performance airfoil designs tailored for varying power scales. Ultimately, TurboDiff provides an open-source framework bridging differentiable physics and scalable engineering, accelerating high-efficiency wind energy deployment in unique climatic regions like Pakistan\u2019s coastal corridors.<\/p>\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
mk07811@st.habib.edu.pkI am excited by the domain of numerical optimization, high-performance computing, and understanding the mathematics of machine learning. I intend to pursue Data Science and Robotics. mk07811@st.habib.edu.pkmusab.kas@gmail.comhttps:\/\/www.linkedin.com\/in\/musab-kasbati\/Portfoliohttps:\/\/github.com\/musabkas+923368277877 Dhanani School of Science and EngineeringBS Computer Science 2026C++| Jax| Nextjs| Python| Pytorch|Drive for Results; Takes InitiativeMachine Learning; Robotics; Software Engineering;Dean’s List 2023, 2024, 2025| High Achievement Scholarship […]<\/p>\n","protected":false},"author":9,"featured_media":8576,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"single-graduate-01.php","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"program":[29],"school":[42],"class":[44],"minor":[],"competencies":[61,59],"class_list":["post-8143","graduate","type-graduate","status-publish","format-standard","has-post-thumbnail","hentry","program-bscs","school-dhanani-school-of-science-and-engineering","class-class-of-2026","competencies-drive-for-results","competencies-takes-initiative"],"acf":{"scholarship":"","aspiration_statement":"I am excited by the domain of numerical optimization, high-performance computing, and understanding the mathematics of machine learning. I intend to pursue Data Science and Robotics.","video_profile_link":"","habib_email":"mk07811@st.habib.edu.pk","email":"musab.kas@gmail.com","linkedin_link":"https:\/\/www.linkedin.com\/in\/musab-kasbati\/","portfolio_Title":"Portfolio","portfolio_link":"https:\/\/github.com\/musabkas","personal_cell_1":"+923368277877","personal_cell_2":"","core_skills":[{"skill":"C++"},{"skill":"Jax"},{"skill":"Nextjs"},{"skill":"Python"},{"skill":"Pytorch"}],"academic_awards":[{"academic_record":"Dean's List 2023, 2024, 2025"},{"academic_record":"High Achievement Scholarship 2023, 2024, 2025"},{"academic_record":"President's List 2023, 2025"}],"leadership-experiences":[{"leadership":"President, Brain.hack() - CSEC (Computer Science and Engineering Club)"},{"leadership":"Vice President, Brain.hack() - CSEC (Computer Science and Engineering Club)"},{"leadership":"Active Member, Math Club"}],"work-experience":[{"experience":"Data Science Intern, Securiti.ai (June \u2013 August 2025)"},{"experience":"Summer Tehqiq Research Program - Student Researcher, Habib University - GSCP (May \u2013 August 2025)"},{"experience":"Ml Intern, Toyota IMC (November 2024 \u2013 June 2025)"},{"experience":"Algorithms Researcher, Cispa Helmholtz Center for Information Security (June \u2013 July 2024)"}],"publications-projects":[{"publications_title":"","publications":"Internship \u2013 Research Abroad internship at CISPA Helmholtz Center for Information Security in Germany"},{"publications_title":"","publications":"Competition \u2013 Meta Hacker Cup Quarter Finalist - 1st in Pakistan and 128th in the World"},{"publications_title":"","publications":"Award \u2013 ICPC International Collegiate Programming Contest Regional Gold Medalist"}],"project_title":"TurboDiff: A 2D Differentiable Fluid Simulator for Wind Turbine Airfoil Optimization","fyp_desc":"To maximize wind turbine efficiency, traditional design relies on computationally expensive solvers or low-fidelity tools lacking gradient capabilities. We developed TurboDiff, a lightweight, 2D differentiable CFD (Computational Fluid Dynamics) simulator specifically for Aerodynamic Shape Optimization (ASO).\r\n\r\nBy leveraging Automatic Differentiation (AD) and Numerical Optimization, TurboDiff solves incompressible Navier-Stokes equations using Cartesian grids and Signed Distance Functions. This high-performance approach bypasses complex adjoint derivations, instead utilizing automatic gradients to iteratively refine airfoil geometries parameterized by Class-Shape Transformation.\r\n\r\nThe research produced three high-performance airfoil designs tailored for varying power scales. Ultimately, TurboDiff provides an open-source framework bridging differentiable physics and scalable engineering, accelerating high-efficiency wind energy deployment in unique climatic regions like Pakistan\u2019s coastal corridors.","project_pictures":[{"project_pic":8794}],"project-video":""},"yoast_head":"\n