Amelie Dinh
About Amelie Dinh
Amelie Dinh is the Head of Contextual Research, known for her work on the intersection of design and machine learning, and her participation in prominent conferences like D&AD Festival and Mouvo Conference.
Amelie Dinh Title: Head of Contextual Research
Amelie Dinh holds the position of Head of Contextual Research. In her role, she delves into the nuanced intersections of design and machine learning, emphasizing the visual and narrative potentials within these fields. Her leadership in contextual research is marked by a focus on understanding and exploring computational abstractions and the role of generated machine patterns in visual discovery.
Amelie Dinh at D&AD Festival 2024
In 2024, Amelie Dinh participated in the 'Imaginations of AI' panel at the D&AD Festival in London. This event highlighted insights into the future possibilities and ethical considerations of AI, with Dinh contributing substantial knowledge from her research in design and generative machine learning.
Amelie Dinh at Mouvo Conference
Amelie Dinh was a speaker at the Mouvo Conference in Prague, where she addressed the role of metaphor, narrative, and visual language in AI. Her talk focused on how these elements integrate into the development and perception of AI technologies, further bridging the gap between technical and creative disciplines.
Machine Windows: Views from the Latent Space
Amelie Dinh co-authored the research project 'Machine Windows: Views from the Latent Space' with machine learning researcher Claartje Barkhof. This project explores the visual possibilities and complexities of generative machine learning, providing interactive ways to delve into the latent spaces of machine-generated patterns. Through 'Machine Windows,' Dinh investigates the intricate layers of machine learning, delivering new insights into computational abstractions.
Research Focus on Generative Machine Learning
Amelie Dinh’s research focuses on the intersection of design and machine learning, particularly in the context of generative machine learning. She aims to develop interactive methods to explore and visualize machine learning processes, investigating how generated patterns can reveal deeper insights into the computational mechanisms at work. Her work emphasizes the need for understanding the complexities and visual possibilities that generative machine learning offers.