Yasmeena Akhter is originally from India and holds a PhD focused on machine learning and artificial intelligence in medicine. Her doctoral research addressed highly relevant problems in medical imaging, particularly the detection of respiratory diseases such as silicosis. Her work has been presented at leading artificial intelligence conferences, including MICCAI, AAAI and IJCAI. Her current research focuses on developing machine learning models with random effects to better understand digital health trajectories across multiple time scales. Her goal is to create individualized, data-driven models that capture both population-level patterns and individual-specific variability, contributing to a new generation of personalized predictive models in medicine. Yasmeena is building a strong academic career through ambitious and impactful scientific contributions. She is currently developing a new generation of foundation models with random effects, an innovative research direction with the potential to transform longitudinal prediction and personalized decision-making in medicine.