I am a Machine Learning Scientist with a multidisciplinary background spanning Medical Engineering, Computational and Data Science, and Clinical Bioinformatics. My work sits at the intersection of clinical science and artificial intelligence, driven by a passion for developing efficient, interpretable models that can actively support clinical decision-making.

Over the years, I have applied ML/AI across a variety of challenging domains, including:

• Medical Image Reconstruction
• Timeseries Analysis
• Instance Segmentation
• Computational Biology

• Symbolic Regression: My current primary research focuses on exhaustive Symbolic Regression, approaching it from both an algorithmic and software engineering perspective. I am always eager to exchange ideas or collaborate in this space. If you are working on similar problems or have an exciting opportunity, feel free to reach out!
• Science Communication: I received training in science communication during my Ph.D. In recent discussions about AI, I've realized there is a strong need to better communicate the distinction between language usage and reasoning to non-technical audiences. Giving people a clearer understanding of the true capabilities of Large Language Models (LLMs) is incredibly important right now. If you have ideas or resources on this topic, I'd love to connect!
• Lifelong Learning: Outside my core research, I'm always eager to dive into new topics like game theory and coding theory. I find it particularly fun and helpful to discover unexpected connections between seemingly completely different fields.