Dr. Aleksandrina Goeva is an innovative computational scientist advancing biomedical discovery through mathematical and computational approaches. She currently serves as Assistant Professor at The Donnelly Centre for Cellular and Biomolecular Research at the University of Toronto, a position she assumed in May 2024. With a lifelong passion for mathematics cultivated in Bulgaria, she earned her Bachelor of Science in Applied Mathematics from Sofia University before completing her PhD in Mathematics and Statistics at Boston University in 2017. Her intellectual trajectory shifted during graduate studies when she established a statistical consulting unit at Boston University, providing analytical support across diverse research domains and discovering her calling to apply mathematical rigor to biological challenges.
Dr. Goeva has made significant contributions to statistical methodology development for single-cell RNA sequencing and spatial transcriptomics data analysis, creating innovative computational frameworks that reveal disease mechanisms at unprecedented resolution. Her work on the HiDDEN machine learning method enables detection of disease-relevant populations in case-control studies, offering new pathways for precision medicine through sophisticated disease characterization. With over 5,300 citations according to Google Scholar, her research bridges statistical learning theory with practical applications in computational neuroscience and biomolecular research. Her expertise in developing statistically grounded methods provides researchers with powerful analytical tools to interpret complex biological data and uncover novel disease insights.
As a new faculty member at The Donnelly Centre, Dr. Goeva continues to integrate mathematical innovation with biomedical discovery, developing novel approaches that transform how scientists study complex diseases. She actively collaborates across disciplines, sharing her statistical and machine learning expertise to help fellow researchers re-envision problem-solving in cellular and biomolecular research. Her laboratory focuses on advancing computational methods that extract meaningful biological insights from high-dimensional genomic data, particularly through single-cell and spatial transcriptomics technologies. With her unique perspective at the intersection of mathematics and biology, Dr. Goeva is positioned to make transformative contributions to understanding disease mechanisms and developing new therapeutic approaches through computational innovation.
