My PhD focuses on understanding and improving the trustworthiness of machine learning models in high-stakes applications like healthcare. Modern AI systems can achieve impressive accuracy, but they can still fail in unexpected ways, leak sensitive information, or behave unfairly. During my PhD, I developed mathematical methods to analyze these models and understand when they are truly reliable. Ultimately, the goal of my work is to help make AI safer for real-world decision-making.
What made you want to pursue a PhD?
I’ve always enjoyed learning, especially mathematics, and over time I realized that I was also drawn to research and problem solving. What appealed to me most was the opportunity to explore difficult questions in depth and develop new ideas and solutions. A PhD felt like the natural path to explore those kinds of problems more deeply.
What is the most fascinating or interesting with your thesis subject?
What I find most interesting is that high accuracy alone tells us very little about whether an AI model is actually reliable. A model can perform extremely well and still fail in unexpected ways, which raises many challenging questions about trustworthiness.