Delia M
- Research Program Mentor
MS at University of Edinburgh
Expertise
Artificial intelligence(ML/NLP/DL), Computer vision & graphics
Bio
I'm driven by research that connects deep technical work with real-world applications. My journey has taken me through roles in cybersecurity, NLP, recommendation systems, health tech, and robotics—across both start-ups and large tech companies. I recently completed an MSc in collaboration with Amazon, focusing on adversarial attacks in neural architectures and multilingual models. Now, I work on the behavior system for a robotic pet—designing its own emotional responses while also enabling it to classify and respond to human emotions. This involves cognitive modeling, computer vision, reinforcement learning, and interpreting sensor data for emotional and health insights. Outside of work, I love animals and visual media. I often read about animal behavior and psychology to better understand why both people and animals act the way they do. As a hobby, I enjoy running and listening to music—it helps me clear my head and reset. :)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.