Announcing the 2025 GRADflix finalists
Announcing the finalists of the 2025 GRADflix competition. Finalists will have their videos showcased at the 2025 GRADflix showcase at Fed Hall on February 4.
Announcing the finalists of the 2025 GRADflix competition. Finalists will have their videos showcased at the 2025 GRADflix showcase at Fed Hall on February 4.
2024 GRADflix finalist, Liam Bursey, shares how architecture is an act of storytelling, why he chose to participate in GRADflix, and ways of demonstrating how research makes a difference.
Adebusola's research investigates the use of technology and alert systems to reduce the risk of missing persons with dementia. It is Adebusola's goal to raise awareness of the risks associated with people living with dementia.
In her interview, she discusses the importance of knowledge communication, her creative process while creating her video, and the importance of researching dementia.
Graduate Studies and Postdoctoral Affairs is excited to announce the 2024 GRADflix showcase winners!
Meet the 2024 GRADflix finalists. The judges have selected the top 15 graduate students who will have their videos featured at the GRADflix showcase.
Anna Good is a Master’s student in Department of History in the Faculty of Arts. Anna’s research examines documents of four soldiers who attempted suicide during the world wars.
Karen Hoc is a PhD candidate in the School of Public Health Sciences. Her research focuses on promoting healthier beverage consumption.
Marina Ansanelli is a PhD candidate in Physics and Astronomy in the Faculty of Science. Her research is centered around understanding more about the deepest underpinnings of nature, by means of investigating the foundations of Quantum Mechanics.
The first place winner and people's choice is Andrew Stella, whose research focuses on synthesizing polymers that can be used to detect the presence of toxic gases.
Yuzhe You, from the Faculty of Mathematics, centred her GRADflix submission on nteractive visualization tools that enhance AI interpretability and help improve the “adversarial robustness” of machine learning models.