The Vitae Three Minute Thesis (3MT) competition challenges Doctoral candidates to present a compelling spoken presentation (using one single slide) on their research topic and its significance, in a language appropriate to a non-specialist audience, in just 3 minutes.
Developed by The University of Queensland, 3MT cultivates students’ academic, presentation, and research communication skills. The competition supports their capacity to effectively explain their research in three minutes.
3MT 2024 Finalists
We are delighted to announce the UWS 3MT 2024 Finalists below:
Alexander Dow
3MT Title: Work Smarter, Not Harder: Bringing Autonomous Drones To Urban Environments With LiDAR
Biography: Alexander's research focuses on the use of LiDAR sensors to enable autonomous drone flight. Ground-to-ground autonomous driving object detection techniques were modified and applied to an aerial drone-to-drone context. Real-time sense and detect on onboard systems has been made achievable through modifications made to sparse convolutions, and the work has been applied practically to point cloud data cleaning.
Amina Manal Zidi
3MT Title: Home and Away: The Lived Experiences of UK-based Algerian Doctoral Graduates on Returning Home to Algeria.
Biography: Amina Manal Zidi is currently pursuing her PhD at The University of The West of Scotland. She is curious about gaining insights into the experiences of international students who have completed their doctoral degrees and how they navigate their return to their home country (Algeria) after studying in the UK.
Annabel Simpson
3MT Title: Fighting Fit - Supporting the Development of a Health-Associated Oral Microbiome
Biography: Most dental research focuses on harmful bacteria which cause conditions like tooth decay and gum disease. However, some bacterial species carry out reactions which benefit you and support oral health. My research focuses on how the growth of these ‘good’ bacteria can be encouraged by improving oral hygiene and increasing physical activity levels.
Cezar Anicai
3MT Title: IoT and Machine Learning Enabled Estimation of Health Indicators from Ambient Data
Biography: Cezar Anicai is currently pursuing a PhD degree in Computing at the University of the West of Scotland. He holds a MSc degree in Advanced Computing and a BEng degree in Automatic Control and Computer Engineering. His research interests include machine learning, deep learning, bio-inspired and evolutionary algorithms, internet of things and distributed learning.
Chantal van Drimmelen
3MT Title: Do not throw your phone into the water – Impact of rare earth elements on waterflea behaviour
Biography: I want to contribute to the betterment of this world through my research within the field of ecotoxicology. My PhD focuses on a metal group called the rare earth elements and their potential toxic effects on our aquatic ecosystems. In my research I apply standardized and out-of-the box experimental approaches. There is still so much to discover!
Carol Becckwith
3MT Title: How Songs Help
Biography: Carol Beckwith is a PhD student in the School of Health & Life Sciences. Her PhD research looks at how someone who cares for a person with dementia makes sense of what it means to be a carer by writing songs that articulate that experience.
Ellis Larcombe
3MT Title: Addressing Microbiological Problems Within the Ornamental Fish Trade.
Biography: My research is on refining the use of “over the counter” fish medicines in treating pet fishes. I am investigating fish behaviour, physiology and the effects these products have on bacterial communities to ultimately improve standards of welfare for the most numerous pets in the UK.
Johanna Imiela
3MT Title: Navigating Human Rights in Government-Supported Business: A Focus on State Export Credit Agencies
Biography: Human rights impacts are pressing issues within international trade and supply chains. The UN Guiding Principles on Business and Human Rights offer guidance for states and business to address these concerns, though practical implementation remains challenging and is largely unexplored in economics. My project aims to pinpoint strategies for safeguarding human rights in the context of state Export Credit Agencies.
Julia Donnelly
3MT Title: Breaking menstrual taboos: one kick at a time
Biography: Julia’s research focuses on understanding the landscape of menstrual health, literacy, and support within elite and adolescent soccer. Through identifying a perception from players of the menstrual cycle negatively impacting performance, this research has targeted support structures within soccer to promote normalizing the conversation surrounding the menstrual cycle and ensure players health and wellbeing is prioritized throughout their progression through sport.
Khrisnamurti
3MT Title: Behind the Tales: Unveiling the Craft of Tourist Guides
Biography: Khrisnamurti is a PhD candidate at UWS. His work explores the intricate communication strategies used by heritage interpreters and their impact on shaping visitors' attitudes and behaviours at cultural heritage sites. This research also investigates how heritage interpreters acquire and refine their knowledge to craft compelling and interpretation narratives.
Philip Wölki
3MT Title: What information is required to develop an Employer Value Proposition?
Biography: Philip Wölki is a student at UWS in collaboration with HAW Hamburg, in the School of Business & Creative Industries
Thorben Ortmann
3MT Title: Virtual Worlds, Real Emotions: Recognizing Facial Expressions of Virtual Reality Users
Biography: Thorben Ortmann holds a master's degree in computer science. He works as a Research Associate at the Hamburg University of Applied Science. His research interests lie in the interdisciplinary field of affective computing. As part of his doctoral research, he investigates deep learning techniques to recognize human emotion in virtual reality environments.
Yingo Zhu
3MT Title: Integrating AI and IoT for Advanced Indoor Air Quality Monitoring
Biography: My research employs IoT sensors to monitor ambient environmental conditions, analyzing correlations between occupants and air parameters. Utilizing machine learning, it predicts future trends and occupancy, aiming to enhance occupants' health and well-being.