Delia M
- Research Program Mentor
MS candidate at University of Edinburgh
Expertise
Artificial intelligence(ML/NLP/DL), Computer vision & graphics
Bio
I am deeply passionate about human-computer interaction and technology with tangible real-world applications. Currently pursuing a master's degree in AI, I have delved into various facets such as vision, graphics, security, and NLP. My diverse experience spans working with start-ups and major tech companies as well as engaging in research ranging from cybersecurity to NLP recommender systems and wearables. I am also currently researching LLMs and ViTs. Presently, my focus lies in wearables and health trend predictions through AI, with a keen interest in innovative approaches to address addiction and diseases. My love for wearables has even begun to coincide with my love for running, where I now relive and analyse my run afterwards through the post-run statistics.Project ideas
Create cartoons from real images
Create a program that will take a real image as an input, and use Computer Vision Tools to transform the image into a cartoon. You can have fun with removing/changing the colour hue, increasing the grain, and finding parameters that create your ideal cartoon image. The outcome in creating these cartoons could be used to create a cartoon comic for real life stories or even by 'cartoonifying' real-life videos. This will give practical experience in working with OpenCV tools that are used in real-world (both in university research and corporations that are incorporating computer vision technology). You'll learn about the process of what tools to use and in what order (how they work together). Specifically looking into techniques regarding smoothing, thresholding (how edges are handled), and filters that can be applied to the image for different effects.
Predict emotions from facial images
Create a classification and prediction algorithm that will be trained on a group of faces to recognise emotion. After training, when the model is shown a new face it should be able to predict what a new face is feeling. This can be applied to UI/UX testing to get a better understanding as to how a user feels about a particular product or feature. Given that questionnaires are not always reliable, this could help supplement the findings from these exams to provide more comprehensive and honest results. This will give practical experience in working with creating a classification prediction model and corresponding modern technologies for ML. This project will improve ML understanding and provide a good background for future work done to apply AI practically.