Computer Science Master’s student at Trinity College Dublin. I work across engineering, research, and community building. Experience includes multiple internships at Microsoft and Intel, scaling HackEurope into Europe’s largest student hackathon, and governing large student organisations. Focused on building technology that is understandable, useful, and accessible.

Healthcare & Biotechnology Government, Legal & Defense Finance Education & Future of Work

- MSc Computer Science at Trinity College Dublin (Research Project: Regulatory Compliance of LLM Risk Assessments) - Founding team at HackEurope, Europe's largest student hackathon - https://www.hackeurope.com/ - Building Give(a)Go, Ireland's largest builder community - https://www.giveago.co/ - Competing in hackathons, tech community events and travelling! ​

Hi, I’m Martha, a Master’s student in Computer Science at Trinity College Dublin. I came into computer science with no prior experience and ended up loving the creativity and problem-solving it demands. I have previously interned on the Search Engine team at Workday and I’m excited about how rapidly the field is evolving and the opportunities to build so quickly right now. Outside of tech, I’m passionate about women’s health and sustainable/innovative food systems.

I’m interested in ethical AI (fairness, bias, faithfulness, explainability) and sustainable AI (efficient training, model compression, low-resource deployment).

Right now, I’m working on my dissertation, where I’m exploring how green AI model-training techniques influence the explainability of machine learning models. I’m interested in whether more energy-efficient training methods change how transparent or interpretable a model can be. I also recently won a hackathon with two of my best friends, where we built an AI-powered productivity planner designed around the menstrual cycle.

I’m Leah Weldon, a Master’s student in Computer Science at Trinity College Dublin with a focus on AI, software engineering, and responsible technology. I came into CS with a strong interest in problem-solving and ended up loving how quickly ideas can become real systems. I previously worked as a software engineering intern at IBM, where I built AI-driven automation tools, agentic RAG workflows, and evaluation pipelines. I enjoy rapid prototyping, collaborative building, and creating tools that feel genuinely useful. Outside of tech, I care about women in STEM, education, and projects that make technology more equitable and accessible.

I’m interested in responsible and adaptive AI systems, especially fairness, robustness, and how models behave under distribution shifts. My dissertation focuses on evaluating risk-detection models, and I’m keen to connect with people exploring safe AI deployment, agentic workflows, creative reasoning, and building tools that make intelligent systems more reliable and aligned with real-world needs.

I’m working on my dissertation, developing methods to evaluate fairness and robustness in risk-detection models using adversarial prompts and distribution-shift testing. I’m also experimenting with lightweight agentic prototypes and small reasoning workflows, exploring how to make AI systems more dependable, explainable, and practically useful in real applications.