This month, we've got together three PGSA Conference Grant recipients, who we helped to get to international academic conferences earlier this year. They'll be presenting the work that they took to the international stage, so this a great opportunity to see some high-calibre from a range of disciplines. Plus, this being a PGSA event, there will inevitably be a pizza lunch to enjoy after the presentations.

Speaking at this Postgrad Talks will be:
 

Will Stanford Abbiss (Media Studies)

'Anything Is Possible Now': Sound, Image and Text in Dancing on the Edge

My paper will assess the co-ordination and contradictions between the jazz music and period aesthetics of Dancing on the Edge (BBC Two, 2013). The creative freedom afforded by the status of writer-director Stephen Poliakoff, in conjunction with the public service ethos of BBC Two, will connect theories of televisual authorship to the serial’s historiographical point of view, presenting it as an exception to contemporary televisual trends.


Malik Hasanain (Health)

Challenges of prostate cancer: a narrative study among Jordanian Muslim men

Prostate cancer is the most common cancer among men around the world. Men with prostate cancer deal with a broad range of challenges from pre-diagnostic symptoms to the investigative tests and the treatment journey. Muslim men face a particular set of challenges because prostate cancer and its treatments impact on their ability to perform roles in relation to being Muslim. In my presentation, I will discuss some of the challenges facing Jordanian Muslim men with prostate cancer with a specific focus on their experience and adaptation to prostate cancer.

 

Ying Bi (Computer Science)

Genetic Programming for Image Classification

Image classification is an important task in computer vision and machine learning. However, image classification is a challenging task due to high variations of images. Many algorithms have been developed for image classification. But most of them have fixed model complexity and have poor interpretability. Genetic programming is an evolutionary computation algorithm and can automatically evolve solutions for solving a problem. This study proposes a new genetic programming algorithm for image classification. The proposed method not only achieves better classification performance but also provides high interpretability of the evolved solutions.