Ada is a quantitative researcher with formal training in statistics and biostatistics, and hands-on experience developing ML, DL, and AI-based pipelines to address neuropsychiatric and brain-related research questions.
Her research interests center on designing and applying machine learning and deep learning models for neuropsychiatric problems, including techniques such as:
- Neural network architectures for temporal and imaging data
- Evidence-based statistical modeling
- Reproducible and scalable data workflows
She describes herself as a lifelong learner who finds joy in exploring new ideas, tools, and challenges, and is motivated by projects that connect data science with real human impact.