Motorcycle Girl To be Screened at Stanford University

Zenith Irfan

Pakistani cinema is in its birth stage right now where certain people are now also saying it as the revival area of the cinema. There are new movies being created with bigger budgets and improved cinematography and directions. This has somewhat led to an increase in the revenue that has been generated by Pakistani movies. One such movie that was recently released was Motorcycle Girl starring the beautiful Sohai Ali Abro. The movie was welcomed by the fans and critics for its amazing storyline and how it portrayed the struggles of every other women in the country. The movie broke a lot of barriers and it has broken yet another barrier this time.

The movie Motorcycle Girl will now be shown at the Stanford University for the Global Studies Film Festival of the university. The movie showcased the story of Zenith Irfan who then shared this amazing news with all the Pakistani fans. She said that Stanford University is showing her biopic in July and this news is huge for her. She said that she cannot believe that her biopic will be showcased at Stanford University as she has no words to explain her joy and happiness. And this is a big achievement for the entire country as well that a biopic will be on display at Stanford University.

The film will be screened on 31st July 2019 at the Global Studies Film Festival and is part of a total of 10 films that will be shared at the festival. The theme of this years’ film festival is Earth: Habitat for All. The movie showcased the story of Zenith Irfan as to how a young woman broke all the barriers of the Pakistani society to ride a motorcycle all across the northern areas of Pakistan.

YouTube video
YouTube video
YouTube video
Jennifer Mccarthy

Jennifer is Associate Professor (Ph.D) at Air University. Research Interests: Informatics; analysis of large-scale biological data sets (genomics, gene expression, proteolytic, networks); algorithms for integration of data from multiple data sources; visualization of biological data; machine learning methods in bio-informatics.

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